From 8ba4fa267754cfd56d902d9084f9b11636e2e871 Mon Sep 17 00:00:00 2001
From: alessandrozocca <alessandrozocca@users.noreply.github.com>
Date: Wed, 22 Nov 2023 13:50:06 +0000
Subject: [PATCH] deploy: abc2f8c3c21e2a427f56b96e61607b7deea93d6b

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HcmV?d00001

diff --git a/_sources/notebooks/02/02.00.md b/_sources/notebooks/02/02.00.md
index a17311d5..32e67ebb 100644
--- a/_sources/notebooks/02/02.00.md
+++ b/_sources/notebooks/02/02.00.md
@@ -18,14 +18,14 @@ Linear problems can (i) be maximization problems, (ii) involve equality constrai
 
 This chapter includes several with companion Pyomo implementation that explore various modeling and implementation aspects of LOs:
 
-* A first LO example, modelling [the microchip production problem of company BIM](01-bim.ipynb)
+* [Microchip production problem of company BIM](01-bim.ipynb)
 * [Least Absolute Deviation (LAD) Regression](02-lad-regression.ipynb)
 * [Mean Absolute Deviation (MAD) portfolio optimization](03-mad-portfolio-optimization.ipynb)
 * [The dual problem of the microchip production problem](04-bim-dual.ipynb)
 * [A variant of BIM problem: maximizing the lowest possible profit](05-bim-maxmin.ipynb)
 * [Two variants of the BIM problem using fractional objective or additional fixed costs](06-bim-fractional.ipynb)
-* [The BIM production problem using demand forecasts](07-bim-rawmaterialplanning)
+* [The BIM production problem using demand forecasts](07-bim-raw-material-planning.ipynb)
 * [Extra material: Wine quality prediction problem using $L_1$ regression](08-L1-regression-wine-quality.ipynb)
-* [Extra material: Multi-product facility production](09-multiproductionfaciliity_worstcase.ipynb)
+* [Extra material: Multi-product facility production](09-production-faciliity-worst-case.ipynb)
 
 Go to the [next chapter](../03/03.00.md) about mixed-integer linear optimization.
\ No newline at end of file
diff --git a/_sources/notebooks/02/07-bim-rawmaterialplanning.ipynb b/_sources/notebooks/02/07-bim-demand-forecast.ipynb
similarity index 100%
rename from _sources/notebooks/02/07-bim-rawmaterialplanning.ipynb
rename to _sources/notebooks/02/07-bim-demand-forecast.ipynb
diff --git a/_sources/notebooks/02/09-multiproductionfaciliity_worstcase.ipynb b/_sources/notebooks/02/09-production-faciliity-worst-case.ipynb
similarity index 100%
rename from _sources/notebooks/02/09-multiproductionfaciliity_worstcase.ipynb
rename to _sources/notebooks/02/09-production-faciliity-worst-case.ipynb
diff --git a/_sources/notebooks/03/06-facility-location.ipynb b/_sources/notebooks/03/06-facility-location.ipynb
new file mode 100644
index 00000000..c1a16e9b
--- /dev/null
+++ b/_sources/notebooks/03/06-facility-location.ipynb
@@ -0,0 +1,11507 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6HJBsbRamSjV"
+      },
+      "source": [
+        "# 3.6 Facility location problem\n",
+        "\n",
+        "This notebook illustrates Example 20 from Chapter 3 of the textbook Hands-On Mathematical Optimization with Python, Cambridge University Press, 2024.\n",
+        "\n",
+        "For details about these models and more information we refer to the book.\n",
+        "\n",
+        "Also notice that at the end of Chapter 3 you may find a number of exercises that show that versions of this problem aiming at maximizing the importance of the customers served, when facing constraints that limit the possibility of serving all, leads to even more striking evidence for the importance of selecting the right model: it can offer large factors of speedup in solving the relevant instances!\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFJeMMrRmSjY"
+      },
+      "source": [
+        "## Preamble: Install Pyomo and solvers\n",
+        "\n",
+        "The following cell sets and verifies a Pyomo installation and the solver CBC. If run on Google Colab, the cell installs Pyomo and the CBC solver, while, if run elsewhere, it assumes they all have been previously installed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W4-kKr6smSjZ",
+        "outputId": "41c6fc95-1358-4e7c-9bdc-0c293ad75e5b"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if 'google.colab' in sys.modules:\n",
+        "    %pip install pyomo >/dev/null 2>/dev/null\n",
+        "    !apt-get install -y -qq coinor-cbc    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JmSjUyeMmSja"
+      },
+      "source": [
+        "## Problem description\n",
+        "\n",
+        "Consider the problem of a supplier facing the task of fulfilling specific customer demands with minimal costs while simultaneously deciding how many facilities to build and where. In terms of data, we are given a set $I$ of customers, a set $J$ of possible locations, the cost $c_j$ of building facility $j$, and the cost $h_{ij}$ incurred to satisfy the demands of customer $i$ at facility $j$.\n",
+        "\n",
+        "In this notebook we will formulate two equivalent models for this problem and compare their performance."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A82bpYdZmSja"
+      },
+      "source": [
+        "## First MILO formulation\n",
+        "\n",
+        "Introduce two sets of binary variables,\n",
+        "\n",
+        "$$\n",
+        "\\begin{equation*}\n",
+        "    x_j:=\n",
+        "    \\begin{cases}\n",
+        "    1 & \\text{ if facility $j$ is built,}\\\\\n",
+        "    0 & \\text{ otherwise,}\n",
+        "    \\end{cases}\n",
+        "\\end{equation*}\n",
+        "$$\n",
+        "\n",
+        "and\n",
+        "\n",
+        "$$\n",
+        "\\begin{equation*}\n",
+        "    y_{ij}:=\n",
+        "    \\begin{cases}\n",
+        "    1 & \\text{ if customer $i$ is served at facility $j$,}\\\\\n",
+        "    0 & \\text{ otherwise.}\n",
+        "    \\end{cases}\n",
+        "\\end{equation*}\n",
+        "$$\n",
+        "\n",
+        "The resulting MILO is\n",
+        "\n",
+        "$$\n",
+        "\\begin{align*}\n",
+        "    \\min \\quad & \\sum_{j \\in J} c_j x_j + \\sum_{i \\in I} \\sum_{j \\in J} h_{ij} y_{ij} \\\\\n",
+        "    \\text{s.t.} \\quad & \\sum_{j \\in J} y_{ij} =1 \\qquad \\forall \\, i \\in I & \\text{(every customer is served)}\\\\\n",
+        "    & y_{ij} \\leq x_{j} \\qquad \\forall \\, i\\in I, \\, \\forall \\, j \\in J & \\text{(facility built before use)}\\\\\n",
+        "    & x_j \\in \\{0,1\\} \\qquad \\forall \\, i\\in I\\\\\n",
+        "    & y_{ij} \\in \\{0,1\\} \\qquad \\forall \\, i\\in I, \\, \\forall \\, j \\in J.\n",
+        "\\end{align*}\n",
+        "$$"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XzvWI3kXmSja"
+      },
+      "source": [
+        "## Second MILO formulation\n",
+        "\n",
+        "Let $n=|J|$ be the number of possible facility locations and $m=|I|$ the number of customers. Note that, since $\\sum_j y_{ij} =1$ for every $i \\in I$, we can replace the $n \\times m$ constraints $y_{ij} \\leq x_j$ by only $n$ constraints, namely\n",
+        "\n",
+        "$$\n",
+        "\\begin{equation*}\n",
+        "    \\sum_i y_{ij} \\leq n x_j \\qquad \\forall \\, j \\in J.\n",
+        "\\end{equation*}\n",
+        "$$\n",
+        "\n",
+        "The second MILO is\n",
+        "\n",
+        "$$\n",
+        "\\begin{align*}\n",
+        "    \\min \\quad & \\sum_{j \\in J} c_j x_j + \\sum_{i \\in I} \\sum_{j \\in J} h_{ij} y_{ij} \\\\\n",
+        "    \\text{s.t.} \\quad & \\sum_{j \\in J} y_{ij} =1 \\qquad \\forall \\, i \\in I & \\text{(every customer is served)}\\\\\n",
+        "    & \\sum_i y_{ij} \\leq n x_j \\qquad \\forall \\, j \\in J & \\text{(facility built before use)}\\\\\n",
+        "    & x_j \\in \\{0,1\\} \\qquad \\forall \\, i\\in I\\\\\n",
+        "    & y_{ij} \\in \\{0,1\\} \\qquad \\forall \\, i\\in I, \\, \\forall \\, j \\in J.\n",
+        "\\end{align*}\n",
+        "$$\n",
+        "\n",
+        "This approach leads to a more concise mathematical formulation of the model. However, it may not be a good idea if we want to solve the problem using its linear relaxation. Indeed, by reducing the number of constraints, we inadvertently made the feasible region of the relaxation larger and less tight around the feasible integer points. This fact becomes clearly evident in the increased run-time required to solve the optimization problem when working with these weaker constraints, see figures below."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GoWTKCS4mSjb"
+      },
+      "source": [
+        "Instead of defining two separate Pyomo models, we will define a single common model and then add either the strong or weaker formulation of the \"facility built before use\" constraint."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "q92lFGTHUO9-"
+      },
+      "outputs": [],
+      "source": [
+        "import pyomo.environ as pyo\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import itertools as it\n",
+        "import matplotlib.pyplot as plt\n",
+        "from time import perf_counter as pc\n",
+        "from tqdm.notebook import tqdm\n",
+        "import subprocess"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W1Hq9OxQmSjb"
+      },
+      "source": [
+        "# Solvers and options"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "g8ASWdpwmSjc"
+      },
+      "outputs": [],
+      "source": [
+        "def ListAvailableSolvers():\n",
+        "    shell_command = \"pyomo help --solvers\"\n",
+        "    output = subprocess.check_output(shell_command, shell=True).decode()\n",
+        "    return [\n",
+        "        line.strip()[1:]\n",
+        "        for line in output.split()\n",
+        "        if line.strip().startswith(\"+\") and not line.strip().endswith(\")\")\n",
+        "    ]\n",
+        "\n",
+        "\n",
+        "def GetSolverName(solver):\n",
+        "    try:\n",
+        "        return solver.name\n",
+        "    except:\n",
+        "        return \"appsi_highs\"\n",
+        "\n",
+        "\n",
+        "def SwitchCutsOff(solver):\n",
+        "    solver_name = GetSolverName(solver)\n",
+        "    if \"cbc\" in solver_name:\n",
+        "        solver.options[\"Cuts\"] = \"off\"\n",
+        "    elif \"cplex\" in solver_name:\n",
+        "        solver.options[\"mip_cuts_bqp\"] = -1\n",
+        "        solver.options[\"mip_cuts_cliques\"] = -1\n",
+        "        solver.options[\"mip_cuts_covers\"] = -1\n",
+        "        solver.options[\"mip_cuts_disjunctive\"] = -1\n",
+        "        solver.options[\"mip_cuts_flowcovers\"] = -1\n",
+        "        solver.options[\"mip_cuts_pathcut\"] = -1\n",
+        "        solver.options[\"mip_cuts_gomory\"] = -1\n",
+        "        solver.options[\"mip_cuts_gubcovers\"] = -1\n",
+        "        solver.options[\"mip_cuts_implied\"] = -1\n",
+        "        solver.options[\"mip_cuts_localimplied\"] = -1\n",
+        "        solver.options[\"mip_cuts_liftproj\"] = -1\n",
+        "        solver.options[\"mip_cuts_mircut\"] = -1\n",
+        "        solver.options[\"mip_cuts_mcfcut\"] = -1\n",
+        "        solver.options[\"mip_cuts_rlt\"] = -1\n",
+        "        solver.options[\"mip_cuts_zerohalfcut\"] = -1\n",
+        "    elif \"gurobi\" in solver_name:\n",
+        "        solver.options[\"Cuts\"] = 0\n",
+        "    elif \"highs\" in solver_name:\n",
+        "        pass  # there is no way to do this, see\n",
+        "        # https://ergo-code.github.io/HiGHS/dev/options/definitions/\n",
+        "    elif \"xpress\" in solver_name:\n",
+        "        solver.options[\"CUTSTRATEGY\"] = 0\n",
+        "    else:\n",
+        "        pass\n",
+        "    return solver\n",
+        "\n",
+        "\n",
+        "def SwitchPresolveOff(solver):\n",
+        "    solver_name = GetSolverName(solver)\n",
+        "    if \"cbc\" in solver_name:\n",
+        "        solver.options[\"Presolve\"] = \"off\"\n",
+        "    elif \"cplex\" in solver_name:\n",
+        "        solver.options[\"preprocessing_presolve\"] = 0\n",
+        "    elif \"gurobi\" in solver_name:\n",
+        "        solver.options[\"Presolve\"] = 0\n",
+        "    elif \"highs\" in solver_name:\n",
+        "        solver.options[\"presolve\"] = \"off\"\n",
+        "    elif \"xpress\" in solver_name:\n",
+        "        solver.options[\"PRESOLVE\"] = 0\n",
+        "    else:\n",
+        "        pass\n",
+        "    return solver\n",
+        "\n",
+        "\n",
+        "def LimitSolveTime(solver, max_in_seconds):\n",
+        "    solver_name = GetSolverName(solver)\n",
+        "    if \"cbc\" in solver_name:\n",
+        "        solver.options[\"seconds\"] = max_in_seconds\n",
+        "    elif \"cplex\" in solver_name:\n",
+        "        solver.options[\"timelimit\"] = max_in_seconds\n",
+        "    elif \"gurobi\" in solver_name:\n",
+        "        solver.options[\"TimeLimit\"] = max_in_seconds\n",
+        "    elif \"highs\" in solver_name:\n",
+        "        solver.options[\"time_limit\"] = max_in_seconds\n",
+        "    elif \"xpress\" in solver_name:\n",
+        "        solver.options[\"MAXTIME\"] = max_in_seconds\n",
+        "    else:\n",
+        "        pass\n",
+        "    return solver\n",
+        "\n",
+        "\n",
+        "def ClosureForMaxTime(max_in_seconds):\n",
+        "    return lambda solver: LimitSolveTime(solver, max_in_seconds)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "SA0joA-h2imU"
+      },
+      "outputs": [],
+      "source": [
+        "def compose(*functions):\n",
+        "    def composed(*args, **kwargs):\n",
+        "        result = args[0]\n",
+        "        for func in functions:\n",
+        "            result = func(result)\n",
+        "        return result\n",
+        "\n",
+        "    return composed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "Kb9bxC4mmSjc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['appsi_gurobi',\n",
+              " 'appsi_highs',\n",
+              " 'cbc',\n",
+              " 'gdpopt',\n",
+              " 'gdpopt.gloa',\n",
+              " 'gdpopt.lbb',\n",
+              " 'gdpopt.loa',\n",
+              " 'gdpopt.ric',\n",
+              " 'gurobi',\n",
+              " 'gurobi_direct',\n",
+              " 'gurobi_persistent',\n",
+              " 'ipopt',\n",
+              " 'mindtpy',\n",
+              " 'mindtpy.ecp',\n",
+              " 'mindtpy.fp',\n",
+              " 'mindtpy.goa',\n",
+              " 'mindtpy.oa',\n",
+              " 'mosek',\n",
+              " 'mosek_direct',\n",
+              " 'mosek_persistent',\n",
+              " 'mpec_minlp',\n",
+              " 'mpec_nlp',\n",
+              " 'multistart',\n",
+              " 'scipy.fsolve',\n",
+              " 'scipy.newton',\n",
+              " 'scipy.root',\n",
+              " 'scipy.secant-newton',\n",
+              " 'trustregion']"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "initial_solvers = ListAvailableSolvers()\n",
+        "initial_solvers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eqgDYMxFmSjc"
+      },
+      "source": [
+        "# The models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "A0WRo9_bmSjc"
+      },
+      "outputs": [],
+      "source": [
+        "def FacilityLocationCommon(installation, service):\n",
+        "    model = pyo.ConcreteModel(\"Facility location\")\n",
+        "    model.facilities = pyo.Set(initialize=range(len(installation)))\n",
+        "    model.customers = pyo.Set(initialize=range(len(service)))\n",
+        "\n",
+        "    model.x = pyo.Var(model.facilities, within=pyo.Binary)\n",
+        "    model.y = pyo.Var(model.customers, model.facilities, within=pyo.Binary)\n",
+        "\n",
+        "    @model.Objective(sense=pyo.minimize)\n",
+        "    def obj(model):\n",
+        "        return sum(installation[j] * model.x[j] for j in model.facilities) + sum(\n",
+        "            service[i][j] * model.y[i, j]\n",
+        "            for i in model.customers\n",
+        "            for j in model.facilities\n",
+        "        )\n",
+        "\n",
+        "    @model.Constraint(model.customers)\n",
+        "    def ChooseOneFacility(model, i):\n",
+        "        return sum(model.y[i, j] for j in model.facilities) == 1\n",
+        "\n",
+        "    return model\n",
+        "\n",
+        "\n",
+        "def FacilityLocationWeak(installation, service):\n",
+        "    model = FacilityLocationCommon(installation, service)\n",
+        "    model.name += \" weak model\"\n",
+        "\n",
+        "    @model.Constraint(model.facilities)\n",
+        "    def ServeIfOpen(model, j):\n",
+        "        return (\n",
+        "            sum(model.y[i, j] for i in model.customers)\n",
+        "            <= len(model.customers) * model.x[j]\n",
+        "        )\n",
+        "\n",
+        "    return model\n",
+        "\n",
+        "\n",
+        "def FacilityLocationStrong(installation, service):\n",
+        "    model = FacilityLocationCommon(installation, service)\n",
+        "    model.name += \" strong model\"\n",
+        "\n",
+        "    @model.Constraint(model.customers, model.facilities)\n",
+        "    def ServeIfOpen(model, i, j):\n",
+        "        return model.y[i, j] <= model.x[j]\n",
+        "\n",
+        "    return model\n",
+        "\n",
+        "\n",
+        "def GetSolution(model):\n",
+        "    X = [model.x[j]() > 0.5 for j in model.facilities]\n",
+        "    Y = [[model.y[i, j]() > 0.5 for j in model.facilities] for i in model.customers]\n",
+        "    return X, Y, model.obj()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "raacgtZxmSjc"
+      },
+      "source": [
+        "We introduce functions to generate random instances for the problem and to then visualize the optimal solution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "9upGR6wPUO-A"
+      },
+      "outputs": [],
+      "source": [
+        "def GenerateFacilityLocationInstance(nofFacilities, nofCustumers):\n",
+        "    facilities = range(nofFacilities)\n",
+        "    customers = range(nofCustumers)\n",
+        "    xC = np.random.randint(0, 100, nofCustumers)\n",
+        "    yC = np.random.randint(0, 100, nofCustumers)\n",
+        "    xF = np.random.randint(0, 100, nofFacilities)\n",
+        "    yF = np.random.randint(0, 100, nofFacilities)\n",
+        "\n",
+        "    installation = np.random.randint(1000, 2000, nofFacilities)\n",
+        "\n",
+        "    dist = lambda i, j: ((xC[i] - xF[j]) ** 2 + (yC[i] - yF[j]) ** 2)\n",
+        "\n",
+        "    service = [[dist(i, j) for j in facilities] for i in customers]\n",
+        "\n",
+        "    return installation, service, xC, yC, xF, yF\n",
+        "\n",
+        "\n",
+        "def ShowFacilityLocation(xC, yC, xF, yF, X=[], Y=[], value=None, title=None, ax=None):\n",
+        "    if ax is None:\n",
+        "        ax = plt.gca()\n",
+        "    [\n",
+        "        ax.plot([xC[i], xF[j]], [yC[i], yF[j]], \"g-\")\n",
+        "        for j in range(len(X))\n",
+        "        if X[j]\n",
+        "        for i in range(len(Y))\n",
+        "        if Y[i][j]\n",
+        "    ]\n",
+        "    ax.plot(xC, yC, \"o\")\n",
+        "    ax.plot(xF, yF, \"s\")\n",
+        "    if title:\n",
+        "        ax.set_title(title)\n",
+        "    elif value:\n",
+        "        ax.set_title(f\"Optimal value: {value}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jLJ3UjTRmSjd"
+      },
+      "source": [
+        "We then generate an instance of the problem with 10 facilities and 100 customers and initialize both a strong and a weak Pyomo models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "-HYGbBjyUO-C",
+        "outputId": "4a99fe24-caeb-4362-ef7b-048a9fc51118"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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5KmIEJQ3m053jddvaUdro510wrbSPn9uDyCyj/fzyMdVZx9rlY6pt/XkqYgQlhUpv2VSNW22bqyQv311q/b4Lptn28Xt7EJlhZjwUFgQyjrXeqNB+XNkeQent7cWdd96J+vp6DBo0CGeddRbuuece9N1uRQiBu+66CzU1NRg0aBAaGxvx8ccf230qljCf7hw32nb9jgOYsnIj5qzegiVrWzFn9RZMWbmxX2QmXto4Y/xwNJxVaWpCpMMLDc20jw7tQWSU2fGQaaxxXDkwQVm5ciWeeOIJPPbYY/jwww+xcuVKPPjgg3j00UcTxzz44IN45JFHsGrVKmzduhWDBw/GtGnTcOyY97k05tOd43Tbupk+0n0XzFRsD6JT7BoPuo8r21M8//3f/40ZM2bgyiuvBACceeaZeOGFF7Bt2zYAsejJww8/jB/84AeYMWMGAOA//uM/UFVVhZdffhmzZ8+2+5RMcSKf7qddAPPh5FoFL1JzfKFhMqfbg0glRseD0ylpldk+QfnqV7+KJ598Eh999BH+5m/+Bu+99x42b96Mhx56CACwZ88ehEIhNDY2Jv5OMBjEpEmT0NzcnHaC0tPTg56ensT/RyIRu087we58OquBTnFyrYLdu8QaxRcaJvPrrqBEVuQaD0bHua7jyvYUzx133IHZs2dj1KhRGDBgAM4//3wsXboUc+fOBQCEQrEd9KqqqpL+XlVVVeJ7qZqamhAMBhN/6urq7D7tBDvzfl5XrMjGyZyqn1Jz7DdE/sdxnpvtE5Rf//rXeP7557FmzRps374dzz77LP71X/8Vzz77rOXPXLFiBcLhcOJPW1ubjWfcnx15P+6umZ5TOVW/lLqy3xD5H8e5MbaneL73ve8loigAcM455+Czzz5DU1MT5s2bh+rqWG13e3s7ampO/TJqb2/H+PHj035mSUkJSkpK7D7VrPLN+3mVclCBEzlVv5S6qtZvVFsnQ+S13qjAM2/tUWqce8X2CUp3dzcKCpIDM4WFhYhGowCA+vp6VFdXY8OGDYkJSSQSwdatW7Fw4UK7Tycv+eT9/JRycILdOVW7don1mkr9RtV1MkReSTdmspFhnHvJ9hTPVVddhfvuuw//+Z//iU8//RTr1q3DQw89hGuuuQYAEAgEsHTpUtx777149dVX8f777+P6669HbW0tZs6caffpeMYvKQeV+KEkT5V+w/w5kTmZxkw2Xo9zr9keQXn00Udx55134uabb8bBgwdRW1uLb3/727jrrrsSx9x22204evQoFixYgK6uLkyZMgXr16/HwIH+uRl+STmoRpWSvEypERX6DXdb9j83U3dupwm9SEtmGzPpeDbOu9qA7g70CoEP9kXQ2X0cFaXFGDu8DIWBAFBaCZQ7V6SSKiD6bvGqiEgkgmAwiHA4jLKyMq9PJ6P4jBlIn3JQ5V/1ZK9cqRHZ+03z7g7MWb0l53EvzL9Y6/y5qtxM3bmdJvQqLWl0zAAejvOuNuCxCcDJnszHFJUAi1vymqSY+f3NlwU6yA8pB7KXkdSI7P1GpXUyZI6bqTu304RepiXNjAXPxnl3R/bJCRD7fneHO+cDvizQcaqkHMh5ZlIjMvcbVdbJkDlupu7cThN6nZY0OhbuvHI0vjW53pNx3isECm08zg6MoLggnxfTkX+YfVmirP0mvk4m09kEEAube7FOpjcq0Ly7A6+07kPz7g7t95Eww80Xpbr9UlavXwJrdMx4NTkBgA/2Gduh3ehxdmAEhcglfkmNyFrSzbLn/LjZP90eC16PPVnHTF+d3cdtPc4OjKAQucRPqRHZ1smw7Dl/bvZPt8eCDGNPtjGTqqK02Nbj7MAICpFLVCghNsOut7Xmy+v1BX7hZv90eyzIMvZkXls2drixilijx9mBExQil6gQ5jXLrre15kO11wPIys3+6fZYkGnsyfpm4sKAsWs3epwdmOIhcpHsYV47uZV28Xp9gZ+42T/dHgs6jT1LSitj+5xkU1QSO84l3KiNyAN+f8leb1RgysqNGSMb8ZD65tun5n3d3DguP+n6IgDuJKsjF3aSNfP7mykeIg/IGua1i5tpF1nWF6hIhsont8eC38deXsrrgPI6FAI4d7jXJ8MUDxE5wM20S3x9AYB++0yourbHDax8ItlxgkJEtnO7rJPrC8zJVfkExCqfuNEdeYkpHh9ijpW85kXaReYSTtmw8ikPX67TyMjlN/76GScoPiNDTpnIq7JOri8whpVPFrn0xl+KYYrHR5hTJpkw7SIvGXZWVZKEb/z1M0ZQfIK7aepJ9nQe0y5yYuUTqYATFJ9gTlk/qqTzmHaRTzwFd9Nz29N+X4CVT+Q9pnh8gjllvTCdR0R+xwmKTzCnrA+WiFK+4n0ok3hKmH2IvMQJSh+9UYHm3R14pXUfmnd3KDU44znlTAHZAGLhf+aU1WcmnUeUDvsQqYBrUL6kSj4/E5ne1knOYjqP8sU+RCpgBAX+yeezrFMPTOdRvtiHLJLwjb9+pn0ExW/luSzr9D+WiFK+JowcioIAkC2LXRCIHUd9lNfFNmHLYydZ2bcGkIn2ExQ/lueyrNPfmM6jfLV89nnWyQkQm7y0fPY5nyWpvnzjrxWqLyVwm/YpHuZiSUVM51E++Nxzn1+WErhJ+whKvrlYhuvIK0znOcfv45prUNzlt6UEbtF+gpJPPp/hOvIa03n202Fccx2Tu/y4lMAN2qd44vl8AP32EMmWz2e4jsh/dBnXVp97ZA1TatZoP0EBzOfzuZMnkf/oNq65jsk9TKlZo32KJ85MPl+mcJ3fc+VEbpFpXLuF65jcwZSaNZyg9GE0ny9LuE6HXDmRW2QZ127jOibncWsAa5jisUCGcJ0uuXIit8gwrsm/mFIzjxEUC7wO17Fkjch+Xo9r8j+m1MxhBMUCr1fA802kRPbzelyTHuIptRnjh6PhrEr2pyw4QbHIy3CdrrlyIqcxDE8kD6Z48uBVuI65ciLnZBrXANC8u4OheZKClQpO1ao+OUHJkxcr4JkrJ3JW6rhmxRzJxEp/VLEPM8WjIObKidzDijmSiZX+qGof5gRFUcyVEzlPt91lSW5W+qPKfZgpHoWxZI3IWTruLkvystIfVe7DnKAojrtAEjmHFXMkEyv9UeU+zBQPEVEGrJgjmVjpjyr3YUZQiIgyYMUcgK42oLsj8/dLK4HyOvfOR2NW+qOVvyNLOTInKEREGWj/kreuNuCxCcDJnszHFJUAi1s4SXGBlf5o9u/IVI7MFA8RURZaV8x1d2SfnACx72eLsJCtrPRHo39HtnLkgBBCvtqiHCKRCILBIMLhMMrKyrw+HSLSgCxhb1ftbwWevDT3cQs2AbXjnT4b6sPunWR7owJTVm7MWPETTwVtvn1qXv3ezO9vpniIiAxgxRzJxEp/zPZ3ZCxHZoqHiIhIczKWI3OCQkREpDkZy5E5QSEiItJcvBw50+qSAGLVPG6W1HOCQkREpDkZX0LLCQoREaVXWhnb5ySbopLYcaQ82UrqWWZMRESZcSdZ7ThZUs8yYyIiskd5HScgmpGlpJ4pHiIiIpIOJyhEREQkHaZ4iIg0puUW/qQERyIo+/btw3XXXYfKykoMGjQI55xzDt55553E94UQuOuuu1BTU4NBgwahsbERH3/8sROnQkREGazfcQBTVm7EnNVbsGRtK+as3oIpKze6/lI4onRsn6B8/vnnmDx5MgYMGIDf/va32LlzJ37yk59g6NChiWMefPBBPPLII1i1ahW2bt2KwYMHY9q0aTh2zL0tdImIdCbbm2uJUtleZnzHHXfgrbfewh//+Me03xdCoLa2Frfeeiu++93vAgDC4TCqqqrwzDPPYPbs2Tl/BsuM7adbmFen6/XdtbLsNS+9UYEtuzuwaM12dH1xIu0xdr25liiVp2XGr776KqZNm4ZvfOMb2LRpE4YPH46bb74Z8+fPBwDs2bMHoVAIjY2Nib8TDAYxadIkNDc3p52g9PT0oKenJ/H/kUjE7tPW2vodB/Cj13Ym/UuqJjgQd181xvWNedyg0/X67lq72oDHJgAnezIfU1QCLG7hJCWNdP0hHS/eXEuUyvYUzyeffIInnngCZ599Nn73u99h4cKF+M53voNnn30WABAKhQAAVVVVSX+vqqoq8b1UTU1NCAaDiT91dXzw2EW3MK9O1+vLa+3uyD45AWLfzxZh0VSm/pCNm2+uJUpl+wQlGo3iggsuwP3334/zzz8fCxYswPz587Fq1SrLn7lixQqEw+HEn7a2NhvPWF+9UYEfvbYT6XJ88a/96LWd6I0qt9lwWjpdr07XSrll6w/ZuPnmWqJUtk9QampqMGbMmKSvjR49Gnv37gUAVFdXAwDa29uTjmlvb098L1VJSQnKysqS/lD+tu3pzPqvqb5hXj/Q6Xp1ulY6pTcq0Ly7A6+07kPz7o7EBDRXf0jlxZtriVLZvgZl8uTJ2LVrV9LXPvroI4wcORIAUF9fj+rqamzYsAHjx48HEFtTsnXrVixcuNDu06EsjIZv/RLm1el6dbpWism23qjnZNTw53j15lqiVLZHUJYtW4YtW7bg/vvvx5///GesWbMGTz75JBYtWgQACAQCWLp0Ke699168+uqreP/993H99dejtrYWM2fOtPt0KAuj4Vu/hHl1ul6drpVyrzf69FC34c/y6s21RKlsj6BceOGFWLduHVasWIF/+Zd/QX19PR5++GHMnTs3ccxtt92Go0ePYsGCBejq6sKUKVOwfv16DBzIhyUA18ooL6qvQE1wIELhY2lz0/FSQ7+EeXW6XtWu1Xel0C7Ktd4oAGDt23tRXVaC9khPxnUo5YMG4PG5F+Dir1Rq1fbp+h6AnP2RfdZ5tu+D4gZf74Pichll/F9eAJIeXPFh5rd/Sel0vapcq6lS6P2twJOX5v7QBZuA2vG2nqesmnd3YM7qLTmPW9Z4Nh7+r9iO3TL3Bzel63vlpQMAAF3dp/aISe2Pvivfd5GZ3998WaBsXC6jnD6uBk9cdwGqg8nRK7+GeXW6XhWu1XQpdGllbIKeTVFJ7DhNGF1HdObpg6XvD27K1Pe6uk8kTU6A5P7oy/J9STGC4pGM4UGP/oWoW7hSp+uV9Vp7owJTVm7MWF2ScTdT7iSbxGgE5YX5F6PhrEpp+0M2dp9zrr6XTgBAVVkJgABCEZN91sL5qXaPjPJ0J1nKLWt40KMlAYUFAa12jNTpemW9VjOl0EnnX16n1QQkF7PrjWTtD5k4kU4xW3YNxPpjKJI9um3HDrxMH53CFI/LcoUH/3v3IY/OjMhdLIW2R2FBAHdfFdt7KvXf2KqXDDuVTnG6T1n9fKaPknGC4iIju3v+7M1P3DwlIs+wFNo+Kqw3MsvJ3ZCd7lNWPp+7P/fHFI+LjIS0Dx05DuRYA0gki3xy5aqVQstu+rgaXD6m2jdrFyynAA3I1ffS6bsGpT1if5918npVxQmKixiqJj/JN1ceT00sfG47Akhf+qpqasIrqq0vycbJFGC2vpdOvAf+8OqxAOBIn2XKsz+meFxkJOz3uRiCaAHLKEluduXK/ZiaIHs4nQLM1PeGlg5I7IUS17c/OtVnmfLsjxEUFxkJaYvgGRA3vQN8keUlbibLKP1cskbuM7Jz6Y9e24nLx1Qb6md+S034gQzPDDdSgJn6HpB9J1kn+ixTnv1xguIiwyHtoTXA0BG2/EyWrJHdnMiV+yk1oTpZnhlupQAz9b1c/dHuPsuUZ39M8bjMzZA2S9bICcyV+5dszwzdUoC6XW8ujKB4wI2Qtt1heKI45sr9SdZnhm4pQN2uNxtOUDzidEibJWvkFObK/cmOZ0Z87crh9k9QXdSNscPLUBhI84vV5Do63VKAul1vJpyg+BTD8OQU5sr9Kd9nRnztSiD8F2wsuRUDAyfSHgfA1jeyk39xDYpPMQxPTmKu3H/yeWb0XbsyNHA4++QEsPWN7ORfjKD4lM5heBlKJHVgNFfu5v3gvbfO6jMj29oVu/C+6okTFJ/SNQwvS4mkLnLlyt28H7z3+bH6zLDyZmAzeF/1xRSPj+kWhpetRFJ3bt4P3nt7WHlmOLmOjfdVb4yg+Nz0cTWYOqoKv2z+FJ91dmNkRSm+2XAmiov8NTeVtURSV27eD957e5ktc3VqHRvvK3GC4nPpwqM/37zHd+FRllXLxc37wXtvPzNlrlbeDGwE7yv565/RlESn8CjLquXi5v3gvfdWfO0KcGqtih14X4kTFJ/KFR4FYuHR3qiTa+/dw7Jqubh5P3jvvdd37crnYgiOiQHZ/4KBN7LzvhJTPD6lW3jUSJi5fNAARIVAb1QYylkfPxm1Ze2OjiWSbpa561xSL5O+a1febB+T906yvK/ECYpP6RYezVYiGdf1xQnM/flWQyWKTb/ZidV/3IO+Aab7fvMh5v9tPVZcMcbweelaIulmmbuuJfUySqxdseEfPbyvxBSPT+kYHs1UIpkq1xqcpt/sxM/eTJ6cAEBUAD97cw+afrPT0PnotAYoHTfL3HUrqdcF76veAkII5RYhRCIRBINBhMNhlJWVeX06UuqNCkxZuTFneHTz7VN99y+Q3qjAlt0dWLRmO7q+SL/ldqbrP34yilF3/rbf5KSvggDwp3u+njXdE2//TGk2U+3f1ZZ9W3CTL15zm12pMiN0TKfpgPfVP8z8/maKx6d0Do8WFgRQUBDIODkBMq/B+WXzp1knJ0AskvLL5k9x499+JeMxtq0B6moDHpsQe3dJJhK/eM3tMne+BdafeF/1xBSPj+kcHrW6Buezzm5Dfy/XcbatAeruyD45AaR98ZruKS4iyg8jKD5ndldIv7C6BmdkRamhv5frOB3XAPXFXUCJKF+MoGggHh6dMX44Gs6q1OIXQrxEMdOVBhCrpkktUfxmw5nI1TwFgdhxTvx8vzCT4iJySm9UoHl3B15p3Yfm3R2+2fdJF5ygkC9l290y2xqc4qICzP/b+qyfPf9v63Mu8rT68/1CtzJ3ks/6HQcwZeVGzFm9BUvWtmLO6i2YsnIjU4sK4QSFfMvqGpwVV4zBty+p7xdJKQgA377E+D4oOq8B0j3FRd7i+id/4BoUyovs5X9W1+CsuGIMbv37UXmXx+q6Boi7gJJXuP7JPzhBIctU2SXVaolicVFB1lJip3++ynQucydv6faaDz9jiocsYQjVJaWVsX1OsjHw4jUv6JziIu9w/ZN/MIJCpjGE6qLyutgmbHnsJOtlGk7XFBd55/TTckzoTR6XDyfHnuzpdTtwgkKmMYTqsvI6y7vEypCG0zHFRR4yWknscMWxk2NPhnHtBqZ4yDSGUNXANBzp6NDRHDsvmzzOCifHnk7jmhMUMo0lpPLLlYYDYmk4blxFfuP188nJsafbuOYEhUzTfZdUFXAnV9KV188nJ8eebuOaExQyTfddUlXANBzpyuvnk5NjT7dxzQkKWcISUrl5HeYm8pKXzycnx55u45pVPGQZS0jdZaaskDu5ku7cfj7Fx2cocgwVgweg8+iJtMflM/Z0G9ecoFBeWELqDrNlhdzJlci951O68ZlOvmNPt3HNFA+R5KyWFTINR+S8TOMzHTvGnk7jOiCEUK4eKRKJIBgMIhwOo6yszOvTIXJMb1RgysqNGR9+8ZDu5tunZvxXkw47ThJ5wcj4rBhcjB9cORrVwUHcSRbmfn8zxUMEeQe7Hbv2Mg1HqlN5fHYcPY7q4CDbx6CVcS1rO2bCCQppT+Zto3UrKyRKxfFpD5nbMROuQSGtyb5ttG5lhUR9cXzaQ/Z2zIQTFNKWtNtGd7UB+1uB/a24aOBeXDpkH8YF9mDsl39qcShxKHftJb+Sdnz24fWutUao0I6ZMMVD2pLyrcxdbcBjE4CTsReZFQJ4FgD6vBn+mBiAqT0/wQGcDsBfZYVEcVKOzxQqlP2q0I6ZMIJC2pIyf9zdkZicZDIwcAJDA4d9WVZIFCfl+ExD9rJfVdoxHUZQSFuq5I/TabrmHIydeAkjJ+RLvVGBQ4ezT9TjZBifMu+qrfJzjhMU0pbK20afe0YQkODhR2Q3M7uyyjQ+ZS3nV/k5xxQPacvrt54SUTKju7JyfBqn8nOOExTSmuz5YyJdZKs2ScXxaY6qzzmmeEh7MuePZaLaLpR20/36nZar2iTuzitH41uT66Vre9n7h4rPOccnKA888ABWrFiBJUuW4OGHHwYAHDt2DLfeeivWrl2Lnp4eTJs2Df/+7/+Oqqoqp0+HKC1Z88eyUHEXSjvpfv1uMFpFcvqQEul+qarSP1R7zjma4nn77bfxs5/9DOeee27S15ctW4bXXnsNL774IjZt2oT9+/fj2muvdfJUiNRQWgkUlWQ/pqgkdpxLVN2F0i66X79bVK02Yf9wjmMRlCNHjmDu3LlYvXo17r333sTXw+EwfvGLX2DNmjWYOnUqAODpp5/G6NGjsWXLFlx88cVOnRKR/MrrgMUtsf1QMimtjB3ngly7UAYQ24Xy8jHV0v2r1g66X382dqc03Ko2sfO82T+c5dgEZdGiRbjyyivR2NiYNEFpaWnBiRMn0NjYmPjaqFGjMGLECDQ3N6edoPT09KCn51RNfCQSceq0ibxXXufaBCQXlXehtIPu15+JEykNN3Zltfu82T+c5UiKZ+3atdi+fTuampr6fS8UCqG4uBjl5eVJX6+qqkIoFEr7eU1NTQgGg4k/dXVyPLyJ/E7lXSjtoPv1p+NkSsPJahMnzpv9w1m2R1Da2tqwZMkSvPHGGxg40J5c4YoVK7B8+fLE/0ciEU5SiFyg6roAu+h+/ancSGk4UW3i1HmzfzjL9ghKS0sLDh48iAsuuABFRUUoKirCpk2b8Mgjj6CoqAhVVVU4fvw4urq6kv5ee3s7qqur035mSUkJysrKkv4QkfNUeFurk3S//r56owLPvLXHcEojH/Fqkxnjh6PhrMq812+YScWk0xsVaN7dgVda96F5d0fizb/sH86yPYJy2WWX4f3330/62g033IBRo0bh9ttvR11dHQYMGIANGzZg1qxZAIBdu3Zh7969aGhosPt0iCgPKryt1Um6X3+c0e3n42RLaeSTism1buXuq8bgpue2p/08AT36h1Nsn6AMGTIE48aNS/ra4MGDUVlZmfj6jTfeiOXLl6OiogJlZWW45ZZb0NDQwAoeIgnF1wWkPqSrJdznwQm6X3987YaRHV7jZEtpWE3FZLr2+LqVJ667wKYzpHQ82Un2pz/9KQoKCjBr1qykjdqISE4q7kJpJ7evX5ZdSc1sPw9YKwV241qtlDAbWbfyw1c/QP833CR/LsuMrQsIIcxMjKUQiUQQDAYRDoe5HoWIfEWmXUmbd3dgzuotho6N//o1U23j5rXGoyFA+lRd6nmbufZcXph/McuMv2Tm9zdfFkhEJAnZdiU1s5bEbCmw29dqtoTZznU0sq3JUQVfFugCWcK1MmGbECWTcVdSo2s3zL7Az6trNZOqs3MdTd/P4rPPOE5QHCZTuFYWbBOi/mTcldTo2g2zbxf28lqNvjDPyLVXlZUACKA9YmxtC5995jDF4yDZwrUyYJsQpSfjrqTxMmug/1LQfMqsZbzWVEau/YdXj8UPrzbWPnz2mccJikNyhTCBWAgzvuGPDtgmRJnJuiupE9vPy3qtqYxcu5Fj+Oyzhikeh8gYrvUa28R/mE+3j1tv87XC7jJrma81lZFrz3UMn33WcILiEBVCmG5jm/gL8+n2kn3XWqNrN4x+lszXmsrItWc7hs8+a5jicYgqIUw3sU38g/l0Zzj5Nl/Z6HStfPZZwwiKQ1QKYbqFbeI8N1IuMpbD+olOu/bqcq189lnDCYpDVAthuoFt4iy3Ui7MpzvPznSK7HS4Vj77rGGKx0E6hTCNYps4w82UC/PpRObx2WceIygO0yWEaQbbxF5up1yYT/c/Vmc5g88+czhBcYEOIUyz2Cb2cTvlwny6v7E6y1l89hnHFA+R4txOuTi1uyh5j9VZJBNOUIgU50XKhfl0/+FupyQbpniIFOdVyoX5dH9hdZaeZF5vxAkKkeK8LGFkPt0/WJ2lH9nXGzHFQ+QDTLlQvlidpRcV1hsxgkKOkzmEmI1q582US3qq3Uev6FKdxf6gzm7QnKCQo2QPIWai6nkz5ZJM1fvoBR12O2V/iFFlvRFTPOQYFUKI6ah63pSM99E8P6cK2R9OUWW9ESMo5AhVQoipVD1vSsb7aJ0fU4XsD8lUWW/ECAo5wkwIUSaqnjcl8+o+9kYFmnd34JXWfWje3aHsniHxVOGM8cPRcFal8r+0Oa6TxdcbZbqrAcRSX16vN2IEhRyhSggxlarnTcm8uI9c3yAvjutkqqw3YgSFHKFKCDGVqudNydy+j1zfIDeO6/5UWG/ECAo5QrWSxXjpYShyDBWDB6Dz6Im0x8l23lZYLbNUqTzTzf7H9Q3yU+155BbZ1xtxgkKOUCWECKQPzacj23lbYTUNoVr6ws3+p0rJps5Ueh65TeatCZjiIceoEELMFJpPR6bztsJqGkLV9IVb/Y/rG9SgwvOIkjGCQo6SOYSYLTQPxP5lVTG4GD+4cjSqg4OkOW8rrKYhVE9fuNH/uL4hM9nSgjI/j6g/TlDIcbKGEI2E5juOHkd1cJCU52+G1TSEH9IXTvc/rm9IT9a0oKzPI+qPKR7Slk6heavXqlMbWRVf3wCg374Suq5vUDUtSHLhBIW0pVNo3uq16tRG+eD6hlNypQWBWFpQ1U3syD1M8ZC2dArNW71WmduI6xvcZbS9/ZAWJDlwgkLa0qn00Oq1ytpGXN/gLjPtzbQg2YUpHtKaTqF5q9cqWxtxfYO7zLY304Jkl4AQQrlEYCQSQTAYRDgcRllZmafnYiTsKVso2u+stLdO90jlnWR7owJTVm7MmEKIp5w23z7Vt/fPTVbaO/53cqUF439Hhn5F7jHz+5spnjwYCXvKGor2K6vt7dfQfDpWr1WGNuL6BndZaW8zaUE+HykbpngsMhL2ZCjaXWxv/+P6BndZbW8jaUGOV8qFERQLjOyu+cNXPwAQUHYHTtWovuMpGcP1De7Kp72zVTVxvJIRnKBYYCTsGYr0ZP0MhqLtxdB/ZrLl+E2fT1cb0N0BALhooMClQ/ah48jxxC+3z8UQ7MfpAPxVGi6DfMvMM6UFOV7NkW0Mu4UTFAvsDB8zFG0Phv7Tky3Hb/p8utqAxyYAJ2MT/kIAzwJAyalDjokBmNrzExz4cpLil9JwGThVZs7xapxsY9hNXINigZ3hY4ai7cHQf3+y5fgtnU93R2JyksnAwAkMDRz2ZWm4DJwoM+d4NUa2Mew2RlAsMBL2rCorARBAe0S+HTj9SOYdT70gW47f6fNpuuYcjJ14CSMnDrF7l1yO19xkG8NeYATFgnjYM9MGMgLAD68eix9ezReIuYUvbEtmJsfvh/M594ygNvfWK/H1JDPGD0fDWZV5tTfHa26yjWEvcILiINl24PQ7tvcpsuX4ZTsf8h7Ha3YcM0zxJDG6Ujoeesukb+ht+rgaTB1VhV82f4rPOrsxsqIU32w4E8VFnBs6we8vbDNKthy/bOdjha6VFE5Sebw63R/8MGbyxQnKl8yslDYTegt/cbzf5/588x4tVmB7RYYdT70mW45ftvMxS+dKCqepOF7d6A+qjxk78J/xML9S2mhI7b92hrRegU3ekS3HL9v5mKF7JQUlc6s/qDxm7KL9BCXXSmkglq7pjZ46wmhIbV3rPlOfS2Qn2XL8ls6ntBIoKun/9b6KSmLHOcDK84H8y+3+INsYdpv2KR4rOxoaCb1VDC5Gx9Hjpj6XqC87ctyy5fhNn095HbC4JbGTbFqllbHjUvXZgdbU3+tD1x1Pud4mPS/6g2xj2E3aT1CsrJQ2srvijPG1eOqtT237+aQXO3PcsuX4TZ9PeV3OiUQ/KTvQplVUEpv8ZPlsHSspuN4mM6/6g2xj2C3ap3isrpTOFXq7fEy1rT+f9ME1DzYwsAMtTvZkj7BAv0oK9r3sdOsPXtM+gpLPSulcb+vUfQW2G2QLRed7PvnuHilbe6hOp0oK7lyam+/7gw1pUTtpP0HJ92VYmUJvTr1ki06RLRRtx/nkk+OWrT38QKdxrOt6GzN83R9sSovaSfsUD+DcSmndV2A7SbZQtF3nYzXHLVt7+Iku41jH9TZW+LY/2JQWtZP2EZQ4p1ZK67wC2ymyhaLtPB8rOW7Z2sOPdBjHXF9hnA79QQa2R1Camppw4YUXYsiQIRg2bBhmzpyJXbt2JR1z7NgxLFq0CJWVlTjttNMwa9YstLe3230qptn5Miw3PldXsr1Ey87ziee4M/WQAGJpm745btnaw6/8Po6t9D2d+b0/yMD2CcqmTZuwaNEibNmyBW+88QZOnDiBv//7v8fRo0cTxyxbtgyvvfYaXnzxRWzatAn79+/Htddea/epkE/JFoq283ys7B4pW3uQmrhzKcnG9hTP+vXrk/7/mWeewbBhw9DS0oJLLrkE4XAYv/jFL7BmzRpMnToVAPD0009j9OjR2LJlCy6++GK7T4l8RrZQtN3nE89xpy54rc6w4FW29pBCfAfaXAv+HNqBVlVm+x6RkxxfgxIOhwEAFRWxsGBLSwtOnDiBxsbGxDGjRo3CiBEj0NzczAmKR1QqTzVc6ldxFNjflvmDbCqZc6L00EyO2/elj1Zk2YG2Vwh8sC+C0MlSDOkoxUVlQtq+7gWuryBZODpBiUajWLp0KSZPnoxx48YBAEKhEIqLi1FeXp50bFVVFUKhUNrP6enpQU/PqX8JRSIRx85ZR6qVpxop9Wu6rByFj090pWTOqdJDo7tH+rr0MR9pdqBN7utHAByUuq97RdedS0kujpYZL1q0CDt27MDatWvz+pympiYEg8HEn7o69zaK8TtVy1Nzlfp97YxCV0vmvC499Prnq0DVvk7kCo9fzJlOQAjhyGs4Fy9ejFdeeQVvvvkm6uvrE1/fuHEjLrvsMnz++edJUZSRI0di6dKlWLZsWb/PShdBqaurQzgcRllZmROnr4XeqMCUlRszVoDEUwObb59qejdUt8LDGX/W/lbgyUtzf8CCTUDteOfPxyVe/3xZOdXXiXzFhZ1kI5EIgsGgod/ftqd4hBC45ZZbsG7dOvzhD39ImpwAwIQJEzBgwABs2LABs2bNAgDs2rULe/fuRUNDQ9rPLCkpQUlJjpkdmebEzpFup4tkC0V7fT5e/3xZcZdUIgOsvJjTQbZPUBYtWoQ1a9bglVdewZAhQxLrSoLBIAYNGoRgMIgbb7wRy5cvR0VFBcrKynDLLbegoaGBC2RdZnd5ajyEnhqSi4fQmWogr7AUm0g9tq9BeeKJJxAOh/G1r30NNTU1iT+/+tWvEsf89Kc/xT/8wz9g1qxZuOSSS1BdXY2XXnrJ7lOhHOwsT821mykQ2820N+pIRpEoK5ZiE6nHkRRPLgMHDsTjjz+Oxx9/3O4frwwZ1grYWZ7KEDrJLFdfB4CKwQMQihxD8+4OQ+NRhjFM5Gd8F48HZCnrtbM8lSF0klm2vh7XefQElv2qFUDu8SjLGCbyM77N2GWylTraVZ4qXQhdwpI58lamvp5OtvEo2xgm8ivHyoydZKZMyWlmwrwylzrmG66OX1uudJGr1+ZCyRypJ97XQ+EvcM9/fojOo8fTHpeuz/qhNN8qFc6R5OdpmbFOzIZ5ZV6nkW95qpS7mUpWMkdyiPf15t0dGScnQPrx6IfSfCtUOEfyH6Z4LLIS5vX7Og3uZkoqsTIenSrNlzldpMI5kj8xgmJBrpLaAGIltZePqU6KGEi3TsMBfNEYqcLKeHSzND/Tc8RNKpwj+RcnKBZYDfPK/NZZO/PLKu5myvy6fqyMR11K8+Pj4a0/H5L2HMn/OEGxwGqYV8p1GmB+Wffr15WV8ahDaX668ZCLqmlpkhvXoFiQT5hXtnUauueXdb9+3VkZj74tzUfm8ZCLymlpkhcjKBZkCvPW4hCGBg4jAKDytGJcNHAvsL/t1AFflrVmWqcBAM27O1xLM+ieX9b9+r1iVzrNrs+xsm7KjrVWsqV8s42HTLxMS5P/cYJiQbowby0OYWPJrRgYOBE76ASA1Sl/sagEWNwClNf1W6fhRZpB5hy4G3S/fi/Y1c/tHi9W1k35rTQ/13hI5WVamvTAFI9FqWHeoYHDpyYnmZzsSbtxmFdpBllz4G7R/frdZlc/91NaTqaUr9l+zu0DyGmMoOShb5i3p60Y+IP5z/AyzSBjDtxNul+/m+zq535My8lSmm+0ny/+u7Mw+a//ipVu5DhGUPIUD/N+7W/+ytLfN5NmsFs8B57pERNALGzu1/yy7tfvJrv6uZfjxUnx58iM8cPRcFalJ7/4jY6HZZf/H8/OkfTCCYrHvEwzxHPgAPo9lHTIL+t+/W6yq58zLeccjgeSDScoHvM6zSBTDtwLul+/W+zq516PF7/jeCCZcA2Kx2QoNZQlB24Xs+Wnfrt+K4y0WT5lvUb6ecXgYoTCX6B5d0fGz5ZhvDhBpp2MOR6Mkeme+RUnKB6TpdRQxe3p07FafuqX67fCSJvlW9abrZ/jy//vOHocy379XtbPlmW82EnGnYx1Hg9GyHjP/CgghDCzL48UIpEIgsEgwuEwysrKvD6dmK424LEJsVLiTPrsg5KKHT5/8fLT1A4d/1XFEHV/RtoMgG3tanQb9Vyf7Zfxwj6rHt6z/Jj5/c0Jip262tLuc5Lw5U6ymTBkaF1vVGDKyo0Zf/HFQ/+bb5/KNv2SkTarKisBEEAoYl+7xvt5KHIM97z+ATqPpt8/KNdnqz5e2GeNk+Ve857lz8zvb6Z47FRel3UCkgvDqtZxV1jzjLRZKJIlIghr7Rrv5827OzJOTox8turjhX3WGJmiZbxn7mIVD/kCy0/Ns7MtrHyW7vdM9+s3QrZdg3nP3MUJCvkCy0/Ns7MtrHyW7vdM9+vPJdeuwUBs1+DeqHurFHjP3MUUD/mCX8tPnWSkzeJrUNoj9rerV/dMlvUM7LPZyZhOMXzPKo4mv8k+VY71iBTDCQr5gh/LT51mpM1+ePVYAHCkXb24ZzKtZ2CfzU7GdIqRe9Z0WTkKH59ouaKTTmGKh3yDu2CaZ6TNnGxXN++ZbOsZAPbZbGRNp+S6Z187ozD75ATI+GZ7SsYyY/IdWUL4KnF6J1mjP/9w+yeoLurG2OFlKAyk+WyLoXHZy0O97LOyjpf4PcuVTpHunu1vBZ68NPcH/L9fA6dVZf6+T9NALDMmralefuoFI23mZLsWFgTQUNkNvHClI6FxGdcz9OVVn5Up5ZVK9hRY3vfsV9cBvcczf59pIKZ4iEgS3R2OhcZlXM/gNRlTXql8nQLLNjkBmAYCIyhEnkgND08YORQtn30uXZjdL2Rdz+CVXCW8AcRKeC8fU+15P+TLC/XFCQqRy9KF1QsCQN/tHGQJs/sFS3qTyZ7ySsW0rZ6Y4iFyUaaweupeUzKF2f0gvp4BOLV+IU6G9QxuY8qLVMAJCpFLsoXVU3m1U6af+Xo9g0lMeTmotDK2wDWbwgHunIvimOKRiKzlfmSPXGH1VLKF2f3A6fUMbpRi2/HZTHk5qLwuVn2TbYHrkXZgzf91/FRU/53CCYokZC73I3tYDZczzG4vp9YzODmG7f5s2Ut4lZfrzfb7Wx0/BT/8TmGKRwIqlPtR/qyGy7UJsxsJjReVxI6TjJNj2KnPZsrLQw73db/8TuFOsh6TfYdLsk+unTHTKQgAf7rn6ygu0uTfEl1t2UPjEu6u6eQYduP5oHoaQFkO9XXZf6dwJ1mFqFbuR9ZlC6tnEhVAy2ef63Pvc4XGJeTkGHbj+cASXo841Nf99DtFk3+WyYvlfnrJFFbPhvdebk6OYT4fyCw/9RlOUDzGcj/9TB9Xg823T8WdV442dDzvvdycHMN8PpBZfuozTPF4TMpyvy9zo71C4IN9EXR2H0dFafGpN8xKuA5ANYUFAXxrcj1+vnmPXPeeTHNyDDvx2Vxz4m9S/k6xiBMUj0lX7tfVBjw2ATjZg0IA56Y7hm/ZtIV0954sid/Hm57bnvb7Atbvo919xA+lp5Sdn54rTPFIQKpyPwffKEv9SXXvSUp29RG/lJ5Sbn55rrDMWCIyhF57972LwtVfy33c/D+gcPj5zp+QJmS492SNW2Wd+fQR2UtPyRkyvjWdZcaKkqHc74N9kfRpnXTHDXf8dLQhw70na9wq68ynj/ip9JSM69tn1u84gEt//Hul0ntM8VCSzu7jth5H5HcqlHWqcI7kHFXTe5ygUJKK0mJTx/VGBZp3d+CV1n1o3t3BN++SdlQo61ThHMkZ2d6iLvtb05nioSRjhxtb0zN2eBkrAoigRlmnCudIzlA5vccICiUpDBhbMLX1kw4lQ4ZEdouXdQKnyjjjZCnrVOEcyRkqp/c4QaFkBt6yKYpK8OAfDykZMiRyggplnSqcI9nv9NNyvDXZ5HFuYoqHkpXXxTZhy7KT7PZDBWhd05bxI2QOGdrNyfJglh6rZfq4Glw+plrqe6bCOZLNjP47UcJ/T3KCQv19+ZbNQiBtKfFfDu4DkHmCEidjyNBOTq7B4foeNalQLq7COZJ9Dh3NsfGmyePcxBQPmcaKAGfL9lQtCSQi+aj8vOYERXHpynydLv2NVwRkCgoHEPvXvl8rApws21O5JJCI5GPleS3L9hFM8SgsXRqgvHQAAKCr+0Tia3anBvz0MiornCzbU7kkkIjkY/Z5LVN6mREURWVKA3R1n0ianADOpAZ0rghwsmxP5ZJAIpKT0ee1bOllRlAUlC0NkI5AbKb8o9d24vIx1bZFNnStCHAyp6tyvthprGoisi7X8zpXetmJ3yG5eDpBefzxx/HjH/8YoVAI5513Hh599FFcdNFFXp6SEnKlAdJxKjWgY0WAk7tycsfP9GQKOxOpKtvzWsb0smcpnl/96ldYvnw57r77bmzfvh3nnXcepk2bhoMHD3p1SsrIJ7zP1ED+nNyVkzt+9idb2JnIj2RML3s2QXnooYcwf/583HDDDRgzZgxWrVqF0tJSPPXUU16dkjLyCe/rmBpwgpNrcHRe35OKVU1E7pAxvexJiuf48eNoaWnBihUrEl8rKChAY2Mjmpub+x3f09ODnp5Tm8hEIhFXzlNWudIA6eiaGnCSk2twdF3fk0rGsDORH8mYXvYkgnLo0CH09vaiqqoq6etVVVUIhUL9jm9qakIwGEz8qaurc+tUpZQtDZCOrqkBN8RzujPGD0fDWZW2tq+Tn60KGcPORH4kY3pZiTLjFStWIBwOJ/60teXeZt3vMqUByksHJPZCidMxNUD+IGPYmcivZEsve5LiOf3001FYWIj29vakr7e3t6O6urrf8SUlJSgpke9Ni17LlAYAoH1qgPxBxrAzkZ/JlF72ZIJSXFyMCRMmYMOGDZg5cyYAIBqNYsOGDVi8eLEXp6SsTGVjzMeTH+i+azGRF2TZPsKzFM/y5cuxevVqPPvss/jwww+xcOFCHD16FDfccINXp0REEpIt7ExE7vBso7Z//Md/xP/+7//irrvuQigUwvjx47F+/fp+C2eJiGQKOxOROwJCCOU2EIhEIggGgwiHwygrK/P6dIiIiMgAM7+/lajiISIiIr1wgkJERETS4QSFiIiIpMMJChEREUmHExQiIiKSDicoREREJB1OUIiIiEg6nKAQERGRdDhBISIiIul4ttV9PuKb30YiEY/PhIiIiIyK/942som9khOUw4cPAwDq6uo8PhMiIiIy6/DhwwgGg1mPUfJdPNFoFPv378eQIUMQCNj7srBIJIK6ujq0tbXxPT8OY1u7h23tHra1e9jW7rGrrYUQOHz4MGpra1FQkH2ViZIRlIKCApxxxhmO/oyysjJ2eJewrd3DtnYP29o9bGv32NHWuSIncVwkS0RERNLhBIWIiIikwwlKipKSEtx9990oKSnx+lR8j23tHra1e9jW7mFbu8eLtlZykSwRERH5GyMoREREJB1OUIiIiEg6nKAQERGRdDhBISIiIulwgtLH448/jjPPPBMDBw7EpEmTsG3bNq9PSXlNTU248MILMWTIEAwbNgwzZ87Erl27ko45duwYFi1ahMrKSpx22mmYNWsW2tvbPTpj/3jggQcQCASwdOnSxNfY1vbZt28frrvuOlRWVmLQoEE455xz8M477yS+L4TAXXfdhZqaGgwaNAiNjY34+OOPPTxjNfX29uLOO+9EfX09Bg0ahLPOOgv33HNP0rtc2NbWvPnmm7jqqqtQW1uLQCCAl19+Oen7Rtq1s7MTc+fORVlZGcrLy3HjjTfiyJEj9pygICGEEGvXrhXFxcXiqaeeEh988IGYP3++KC8vF+3t7V6fmtKmTZsmnn76abFjxw7R2toqrrjiCjFixAhx5MiRxDE33XSTqKurExs2bBDvvPOOuPjii8VXv/pVD89afdu2bRNnnnmmOPfcc8WSJUsSX2db26Ozs1OMHDlSfOtb3xJbt24Vn3zyifjd734n/vznPyeOeeCBB0QwGBQvv/yyeO+998TVV18t6uvrxRdffOHhmavnvvvuE5WVleL1118Xe/bsES+++KI47bTTxL/9278ljmFbW/Ob3/xGfP/73xcvvfSSACDWrVuX9H0j7Tp9+nRx3nnniS1btog//vGP4q//+q/FnDlzbDk/TlC+dNFFF4lFixYl/r+3t1fU1taKpqYmD8/Kfw4ePCgAiE2bNgkhhOjq6hIDBgwQL774YuKYDz/8UAAQzc3NXp2m0g4fPizOPvts8cYbb4hLL700MUFhW9vn9ttvF1OmTMn4/Wg0Kqqrq8WPf/zjxNe6urpESUmJeOGFF9w4Rd+48sorxT/90z8lfe3aa68Vc+fOFUKwre2SOkEx0q47d+4UAMTbb7+dOOa3v/2tCAQCYt++fXmfE1M8AI4fP46WlhY0NjYmvlZQUIDGxkY0Nzd7eGb+Ew6HAQAVFRUAgJaWFpw4cSKp7UeNGoURI0aw7S1atGgRrrzyyqQ2BdjWdnr11VcxceJEfOMb38CwYcNw/vnnY/Xq1Ynv79mzB6FQKKmtg8EgJk2axLY26atf/So2bNiAjz76CADw3nvvYfPmzfj6178OgG3tFCPt2tzcjPLyckycODFxTGNjIwoKCrB169a8z0HJlwXa7dChQ+jt7UVVVVXS16uqqvCnP/3Jo7Pyn2g0iqVLl2Ly5MkYN24cACAUCqG4uBjl5eVJx1ZVVSEUCnlwlmpbu3Yttm/fjrfffrvf99jW9vnkk0/wxBNPYPny5fjnf/5nvP322/jOd76D4uJizJs3L9Ge6Z4pbGtz7rjjDkQiEYwaNQqFhYXo7e3Ffffdh7lz5wIA29ohRto1FAph2LBhSd8vKipCRUWFLW3PCQq5ZtGiRdixYwc2b97s9an4UltbG5YsWYI33ngDAwcO9Pp0fC0ajWLixIm4//77AQDnn38+duzYgVWrVmHevHken52//PrXv8bzzz+PNWvWYOzYsWhtbcXSpUtRW1vLtvY5pngAnH766SgsLOxXzdDe3o7q6mqPzspfFi9ejNdffx2///3vccYZZyS+Xl1djePHj6OrqyvpeLa9eS0tLTh48CAuuOACFBUVoaioCJs2bcIjjzyCoqIiVFVVsa1tUlNTgzFjxiR9bfTo0di7dy8AJNqTz5T8fe9738Mdd9yB2bNn45xzzsE3v/lNLFu2DE1NTQDY1k4x0q7V1dU4ePBg0vdPnjyJzs5OW9qeExQAxcXFmDBhAjZs2JD4WjQaxYYNG9DQ0ODhmalPCIHFixdj3bp12LhxI+rr65O+P2HCBAwYMCCp7Xft2oW9e/ey7U267LLL8P7776O1tTXxZ+LEiZg7d27iv9nW9pg8eXK/cvmPPvoII0eOBADU19ejuro6qa0jkQi2bt3Ktjapu7sbBQXJv6oKCwsRjUYBsK2dYqRdGxoa0NXVhZaWlsQxGzduRDQaxaRJk/I/ibyX2frE2rVrRUlJiXjmmWfEzp07xYIFC0R5ebkIhUJen5rSFi5cKILBoPjDH/4gDhw4kPjT3d2dOOamm24SI0aMEBs3bhTvvPOOaGhoEA0NDR6etX/0reIRgm1tl23btomioiJx3333iY8//lg8//zzorS0VDz33HOJYx544AFRXl4uXnnlFfE///M/YsaMGSx9tWDevHli+PDhiTLjl156SZx++unitttuSxzDtrbm8OHD4t133xXvvvuuACAeeugh8e6774rPPvtMCGGsXadPny7OP/98sXXrVrF582Zx9tlns8zYCY8++qgYMWKEKC4uFhdddJHYsmWL16ekPABp/zz99NOJY7744gtx8803i6FDh4rS0lJxzTXXiAMHDnh30j6SOkFhW9vntddeE+PGjRMlJSVi1KhR4sknn0z6fjQaFXfeeaeoqqoSJSUl4rLLLhO7du3y6GzVFYlExJIlS8SIESPEwIEDxVe+8hXx/e9/X/T09CSOYVtb8/vf/z7t83nevHlCCGPt2tHRIebMmSNOO+00UVZWJm644QZx+PBhW84vIESf7fiIiIiIJMA1KERERCQdTlCIiIhIOpygEBERkXQ4QSEiIiLpcIJCRERE0uEEhYiIiKTDCQoRERFJhxMUIiIikg4nKERERCQdTlCIiIhIOpygEBERkXQ4QSEiIiLp/H+iwc5s5T+3uwAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "np.random.seed(2023)\n",
+        "\n",
+        "installation, service, xC, yC, xF, yF = GenerateFacilityLocationInstance(20, 200)\n",
+        "ShowFacilityLocation(xC, yC, xF, yF)\n",
+        "\n",
+        "weak = FacilityLocationWeak(installation, service)\n",
+        "strong = FacilityLocationStrong(installation, service)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "PtIa7AkF3I2_"
+      },
+      "outputs": [],
+      "source": [
+        "def Solve(solver, model):\n",
+        "    t = pc()\n",
+        "    _ = solver.solve(model)\n",
+        "    t = pc() - t\n",
+        "    X, Y, v = GetSolution(model)\n",
+        "    title = f\"{model.name} solved by {GetSolverName(solver)} in {t:.2f} seconds {v:.0f}\"\n",
+        "    return X, Y, v, title"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "eD-SBFIkUO-F",
+        "outputId": "4da09e06-63fe-4861-947b-e1b417ba348c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "solver = pyo.SolverFactory(\"cbc\")\n",
+        "ShowFacilityLocation(xC, yC, xF, yF, *Solve(solver, weak))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "1Qmid6MgmSjd",
+        "outputId": "ac75086e-0cdc-4d63-f17d-6927a49985ff"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "ShowFacilityLocation(xC, yC, xF, yF, *Solve(solver, strong))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cTVJuaS0i0PL"
+      },
+      "source": [
+        "However, this is not a proper performance comparison between the two model formulations, since under the hood `cbc` is already solving the instance in a clever way. The commercial solver `Gurobi` and the open-source solver `HiGHS` also have a similar feature. \n",
+        "\n",
+        "If run on Google Colab, the following cell installs three additional commercial solvers; if run elsewhere, it assumes they all have been previously installed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ctxap_Z3jv0B",
+        "outputId": "43d932b5-58e8-4587-9ab8-a608689b5bb7"
+      },
+      "outputs": [],
+      "source": [
+        "if 'google.colab' in sys.modules:\n",
+        "    %pip install gurobipy >/dev/null 2>/dev/null\n",
+        "    %pip install cplex >/dev/null 2>/dev/null\n",
+        "    %pip install xpress >/dev/null 2>/dev/null"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0DuduTwSoEGM",
+        "outputId": "b7b5c294-7760-441a-a8d4-af575bead6be"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "set()"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "available_solvers = ListAvailableSolvers()\n",
+        "set(available_solvers) - set(initial_solvers)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 890
+        },
+        "id": "5IJCue_ln_wl",
+        "outputId": "80cb3747-2369-49fd-d40f-b730598511b1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]\n",
+            "Copyright (c) 2023 HiGHS under MIT licence terms\n",
+            "Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]\n",
+            "Copyright (c) 2023 HiGHS under MIT licence terms\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 2000x1000 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "installation, service, xC, yC, xF, yF = GenerateFacilityLocationInstance(8, 88)\n",
+        "weak = FacilityLocationWeak(installation, service)\n",
+        "strong = FacilityLocationStrong(installation, service)\n",
+        "\n",
+        "solver_names = [\"cplex_direct\", \"gurobi_direct\", \"xpress_direct\", \"appsi_highs\"]\n",
+        "solver_names = sorted(set(solver_names) & set(ListAvailableSolvers()))\n",
+        "\n",
+        "_, axs = plt.subplots(len(solver_names), 2, figsize=(20, 10))\n",
+        "plt.subplots_adjust(hspace=0.5)\n",
+        "\n",
+        "for i, solver_name in enumerate(solver_names):\n",
+        "    solver = SwitchCutsOff(pyo.SolverFactory(solver_name))\n",
+        "    ShowFacilityLocation(xC, yC, xF, yF, *Solve(solver, weak), ax=axs[i, 0])\n",
+        "    ShowFacilityLocation(xC, yC, xF, yF, *Solve(solver, strong), ax=axs[i, 1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ktcxGDIimSje"
+      },
+      "source": [
+        "We now run a more extensive performance comparison considering increasingly larger instances of the problem. We will see that the strong formulation is consistently faster than the weak formulation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "aPhtOddQChEG"
+      },
+      "outputs": [],
+      "source": [
+        "def SolveInstances(dimensions, solvers, seed, modify=lambda s: s):\n",
+        "    np.random.seed(seed)\n",
+        "    model = {\"weak\": FacilityLocationWeak, \"strong\": FacilityLocationStrong}\n",
+        "    df = pd.DataFrame(\n",
+        "        index=dimensions,\n",
+        "        columns=[f\"{s}_{m}\" for s, m in it.product(solvers, model.keys())],\n",
+        "    )\n",
+        "    for n, m in tqdm(df.index):\n",
+        "        installation, service, *_ = GenerateFacilityLocationInstance(n, m)\n",
+        "        instance = {\n",
+        "            option: model[option](installation, service) for option in model.keys()\n",
+        "        }\n",
+        "        values = dict.fromkeys(solvers)\n",
+        "        for column in df.columns:\n",
+        "            solver = column[: column.rfind(\"_\")]\n",
+        "            option = column[column.rfind(\"_\") + 1 :]\n",
+        "            t = pc()\n",
+        "            modify(pyo.SolverFactory(solver)).solve(instance[option])\n",
+        "            df.at[(n, m), column] = pc() - t\n",
+        "            *_, v = GetSolution(instance[option])\n",
+        "            values[solver] = v\n",
+        "        if len(set(values.values())) > 1:\n",
+        "            print(values)\n",
+        "    return df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fuo0eiU4mSje"
+      },
+      "source": [
+        "We solve instances with 10 to 100 facilities and 100 to 1000 customers. We then plot the run-time of the strong and weak formulations as a function of the number of facilities."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 495,
+          "referenced_widgets": [
+            "072163be46894e5fb2d9d09667429996",
+            "cd2e2ee82e564282a80e76bc9abbce69",
+            "77257b9d0f5043568a2bac799a6c819b",
+            "165d87bc12a847388d237ea630f4a8c9",
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+            "b989b18af609490d9f9e1b58961f711d",
+            "28e45e0536d54afeb6ce0213a5902e51",
+            "66843e746cb84d2ab2045423d955b8d0",
+            "28e4a9b3aa714645b133cb0c8d19fbb4",
+            "4b118cfb1ca948d6a13844eee6771e83",
+            "335e32a1bdb14773b77219dce136d84b",
+            "1073687e38144cbf959a02c7a1edc0ef",
+            "e3bff2ba80814042af7b394ef751ed31",
+            "2024a6b35a8e49ed956dc6577e899ce6"
+          ]
+        },
+        "id": "JBsS9otSAFxp",
+        "outputId": "1df9de62-0e85-439c-df35-a7023cd07998"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f1df101b86464b33ae67dfb114dc3d9b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/9 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "if \"google.colab\" in sys.modules:\n",
+        "    solvers = [\"cbc\"]\n",
+        "else:\n",
+        "    solvers = [\"gurobi_direct\", \"cplex_direct\"]\n",
+        "dimensions = [(n, 10 * n) for n in range(10, 51, 5)]\n",
+        "\n",
+        "SolveInstances(dimensions, solvers, 2023).plot(figsize=(8, 5))\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 625,
+          "referenced_widgets": [
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+            "642df90b5e1b41408a81074ea013358d",
+            "edb5cbfd04ac4822ac2a5eccd3cf6666",
+            "b0e5a3f34fa94ee382af945506190cc3",
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+            "3a2f1d392d7d4f1cb1e1b2be6a28dfec",
+            "c770e86620384eb698dc9d2b2454fbc6",
+            "2a086a827bff4b4cbb9db9f78b6da267",
+            "14a3160cb16c46b7a13d9780b33914da",
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+            "468332aff46e42e49d0e20858d0a2026",
+            "18c2f46a33a1475f9e20b09ed96f4309",
+            "591597f211084bd0b2f44dc35edac400",
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+            "31ac4b264ab4415ba96807ee4d0e06ad",
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+            "4829e9cb01a24b81948f58fa562803dd",
+            "0ef9438e841f4db496122205eef24486",
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+            "5c519d7149904a91badcc572aa3c1f91",
+            "73382149cd2845209238fcf3c9788335",
+            "829f26b3ee6d48629887b83c0e60f983",
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+            "ea3554a8554045ec9223b3e134664989",
+            "500cf76011fe45a683e06f8419b0b63a",
+            "ada1ac7e7a76477486e593e55442895e",
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+            "1e06349ebb494303acf76d783fb9da67",
+            "1a909a2324e841299ae8e4573479c641",
+            "aa6f16444c6940a18f7f6a9870c9927d",
+            "957e6b15bb47489f834c7e824ea2bb97",
+            "934fd49155fd45faa1fe0200ff81c8c8",
+            "0c53603aca8c4d1595d3b704fb40504a",
+            "48a471a2841e4a0fad4d8e3720bd3e5f",
+            "978e1da737f543b1a90fbfde3eb14ec8",
+            "b10d500d3b3c4cb9ad5316404fbed8f8",
+            "7b5e382d691e43789122b23044a6a194",
+            "4c8af45c03994d5585834ba8e889c50b",
+            "aac100fba4be415cbe0973b16a8680ec",
+            "3dd533f28a5245f2ab55c1d445bc9104",
+            "e2f71b4308724c2bbe827e292c9f45ac",
+            "a8e76377609d4397a086f7e75f0f1fff",
+            "3716355844164b24a588e39d39fd6f58",
+            "aebde985ee394558a181784bb24ff3d8",
+            "e720c7dc426a471486fa7c95fca993d2",
+            "8d04329a03f148018cc3cf13225d5c0c",
+            "b426e565409e4ea496f566b5f6c083cb",
+            "457ef66d94164abd8cb7afed6d61f01b",
+            "03a45b815fc3428e80f1a3b0e0f84970",
+            "e069acd4535c4b1c8d58a39fae727403"
+          ]
+        },
+        "id": "JjbJjN1ru3vM",
+        "outputId": "b2fb124e-9582-4c96-f5d9-82d389aa1554"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1ebc3796db2b458fb09228a935f5aed4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/9 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "WARNING: Loading a SolverResults object with an 'aborted' status, but\n",
+            "containing a solution\n",
+            "WARNING: Loading a SolverResults object with an 'aborted' status, but\n",
+            "containing a solution\n",
+            "WARNING: Loading a SolverResults object with an 'aborted' status, but\n",
+            "containing a solution\n",
+            "WARNING: Loading a SolverResults object with an 'aborted' status, but\n",
+            "containing a solution\n",
+            "WARNING: Loading a SolverResults object with an 'aborted' status, but\n",
+            "containing a solution\n",
+            "WARNING: Loading a SolverResults object with an 'aborted' status, but\n",
+            "containing a solution\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "modify = compose(ClosureForMaxTime(10), SwitchCutsOff)\n",
+        "SolveInstances(dimensions, solvers, 2023, modify).plot(figsize=(8, 5))\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 495,
+          "referenced_widgets": [
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+            "e9f02fa8c02846ec92c0c98354f7b57b",
+            "73480f5d144b4b71ba1d60b6090edf68",
+            "9003370e7ac644aea13745f95cdbcc40",
+            "53c7ac1ad2584257838784914b991e56",
+            "f09cc7e7f9164e35bfbc896753efcaba",
+            "473a1c307fb0453684a33f1df2418ba9",
+            "bfd6ce6e77c045acb94760a1f9324ec6",
+            "99823a253a014addb9eca48c2afee8f8",
+            "4a961c5e0aa3439281b152c4f5af59ae",
+            "15e5fb815c034f6faae4160a3a0c82b1",
+            "9330af137bf244f889e247d25e6e558c",
+            "08ddd5f58e354efa88bceab4767f3fb9",
+            "35343359cab04e7885ff633c8069a6bf",
+            "18d0f80e84a1469ead9f82d3bb48913b",
+            "95a7706b521d4dabbb4af9ac99e29619",
+            "8a609b1eb4c54c099171188cadf8682d",
+            "8f50142f64244cdb8ba137f5e29fa70f",
+            "4251d033b4714dd690e7488f3f468ebb",
+            "5fe315366e2d4065847ecb5620e5f6c4",
+            "afdc6fb0d6b4440da5b1fd09dd4d8c19",
+            "9a7c634d7e0646c1be57ed33bcb6efd7",
+            "be32d666dd0d4b4898cd26a6533f3fda",
+            "1155dbabf1db4bf48b567e75ee11e9fa",
+            "8e0e4cf35e4b4aa1bbf59a599150b11a",
+            "c74eebbfa22442c7ba756708f5110a68",
+            "1c891fe6ac6f4cd4a99c7cdc1c618ccd",
+            "97584ec732df478c87a7a36cfc933fdc",
+            "d3f3df0d05ef48b8929943660e4e395f",
+            "caaf55d8eae74a39872c86ceedb5685c",
+            "54fec21ccc8a4ee7b26a2b6bf7f218f4",
+            "3cd0272ed30a45deb92258eefc4fe86f",
+            "46a76fbe44404ef99e2888cb8886dfca",
+            "53246238e747442899aca531a8f27bd5",
+            "943bca12541347c5bbea3d35a9c2496a",
+            "b67cd5e62a01452391260c93b66d28d9",
+            "8ee3a4a6374e4af99e6f694eb9266e20",
+            "076146cd0e694d9480beb78f4c29ce8a",
+            "195b5f677e274fd0ae935287d9ea37e5"
+          ]
+        },
+        "id": "4Ji7zifgu6kH",
+        "outputId": "bbd266ea-0408-4a6c-c6c8-1a2a96a8477e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "480558b0b5cb45c0ba4c6f75ce5d51ab",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/9 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "SolveInstances(dimensions, solvers, 2023, SwitchPresolveOff).plot(figsize=(8, 5))\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.10"
+    },
+    "widgets": {
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diff --git a/_sources/notebooks/03/07-bim-production-revisited.ipynb b/_sources/notebooks/03/07-bim-production-revisited.ipynb
new file mode 100644
index 00000000..62bd1671
--- /dev/null
+++ b/_sources/notebooks/03/07-bim-production-revisited.ipynb
@@ -0,0 +1,1808 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{index} single: Pyomo; block\n",
+    "```\n",
+    "```{index} single: Pyomo; sets\n",
+    "```\n",
+    "```{index} single: Pyomo; parameters\n",
+    "```\n",
+    "```{index} single: solver; cbc\n",
+    "```\n",
+    "```{index} single: application; production planning\n",
+    "```\n",
+    "```{index} pandas dataframe\n",
+    "```\n",
+    "\n",
+    "# 3.7 BIM production revisited"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preamble: Install Pyomo and a solver\n",
+    "\n",
+    "The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it \n",
+    "assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object `SOLVER` for later use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5ssUqKOaPVaE",
+    "outputId": "38c1005a-39f4-4307-e305-19a4c9819396"
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    " \n",
+    "if 'google.colab' in sys.modules:\n",
+    "    %pip install pyomo >/dev/null 2>/dev/null\n",
+    "    %pip install highspy >/dev/null 2>/dev/null\n",
+    " \n",
+    "solver = 'appsi_highs'\n",
+    " \n",
+    "import pyomo.environ as pyo\n",
+    "SOLVER = pyo.SolverFactory(solver)\n",
+    " \n",
+    "assert SOLVER.available(), f\"Solver {solver} is not available.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem description\n",
+    "\n",
+    "We consider again the [BIM raw material planning problem](../02/01-bim-rawmaterialplanning.ipynb) from Chapter 2 but with more sophisticated pricing and acquisition protocols. There are now three suppliers, each of which can deliver the following materials:\n",
+    " - A: **silicon**, **germanium** and **plastic**\n",
+    " - B: **copper**\n",
+    " - C: all the above\n",
+    " \n",
+    "For the suppliers, the following conditions apply: copper should be acquired in multiples of 100 gram, since it is delivered in sheets of that weight. Unitary materials such as silicon, germanium and plastic may be acquired in any number, but the price is in batches of 100. Meaning that 30 units of silicon with 10 units of germanium and 50 units of plastic cost as much as 1 unit of silicon but half as much as 30 units of silicon with 30 units of germanium and 50 units of plastic. Furthermore, supplier C sells all materials and offers a discount if purchased together: 100 gram of copper and a batch of unitary material cost only 7. This set price is only applied to pairs, meaning that 100 gram of copper and 2 batches cost 13.\n",
+    "\n",
+    "The summary of the prices in $ is given in the following table:\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "\n",
+    "|Supplier|Copper per sheet of 100 gram|     Batches|Together|\n",
+    "|:-------|---------------------:|-----------------:|-------:|\n",
+    "| A      |                    - |                5 |      - |\n",
+    "| B      |                    3 |                - |      - |\n",
+    "| C      |                    4 |                6 |      7 |\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Next, for stocked products inventory costs are incurred, whose summary is given in the following table:\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "\n",
+    "|Copper per 10 gram| Silicon per unit| Germanium per unit|Plastic per unit|\n",
+    "|---:|-------:|---:|-----:|\n",
+    "| 0.1|   0.02 |0.02| 0.02 |\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "The holding price of copper is per 10 gram and the copper stocked is rounded up to multiples of 10 grams, meaning that 12 grams pay for 20. The capacity limitations of the warehouse allow for a maximum of $10$ kilograms of copper in stock at any moment, but there are no practical limitations to the number of units of unitary products in stock. The production is made-to-order, meaning that no inventory of chips is kept.\n",
+    "\n",
+    "Recall that BIM has the following stock at the beginning of the year:\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "\n",
+    "|Copper |Silicon |Germanium |Plastic|\n",
+    "|---:|-------:|---:|-----:|\n",
+    "| 480|   1000 |1500| 1750 |\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "The company would like to have at least the following stock at the end of the year:\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "\n",
+    "|Copper |Silicon |Germanium |Plastic|\n",
+    "|---:|-------:|---:|-----:|\n",
+    "| 200|    500 | 500| 1000 |\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "The goal is to build an optimization model using the data above and solve it to minimize the acquisition and holding costs of the products while meeting the required quantities for production. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data import\n",
+    "\n",
+    "In the next cell we import and store the data described in dictionaries or Pandas dataframes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 143
+    },
+    "id": "rvWwY74i7qEy",
+    "outputId": "47766087-eb87-44e7-d3e2-ac2c00af78f4"
+   },
+   "outputs": [
+    {
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+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "      <th>memory</th>\n",
+       "      <td>47</td>\n",
+       "      <td>62</td>\n",
+       "      <td>81</td>\n",
+       "      <td>65</td>\n",
+       "      <td>95</td>\n",
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+       "      <td>86</td>\n",
+       "      <td>89</td>\n",
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+       "      <td>82</td>\n",
+       "      <td>84</td>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec\n",
+       "chip                                                                          \n",
+       "logic     88   125   260   217   238   286   248   238   265   293   259   244\n",
+       "memory    47    62    81    65    95   118    86    89    82    82    84    66"
+      ]
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+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <th>silicon</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plastic</th>\n",
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+       "      <th>germanium</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           logic  memory\n",
+       "silicon        1       0\n",
+       "plastic        1       1\n",
+       "copper         4       2\n",
+       "germanium      0       1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Jan</th>\n",
+       "      <th>Feb</th>\n",
+       "      <th>Mar</th>\n",
+       "      <th>Apr</th>\n",
+       "      <th>May</th>\n",
+       "      <th>Jun</th>\n",
+       "      <th>Jul</th>\n",
+       "      <th>Aug</th>\n",
+       "      <th>Sep</th>\n",
+       "      <th>Oct</th>\n",
+       "      <th>Nov</th>\n",
+       "      <th>Dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>silicon</th>\n",
+       "      <td>88</td>\n",
+       "      <td>125</td>\n",
+       "      <td>260</td>\n",
+       "      <td>217</td>\n",
+       "      <td>238</td>\n",
+       "      <td>286</td>\n",
+       "      <td>248</td>\n",
+       "      <td>238</td>\n",
+       "      <td>265</td>\n",
+       "      <td>293</td>\n",
+       "      <td>259</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plastic</th>\n",
+       "      <td>135</td>\n",
+       "      <td>187</td>\n",
+       "      <td>341</td>\n",
+       "      <td>282</td>\n",
+       "      <td>333</td>\n",
+       "      <td>404</td>\n",
+       "      <td>334</td>\n",
+       "      <td>327</td>\n",
+       "      <td>347</td>\n",
+       "      <td>375</td>\n",
+       "      <td>343</td>\n",
+       "      <td>310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>copper</th>\n",
+       "      <td>446</td>\n",
+       "      <td>624</td>\n",
+       "      <td>1202</td>\n",
+       "      <td>998</td>\n",
+       "      <td>1142</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>1164</td>\n",
+       "      <td>1130</td>\n",
+       "      <td>1224</td>\n",
+       "      <td>1336</td>\n",
+       "      <td>1204</td>\n",
+       "      <td>1108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>germanium</th>\n",
+       "      <td>47</td>\n",
+       "      <td>62</td>\n",
+       "      <td>81</td>\n",
+       "      <td>65</td>\n",
+       "      <td>95</td>\n",
+       "      <td>118</td>\n",
+       "      <td>86</td>\n",
+       "      <td>89</td>\n",
+       "      <td>82</td>\n",
+       "      <td>82</td>\n",
+       "      <td>84</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov  \\\n",
+       "silicon      88   125   260   217   238   286   248   238   265   293   259   \n",
+       "plastic     135   187   341   282   333   404   334   327   347   375   343   \n",
+       "copper      446   624  1202   998  1142  1380  1164  1130  1224  1336  1204   \n",
+       "germanium    47    62    81    65    95   118    86    89    82    82    84   \n",
+       "\n",
+       "            Dec  \n",
+       "silicon     244  \n",
+       "plastic     310  \n",
+       "copper     1108  \n",
+       "germanium    66  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from io import StringIO\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pyomo.environ as pyo\n",
+    "\n",
+    "demand_data = \"\"\"\n",
+    "chip, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec\n",
+    "logic, 88, 125, 260, 217, 238, 286, 248, 238, 265, 293, 259, 244\n",
+    "memory, 47, 62, 81, 65, 95, 118, 86, 89, 82, 82, 84, 66\n",
+    "\"\"\"\n",
+    "\n",
+    "demand_chips = pd.read_csv(StringIO(demand_data), index_col=\"chip\")\n",
+    "display(demand_chips)\n",
+    "\n",
+    "use = dict()\n",
+    "use[\"logic\"] = {\"silicon\": 1, \"plastic\": 1, \"copper\": 4}\n",
+    "use[\"memory\"] = {\"germanium\": 1, \"plastic\": 1, \"copper\": 2}\n",
+    "use = pd.DataFrame.from_dict(use).fillna(0).astype(int)\n",
+    "display(use)\n",
+    "\n",
+    "demand = use.dot(demand_chips)\n",
+    "display(demand)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optimization model\n",
+    "\n",
+    "As usual, we need to start modeling by naming our decisions and identifying the relevant index sets. In the [previous BIM notebook](../02/bim-rawmaterialplanning.ipynb) we already had a set $T$ time periods (i.e., the months) and a set $P$ for the products. Since we want to keep track of how much we buy for which material and from which supplier, we need an additional index set $S$ for the suppliers.\n",
+    "\n",
+    "In the [previous BIM notebook](../02/bim-rawmaterialplanning.ipynb) all decision variables were continuous, but now we also need to take care of a number of integer decision variables:\n",
+    "\n",
+    "- number of batches of the unitary products;\n",
+    "- number of copper sheets;\n",
+    "- number of volume units in stock, since the inventory of copper pays per $10$ g;\n",
+    "- number of copper sheet-batch of unitary product pairs acquired in the same month from supplier C, yielding discount.\n",
+    "\n",
+    "Given the above, we can clearly reuse the variables $s_{pt}$ from [previous BIM notebook](../02/bim-rawmaterialplanning.ipynb) to account for the total number of grams/units of each product. With respect to purchasing variables, however, we now need to keep track of unitary products and suppliers. For this reason, we introduce the integer variables $y_{ts}$ for the number of copper sheets purchased from supplier $s$, and continuous variables $x_{pts}$ for the number of units of unitary product $p$ purchased from supplier $s$. To translate these variables into `amounts of a product purchased regardless of the supplier', we introduce another variable $u_{pt}$.\n",
+    "\n",
+    "Further, we introduce the variables that we use to account for the tricky integer quantities in our model. To account for the number of batches of unitary products at supplier $s$, we introduce integer variables $b_{ts}$, and, similarly, we introduce an integer variable $r_t$ for the number of 10 g copper batches in the inventory. Buying a copper sheet together with a batch of unitary products from supplier C is advantageous as we get a discount of $\\beta = 4 + 6 - 7$. For this reason, we introduce an extra integer variable $p_t$ to count the number of such pairs, so that $\\beta p_t$ describes the amount to the total discount acquired in month $t$. We need to include additional constraints to make sure that these three variables `take the values they should' in relation to the purchasing and inventory variables.\n",
+    "\n",
+    "Taking all the above into account, we shall use the following decision variables:\n",
+    "\n",
+    "- $y_{ts}$: number of sheets of copper purchased in month $t$ from supplier $s$\n",
+    "- $x_{pts}$: number of units of unitary product $p$ purchased in month $t$ from supplier $s$\n",
+    "- $u_{pt}$: total number of units of product $p$ purchased in month $t$ (from all suppliers combined)\n",
+    "- $b_{ts}$: number of batches of 100 unitary products purchased in month $t$ from supplier $s$\n",
+    "- $p_{t}$: number of batch-copper sheet pairs purchased in month $t$ from supplier C \n",
+    "- $r_t$: number of 10-unit amounts of copper (rounded up) in month $t$.\n",
+    "\n",
+    "We begin by formulating the objective function using \\$ cents as unit. If we denote by $\\alpha_s$ the per-batch cost of unitary products at supplier $s$, then the total material acquisition cost is\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "    \\sum_{t \\in T} \\Big( \\sum_{s \\in S} \\pi_{s} b_{ts} + \\alpha_{s} y_{ts} \\Big) - \\beta p_t \\Big),\n",
+    "\\end{equation*}\n",
+    "$$\n",
+    "\n",
+    "where we count the acquisition costs for supplier $C$ as the total cost of the sheets of copper plus the batches of unitary materials minus the discount due to purchasing the pairs:\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "    \\pi_{\\text{C}} b_{t, \\text{C}} + \\alpha_{\\text{C}} y_{t, \\text{C}}  - \\beta p_t.\n",
+    "\\end{equation*}\n",
+    "$$\n",
+    "\n",
+    "The total inventory cost is\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "    \\sum_{t \\in T} \\Big(  0.1 r_t + \\sum_{p \\in P \\setminus\\{\\text{copper}\\}} h_p s_{pt}  \\Big).\n",
+    "\\end{equation*}\n",
+    "$$\n",
+    "\n",
+    "Now, we need to construct a system of constraints to ensure that the above costs are computed correctly. To transform the amounts of unitary materials purchased into batches of $100$, we construct the following constraints:\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "    \\sum_{p \\in P \\setminus\\{\\text{copper}\\}}  x_{pts} \\leq 100 b_{ts}.\n",
+    "\\end{equation*}\n",
+    "$$\n",
+    "\n",
+    "Because the inventory costs for copper are computed by rounding up to 10 g amounts, we account for this via the following constraint\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "    s_{\\text{copper}, t} \\leq 10 r_t.\n",
+    "\\end{equation*}\n",
+    "$$\n",
+    "\n",
+    "In the end, we can account for the number of sheets of copper-unitary material batch pairs purchased at supplier C, we can use the following constraints:\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "    p_t \\leq b_{t,\\text{C}}, \\quad \\text{ and } \\quad p_t \\leq y_{t, \\text{C}}.\n",
+    "\\end{equation*}\n",
+    "$$\n",
+    "\n",
+    "Choosing the maximum possible amount $p_t$ that satisfies such constraints will naturally lead to maximizing the number of pairs and, thus, the total discount.\n",
+    "\n",
+    "The remaining constraints such as the inventory balance constraints, total inventory capacity etc.~are straightforward, which gives us the following complete model formulation:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "    \\min \\quad & \\sum_{t \\in T} \\Big( \\Big( \\sum_{s \\in S} \\pi_{s} b_{ts} + \\alpha_{s} y_{ts} \\Big) - \\beta p_t \\Big) +&\n",
+    "    \\sum_{t \\in T} \\Big (  \\gamma_{\\text{copper}} r_t + \\sum_{p \\in P \\setminus \\{ \\text{copper}\\}} \\gamma_{p} s_{pt}  \\Big) \\\\\n",
+    "    \\textup{s.t.} \\quad \n",
+    "    & \\sum_{p \\in P \\setminus \\{ \\text{copper}\\}} x_{pts} \\leq 100 \\cdot b_{ts} & \\forall \\, t \\in T, \\, \\forall \\, s \\in S\\\\\n",
+    "    & s_{\\text{copper}, t} \\leq 10 \\cdot r_t & \\forall \\, t \\in T\\\\\n",
+    "    & s_{\\text{copper}, t} \\leq 10000 & \\forall \\, t \\in T\\\\\n",
+    "    & u_{\\text{copper}, t} = 100 \\sum_{s \\in S} y_{ts} & \\forall \\, t \\in T\\\\\n",
+    "    & p_t \\leq b_{t,\\text{C}}, \\quad p_t \\leq y_{t, \\text{C}} & \\forall \\, t \\in T \\\\\n",
+    "    & u_{pt} = \\sum_{s \\in S} x_{pts} & \\forall \\, t \\in T, \\, \\forall P \\setminus \\{ \\text{copper}\\}\\\\\n",
+    "    & s_{p,t-1} + u_{p,t} = \\delta_{pt} + s_{pt} & \\forall \\, t \\in T, \\, \\forall p \\in P\\\\ \n",
+    "    & s_{p \\textrm{Dec}} \\geq \\Omega_p &  \\forall \\, p \\in P \\\\\n",
+    "    & b_{ts}, r_t \\in \\mathbb{Z}_{+} & \\forall \\, t \\in T, \\, \\forall \\, s \\in S,\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "where we assume that $s_{p,t-1}$ represents the initial inventory state. Note that because $r_t$, $b_{ts}$ appear with a non-negative coefficient and $p_t$ with a positive coefficient in the objective functions, at the optimal solution these variables will automatically take their minimal ($r_t$, $b_{ts}$) and maximal ($p_t$) values allowed by the corresponding constraints."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pyomo implementation\n",
+    "\n",
+    "In the next cell, we implement the model described above in Pyomo using a function that can take the data as input arguments, so we can then easily use it with different data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def BIMproduction_v1(\n",
+    "    demand,\n",
+    "    existing,\n",
+    "    desired,\n",
+    "    stock_limit,\n",
+    "    supplying_copper,\n",
+    "    supplying_batches,\n",
+    "    price_copper_sheet,\n",
+    "    price_batch,\n",
+    "    discounted_price,\n",
+    "    batch_size,\n",
+    "    copper_sheet_mass,\n",
+    "    copper_bucket_size,\n",
+    "    unitary_products,\n",
+    "    unitary_holding_costs,\n",
+    "):\n",
+    "    m = pyo.ConcreteModel(\n",
+    "        \"BIM product acquisition and inventory with sophisticated prices\"\n",
+    "    )\n",
+    "\n",
+    "    periods = demand.columns\n",
+    "    products = demand.index\n",
+    "    first = periods[0]\n",
+    "    prev = {j: i for i, j in zip(periods, periods[1:])}\n",
+    "    last = periods[-1]\n",
+    "\n",
+    "    @m.Param(products, periods)\n",
+    "    def delta(m, p, t):\n",
+    "        return demand.loc[p, t]\n",
+    "\n",
+    "    @m.Param(supplying_batches)\n",
+    "    def pi(m, s):\n",
+    "        return price_batch[s]\n",
+    "\n",
+    "    @m.Param(supplying_copper)\n",
+    "    def kappa(m, s):\n",
+    "        return price_copper_sheet[s]\n",
+    "\n",
+    "    @m.Param()\n",
+    "    def beta(m):\n",
+    "        return price_batch[\"C\"] + price_copper_sheet[\"C\"] - discounted_price\n",
+    "\n",
+    "    @m.Param(products)\n",
+    "    def gamma(m, p):\n",
+    "        return unitary_holding_costs[p]\n",
+    "\n",
+    "    @m.Param(products)\n",
+    "    def Alpha(m, p):\n",
+    "        return existing[p]\n",
+    "\n",
+    "    @m.Param(products)\n",
+    "    def Omega(m, p):\n",
+    "        return desired[p]\n",
+    "\n",
+    "    m.y = pyo.Var(periods, supplying_copper, domain=pyo.NonNegativeIntegers)\n",
+    "    m.x = pyo.Var(\n",
+    "        unitary_products, periods, supplying_batches, domain=pyo.NonNegativeReals\n",
+    "    )\n",
+    "    m.s = pyo.Var(products, periods, domain=pyo.NonNegativeReals)\n",
+    "    m.u = pyo.Var(products, periods, domain=pyo.NonNegativeReals)\n",
+    "    m.b = pyo.Var(periods, supplying_batches, domain=pyo.NonNegativeIntegers)\n",
+    "    m.p = pyo.Var(periods, domain=pyo.NonNegativeIntegers)\n",
+    "    m.r = pyo.Var(periods, domain=pyo.NonNegativeIntegers)\n",
+    "\n",
+    "    @m.Constraint(periods, supplying_batches)\n",
+    "    def units_in_batches(m, t, s):\n",
+    "        return (\n",
+    "            pyo.quicksum(m.x[p, t, s] for p in unitary_products)\n",
+    "            <= batch_size * m.b[t, s]\n",
+    "        )\n",
+    "\n",
+    "    @m.Constraint(periods)\n",
+    "    def copper_in_buckets(m, t):\n",
+    "        return m.s[\"copper\", t] <= copper_bucket_size * m.r[t]\n",
+    "\n",
+    "    @m.Constraint(periods)\n",
+    "    def inventory_capacity(m, t):\n",
+    "        return m.s[\"copper\", t] <= stock_limit\n",
+    "\n",
+    "    @m.Constraint(periods)\n",
+    "    def pairs_in_batches(m, t):\n",
+    "        return m.p[t] <= m.b[t, \"C\"]\n",
+    "\n",
+    "    @m.Constraint(periods)\n",
+    "    def pairs_in_sheets(m, t):\n",
+    "        return m.p[t] <= m.y[t, \"C\"]\n",
+    "\n",
+    "    @m.Constraint(periods, products)\n",
+    "    def bought(m, t, p):\n",
+    "        if p == \"copper\":\n",
+    "            return m.u[p, t] == copper_sheet_mass * pyo.quicksum(\n",
+    "                m.y[t, s] for s in supplying_copper\n",
+    "            )\n",
+    "        else:\n",
+    "            return m.u[p, t] == pyo.quicksum(m.x[p, t, s] for s in supplying_batches)\n",
+    "\n",
+    "    @m.Expression()\n",
+    "    def acquisition_cost(m):\n",
+    "        return pyo.quicksum(\n",
+    "            pyo.quicksum(m.pi[s] * m.b[t, s] for s in supplying_batches)\n",
+    "            + pyo.quicksum(m.kappa[s] * m.y[t, s] for s in supplying_copper)\n",
+    "            - m.beta * m.p[t]\n",
+    "            for t in periods\n",
+    "        )\n",
+    "\n",
+    "    @m.Expression()\n",
+    "    def inventory_cost(m):\n",
+    "        return pyo.quicksum(\n",
+    "            m.gamma[\"copper\"] * m.r[t]\n",
+    "            + pyo.quicksum(m.gamma[p] * m.s[p, t] for p in unitary_products)\n",
+    "            for t in periods\n",
+    "        )\n",
+    "\n",
+    "    @m.Objective(sense=pyo.minimize)\n",
+    "    def total_cost(m):\n",
+    "        return m.acquisition_cost + m.inventory_cost\n",
+    "\n",
+    "    @m.Constraint(products, periods)\n",
+    "    def balance(m, p, t):\n",
+    "        if t == first:\n",
+    "            return m.Alpha[p] + m.u[p, t] == m.delta[p, t] + m.s[p, t]\n",
+    "        else:\n",
+    "            return m.u[p, t] + m.s[p, prev[t]] == m.delta[p, t] + m.s[p, t]\n",
+    "\n",
+    "    @m.Constraint(products)\n",
+    "    def finish(m, p):\n",
+    "        return m.s[p, last] >= m.Omega[p]\n",
+    "\n",
+    "    SOLVER.solve(m)\n",
+    "\n",
+    "    return m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then solve the model using the provided data. We introduce three auxiliary function to more quickly parse the output of the model and use them to report the solution as a set of Pandas dataframes and visualize the monthly stock levels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]\n",
+      "Copyright (c) 2023 HiGHS under MIT licence terms\n",
+      "Optimal cost is 110216 cents, that is $1102.16\n",
+      "\n",
+      "Monthly purchased material\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Jan</th>\n",
+       "      <th>Feb</th>\n",
+       "      <th>Mar</th>\n",
+       "      <th>Apr</th>\n",
+       "      <th>May</th>\n",
+       "      <th>Jun</th>\n",
+       "      <th>Jul</th>\n",
+       "      <th>Aug</th>\n",
+       "      <th>Sep</th>\n",
+       "      <th>Oct</th>\n",
+       "      <th>Nov</th>\n",
+       "      <th>Dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>silicon</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>228</td>\n",
+       "      <td>233</td>\n",
+       "      <td>274</td>\n",
+       "      <td>228</td>\n",
+       "      <td>349</td>\n",
+       "      <td>257</td>\n",
+       "      <td>690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plastic</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>266</td>\n",
+       "      <td>326</td>\n",
+       "      <td>371</td>\n",
+       "      <td>351</td>\n",
+       "      <td>343</td>\n",
+       "      <td>1310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>copper</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1100</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>3100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>germanium</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov  \\\n",
+       "silicon       0     0     0     0     0   228   233   274   228   349   257   \n",
+       "plastic       0     0     0     0     0     0   266   326   371   351   343   \n",
+       "copper        0     0     0     0     0  1000  1200  1100  1300  1300  1200   \n",
+       "germanium     0     0     0     0     0     0     0     0     0     0     0   \n",
+       "\n",
+       "            Dec  \n",
+       "silicon     690  \n",
+       "plastic    1310  \n",
+       "copper     3100  \n",
+       "germanium     0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Acquisition plan for copper sheet and batch pairs from supplier C\n"
+     ]
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+       "    Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec\n",
+       "0     0     0     0     0     0     3     5     6     6     7     6    20"
+      ]
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+      "Acquisition plan for copper sheets materials\n"
+     ]
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+       "    Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec\n",
+       "B     0     0     0     0     0     7     7     4     7     6     6    11\n",
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+      "Acquisition plan for unitary materials (batches)\n"
+     ]
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+       "    Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec\n",
+       "A     0     0     0     0     0     0     0     0     0     0     0     0\n",
+       "C     0     0     0     0     0     3     5     6     6     7     6    20"
+      ]
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+     "text": [
+      "Acquisition plan for unitary materials (quantities)\n"
+     ]
+    },
+    {
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+       "      <th>plastic</th>\n",
+       "      <td>0</td>\n",
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+       "      <th rowspan=\"3\" valign=\"top\">C</th>\n",
+       "      <th>silicon</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>228</td>\n",
+       "      <td>233</td>\n",
+       "      <td>274</td>\n",
+       "      <td>228</td>\n",
+       "      <td>349</td>\n",
+       "      <td>257</td>\n",
+       "      <td>690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>germanium</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>plastic</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>266</td>\n",
+       "      <td>326</td>\n",
+       "      <td>371</td>\n",
+       "      <td>351</td>\n",
+       "      <td>343</td>\n",
+       "      <td>1310</td>\n",
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+      ],
+      "text/plain": [
+       "                     Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep  \\\n",
+       "supplier materials                                                         \n",
+       "A        silicon       0     0     0     0     0     0     0     0     0   \n",
+       "         germanium     0     0     0     0     0     0     0     0     0   \n",
+       "         plastic       0     0     0     0     0     0     0     0     0   \n",
+       "C        silicon       0     0     0     0     0   228   233   274   228   \n",
+       "         germanium     0     0     0     0     0     0     0     0     0   \n",
+       "         plastic       0     0     0     0     0     0   266   326   371   \n",
+       "\n",
+       "                     Oct   Nov   Dec  \n",
+       "supplier materials                    \n",
+       "A        silicon       0     0     0  \n",
+       "         germanium     0     0     0  \n",
+       "         plastic       0     0     0  \n",
+       "C        silicon     349   257   690  \n",
+       "         germanium     0     0     0  \n",
+       "         plastic     351   343  1310  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The stock levels at the end of each month resulting from the optimal acquisition and production plan\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>Jan</th>\n",
+       "      <th>Feb</th>\n",
+       "      <th>Mar</th>\n",
+       "      <th>Apr</th>\n",
+       "      <th>May</th>\n",
+       "      <th>Jun</th>\n",
+       "      <th>Jul</th>\n",
+       "      <th>Aug</th>\n",
+       "      <th>Sep</th>\n",
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+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>silicon</th>\n",
+       "      <td>912</td>\n",
+       "      <td>787</td>\n",
+       "      <td>527</td>\n",
+       "      <td>310</td>\n",
+       "      <td>72</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>54</td>\n",
+       "      <td>500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plastic</th>\n",
+       "      <td>1615</td>\n",
+       "      <td>1428</td>\n",
+       "      <td>1087</td>\n",
+       "      <td>805</td>\n",
+       "      <td>472</td>\n",
+       "      <td>68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>copper</th>\n",
+       "      <td>4354</td>\n",
+       "      <td>3730</td>\n",
+       "      <td>2528</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>388</td>\n",
+       "      <td>8</td>\n",
+       "      <td>44</td>\n",
+       "      <td>14</td>\n",
+       "      <td>90</td>\n",
+       "      <td>54</td>\n",
+       "      <td>50</td>\n",
+       "      <td>2042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>germanium</th>\n",
+       "      <td>1453</td>\n",
+       "      <td>1391</td>\n",
+       "      <td>1310</td>\n",
+       "      <td>1245</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>1032</td>\n",
+       "      <td>946</td>\n",
+       "      <td>857</td>\n",
+       "      <td>775</td>\n",
+       "      <td>693</td>\n",
+       "      <td>609</td>\n",
+       "      <td>543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov  \\\n",
+       "silicon     912   787   527   310    72    14     0    36     0    56    54   \n",
+       "plastic    1615  1428  1087   805   472    68     0     0    24     0     0   \n",
+       "copper     4354  3730  2528  1530   388     8    44    14    90    54    50   \n",
+       "germanium  1453  1391  1310  1245  1150  1032   946   857   775   693   609   \n",
+       "\n",
+       "            Dec  \n",
+       "silicon     500  \n",
+       "plastic    1000  \n",
+       "copper     2042  \n",
+       "germanium   543  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def Table1d(m, J, retriever):\n",
+    "    return pd.DataFrame([int(0 + retriever(m, j)) for j in J], index=J).T\n",
+    "\n",
+    "\n",
+    "def Table2d(m, I, J, retriever):\n",
+    "    return pd.DataFrame.from_records(\n",
+    "        [[int(0 + retriever(m, i, j)) for j in J] for i in I], index=I, columns=J\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def Table3d(m, I, J, names, K, retriever):\n",
+    "    index = pd.MultiIndex.from_product([I, J], names=names)\n",
+    "    return pd.DataFrame.from_records(\n",
+    "        [[int(0 + retriever(m, i, j, k)) for k in K] for i in I for j in J],\n",
+    "        index=index,\n",
+    "        columns=K,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "m1 = BIMproduction_v1(\n",
+    "    demand=demand,\n",
+    "    existing={\"silicon\": 1000, \"germanium\": 1500, \"plastic\": 1750, \"copper\": 4800},\n",
+    "    desired={\"silicon\": 500, \"germanium\": 500, \"plastic\": 1000, \"copper\": 2000},\n",
+    "    stock_limit=10000,\n",
+    "    supplying_copper=[\"B\", \"C\"],\n",
+    "    supplying_batches=[\"A\", \"C\"],\n",
+    "    price_copper_sheet={\"B\": 300, \"C\": 400},\n",
+    "    price_batch={\"A\": 500, \"C\": 600},\n",
+    "    discounted_price=700,\n",
+    "    batch_size=100,\n",
+    "    copper_sheet_mass=100,\n",
+    "    copper_bucket_size=10,\n",
+    "    unitary_products=[\"silicon\", \"germanium\", \"plastic\"],\n",
+    "    unitary_holding_costs={\"copper\": 10, \"silicon\": 2, \"germanium\": 2, \"plastic\": 2},\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"Optimal cost is {pyo.value(m1.total_cost):.0f} cents, that is ${pyo.value(m1.total_cost) / 100:.2f}\\n\"\n",
+    ")\n",
+    "\n",
+    "print(\"Monthly purchased material\")\n",
+    "display(Table2d(m1, demand.index, demand.columns, lambda m, i, j: pyo.value(m.u[i, j])))\n",
+    "\n",
+    "print(\"Acquisition plan for copper sheet and batch pairs from supplier C\")\n",
+    "display(Table1d(m1, J=demand.columns, retriever=lambda m, j: pyo.value(m.p[j])))\n",
+    "\n",
+    "print(\"Acquisition plan for copper sheets materials\")\n",
+    "display(Table2d(m1, [\"B\", \"C\"], demand.columns, lambda m, i, j: pyo.value(m.y[j, i])))\n",
+    "\n",
+    "print(\"Acquisition plan for unitary materials (batches)\")\n",
+    "display(Table2d(m1, [\"A\", \"C\"], demand.columns, lambda m, i, j: pyo.value(m.b[j, i])))\n",
+    "\n",
+    "print(\"Acquisition plan for unitary materials (quantities)\")\n",
+    "display(\n",
+    "    Table3d(\n",
+    "        m1,\n",
+    "        I=[\"A\", \"C\"],\n",
+    "        J=[\"silicon\", \"germanium\", \"plastic\"],\n",
+    "        names=[\"supplier\", \"materials\"],\n",
+    "        K=demand.columns,\n",
+    "        retriever=lambda m, i, j, k: 0 + pyo.value(m.x[j, k, i]),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"The stock levels at the end of each month resulting from the optimal acquisition and production plan\"\n",
+    ")\n",
+    "stock = Table2d(m1, demand.index, demand.columns, lambda m, i, j: pyo.value(m.s[i, j]))\n",
+    "display(stock)\n",
+    "stock.T.plot(drawstyle=\"steps-mid\", grid=True, figsize=(13, 4))\n",
+    "plt.xticks(np.arange(len(stock.columns)), stock.columns)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Alternative Pyomo implementation using blocks\n",
+    "\n",
+    "In the next cell, we implement a second version of the same model, which is equivalent to the previous one, but uses Pyomo sets and block components to iterate over all the time indices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def BIMproduction_v2(\n",
+    "    demand,\n",
+    "    existing,\n",
+    "    desired,\n",
+    "    stock_limit,\n",
+    "    supplying_copper,\n",
+    "    supplying_batches,\n",
+    "    price_copper_sheet,\n",
+    "    price_batch,\n",
+    "    discounted_price,\n",
+    "    batch_size,\n",
+    "    copper_sheet_mass,\n",
+    "    copper_bucket_size,\n",
+    "    unitary_products,\n",
+    "    unitary_holding_costs,\n",
+    "):\n",
+    "    m = pyo.ConcreteModel(\n",
+    "        \"BIM product acquisition and inventory with sophisticated prices (using blocks)\"\n",
+    "    )\n",
+    "\n",
+    "    periods = demand.columns\n",
+    "    products = demand.index\n",
+    "    first = periods[0]\n",
+    "    prev = {j: i for i, j in zip(periods, periods[1:])}\n",
+    "    last = periods[-1]\n",
+    "\n",
+    "    m.T = pyo.Set(initialize=periods)\n",
+    "    m.P = pyo.Set(initialize=products)\n",
+    "\n",
+    "    m.PT = m.P * m.T  # to avoid internal set bloat\n",
+    "\n",
+    "    @m.Block(m.T)\n",
+    "    def A(b):\n",
+    "        b.x = pyo.Var(supplying_batches, products, domain=pyo.NonNegativeReals)\n",
+    "        b.b = pyo.Var(supplying_batches, domain=pyo.NonNegativeIntegers)\n",
+    "        b.y = pyo.Var(supplying_copper, domain=pyo.NonNegativeIntegers)\n",
+    "        b.p = pyo.Var(domain=pyo.NonNegativeIntegers)\n",
+    "\n",
+    "        @b.Constraint(supplying_batches)\n",
+    "        def in_batches(b, s):\n",
+    "            return pyo.quicksum(b.x[s, p] for p in products) <= batch_size * b.b[s]\n",
+    "\n",
+    "        @b.Constraint()\n",
+    "        def pairs_in_batches(b):\n",
+    "            return b.p <= b.b[\"C\"]\n",
+    "\n",
+    "        @b.Constraint()\n",
+    "        def pairs_in_sheets(b):\n",
+    "            return b.p <= b.y[\"C\"]\n",
+    "\n",
+    "        @b.Expression(products)\n",
+    "        def u(b, p):\n",
+    "            if p == \"copper\":\n",
+    "                return copper_sheet_mass * pyo.quicksum(\n",
+    "                    b.y[s] for s in supplying_copper\n",
+    "                )\n",
+    "            return pyo.quicksum(b.x[s, p] for s in supplying_batches)\n",
+    "\n",
+    "        @b.Expression()\n",
+    "        def cost(b):\n",
+    "            discount = price_batch[\"C\"] + price_copper_sheet[\"C\"] - discounted_price\n",
+    "            return (\n",
+    "                pyo.quicksum(price_copper_sheet[s] * b.y[s] for s in supplying_copper)\n",
+    "                + pyo.quicksum(price_batch[s] * b.b[s] for s in supplying_batches)\n",
+    "                - discount * b.p\n",
+    "            )\n",
+    "\n",
+    "    @m.Block(m.T)\n",
+    "    def I(b):\n",
+    "        b.s = pyo.Var(products, domain=pyo.NonNegativeReals)\n",
+    "        b.r = pyo.Var(domain=pyo.NonNegativeIntegers)\n",
+    "\n",
+    "        @b.Constraint()\n",
+    "        def copper_in_buckets(b):\n",
+    "            return b.s[\"copper\"] <= copper_bucket_size * b.r\n",
+    "\n",
+    "        @b.Constraint()\n",
+    "        def capacity(b):\n",
+    "            return b.s[\"copper\"] <= stock_limit\n",
+    "\n",
+    "        @b.Expression()\n",
+    "        def cost(b):\n",
+    "            return unitary_holding_costs[\"copper\"] * b.r + pyo.quicksum(\n",
+    "                unitary_holding_costs[p] * b.s[p] for p in unitary_products\n",
+    "            )\n",
+    "\n",
+    "    @m.Param(m.PT)\n",
+    "    def delta(m, t, p):\n",
+    "        return demand.loc[t, p]\n",
+    "\n",
+    "    @m.Expression()\n",
+    "    def acquisition_cost(m):\n",
+    "        return pyo.quicksum(m.A[t].cost for t in m.T)\n",
+    "\n",
+    "    @m.Expression()\n",
+    "    def inventory_cost(m):\n",
+    "        return pyo.quicksum(m.I[t].cost for t in m.T)\n",
+    "\n",
+    "    @m.Objective(sense=pyo.minimize)\n",
+    "    def total_cost(m):\n",
+    "        return m.acquisition_cost + m.inventory_cost\n",
+    "\n",
+    "    @m.Constraint(m.PT)\n",
+    "    def balance(m, p, t):\n",
+    "        if t == first:\n",
+    "            return existing[p] + m.A[t].u[p] == m.delta[p, t] + m.I[t].s[p]\n",
+    "        else:\n",
+    "            return m.A[t].u[p] + m.I[prev[t]].s[p] == m.delta[p, t] + m.I[t].s[p]\n",
+    "\n",
+    "    @m.Constraint(m.P)\n",
+    "    def finish(m, p):\n",
+    "        return m.I[last].s[p] >= desired[p]\n",
+    "\n",
+    "    SOLVER.solve(m)\n",
+    "\n",
+    "    return m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then pass the parameters and data to this Pyomo model and solve it, as illustrated in the following code cell. We report the solution as a set of Pandas dataframes and visualize the monthly stock levels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]\n",
+      "Copyright (c) 2023 HiGHS under MIT licence terms\n",
+      "Optimal cost is 110216 cents, that is $1102.16\n",
+      "\n",
+      "The stock levels at the end of each month resulting from the optimal acquisition and production plan\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Jan</th>\n",
+       "      <th>Feb</th>\n",
+       "      <th>Mar</th>\n",
+       "      <th>Apr</th>\n",
+       "      <th>May</th>\n",
+       "      <th>Jun</th>\n",
+       "      <th>Jul</th>\n",
+       "      <th>Aug</th>\n",
+       "      <th>Sep</th>\n",
+       "      <th>Oct</th>\n",
+       "      <th>Nov</th>\n",
+       "      <th>Dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>silicon</th>\n",
+       "      <td>912</td>\n",
+       "      <td>787</td>\n",
+       "      <td>527</td>\n",
+       "      <td>310</td>\n",
+       "      <td>72</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>54</td>\n",
+       "      <td>500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plastic</th>\n",
+       "      <td>1615</td>\n",
+       "      <td>1427</td>\n",
+       "      <td>1086</td>\n",
+       "      <td>804</td>\n",
+       "      <td>471</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>copper</th>\n",
+       "      <td>4354</td>\n",
+       "      <td>3730</td>\n",
+       "      <td>2528</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>388</td>\n",
+       "      <td>8</td>\n",
+       "      <td>44</td>\n",
+       "      <td>14</td>\n",
+       "      <td>90</td>\n",
+       "      <td>54</td>\n",
+       "      <td>50</td>\n",
+       "      <td>2042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>germanium</th>\n",
+       "      <td>1453</td>\n",
+       "      <td>1391</td>\n",
+       "      <td>1310</td>\n",
+       "      <td>1245</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>1032</td>\n",
+       "      <td>946</td>\n",
+       "      <td>857</td>\n",
+       "      <td>775</td>\n",
+       "      <td>693</td>\n",
+       "      <td>609</td>\n",
+       "      <td>543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov  \\\n",
+       "silicon     912   787   527   310    72    15     0    35    24     0    54   \n",
+       "plastic    1615  1427  1086   804   471    67     0     0     0    56     0   \n",
+       "copper     4354  3730  2528  1530   388     8    44    14    90    54    50   \n",
+       "germanium  1453  1391  1310  1245  1150  1032   946   857   775   693   609   \n",
+       "\n",
+       "            Dec  \n",
+       "silicon     500  \n",
+       "plastic    1000  \n",
+       "copper     2042  \n",
+       "germanium   543  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m2 = BIMproduction_v2(\n",
+    "    demand=demand,\n",
+    "    existing={\"silicon\": 1000, \"germanium\": 1500, \"plastic\": 1750, \"copper\": 4800},\n",
+    "    desired={\"silicon\": 500, \"germanium\": 500, \"plastic\": 1000, \"copper\": 2000},\n",
+    "    stock_limit=10000,\n",
+    "    supplying_copper=[\"B\", \"C\"],\n",
+    "    supplying_batches=[\"A\", \"C\"],\n",
+    "    price_copper_sheet={\"B\": 300, \"C\": 400},\n",
+    "    price_batch={\"A\": 500, \"C\": 600},\n",
+    "    discounted_price=700,\n",
+    "    batch_size=100,\n",
+    "    copper_sheet_mass=100,\n",
+    "    copper_bucket_size=10,\n",
+    "    unitary_products=[\"silicon\", \"germanium\", \"plastic\"],\n",
+    "    unitary_holding_costs={\"copper\": 10, \"silicon\": 2, \"germanium\": 2, \"plastic\": 2},\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"Optimal cost is {pyo.value(m2.total_cost):.0f} cents, that is ${pyo.value(m2.total_cost) / 100:.2f}\\n\"\n",
+    ")\n",
+    "\n",
+    "Table3d(\n",
+    "    m2,\n",
+    "    I=[\"A\", \"C\"],\n",
+    "    J=[\"silicon\", \"germanium\", \"plastic\"],\n",
+    "    names=[\"supplier\", \"materials\"],\n",
+    "    K=m2.T,\n",
+    "    retriever=lambda m, i, j, k: 0 + pyo.value(m.A[k].x[i, j]),\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"The stock levels at the end of each month resulting from the optimal acquisition and production plan\"\n",
+    ")\n",
+    "stock = Table2d(m2, I=m2.P, J=m2.T, retriever=lambda m, i, j: pyo.value(m.I[j].s[i]))\n",
+    "display(stock)\n",
+    "stock.T.plot(drawstyle=\"steps-mid\", grid=True, figsize=(13, 4))\n",
+    "plt.xticks(np.arange(len(stock.columns)), stock.columns)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/notebooks/04/dinner-seat-allocation.ipynb b/_sources/notebooks/04/01-dinner-seat-allocation.ipynb
similarity index 99%
rename from _sources/notebooks/04/dinner-seat-allocation.ipynb
rename to _sources/notebooks/04/01-dinner-seat-allocation.ipynb
index bebdf7ff..7df0d0a6 100644
--- a/_sources/notebooks/04/dinner-seat-allocation.ipynb
+++ b/_sources/notebooks/04/01-dinner-seat-allocation.ipynb
@@ -23,7 +23,7 @@
     "```{index} feasibility problem\n",
     "```\n",
     "\n",
-    "# Dinner seating arrangement"
+    "# 4.1 Dinner seating arrangement"
    ]
   },
   {
diff --git a/_sources/notebooks/04/mincost-flow.ipynb b/_sources/notebooks/04/02-mincost-flow.ipynb
similarity index 99%
rename from _sources/notebooks/04/mincost-flow.ipynb
rename to _sources/notebooks/04/02-mincost-flow.ipynb
index 4d23254e..4c04e0c9 100644
--- a/_sources/notebooks/04/mincost-flow.ipynb
+++ b/_sources/notebooks/04/02-mincost-flow.ipynb
@@ -17,7 +17,7 @@
     "```{index} networkx\n",
     "```\n",
     "\n",
-    "# Minimum-Cost Flow Problem"
+    "# 4.2 Minimum-Cost Flow Problem"
    ]
   },
   {
diff --git a/_sources/notebooks/04/gasoline-distribution.ipynb b/_sources/notebooks/04/03-gasoline-distribution.ipynb
similarity index 99%
rename from _sources/notebooks/04/gasoline-distribution.ipynb
rename to _sources/notebooks/04/03-gasoline-distribution.ipynb
index 539f54b1..6500cc43 100644
--- a/_sources/notebooks/04/gasoline-distribution.ipynb
+++ b/_sources/notebooks/04/03-gasoline-distribution.ipynb
@@ -17,7 +17,7 @@
     "```{index} single: Pyomo; display\n",
     "```\n",
     "\n",
-    "# Gasoline distribution\n",
+    "# 4.3 Gasoline distribution\n",
     "   \n",
     "This notebook presents a transportation model to optimally allocate the delivery of a commodity from multiple sources to multiple destinations. The model invites a discussion of the pitfalls in optimizing a global objective for customers who may have an uneven share of the resulting benefits, then through model refinement arrives at a group cost-sharing plan to delivery costs."
    ]
diff --git a/_sources/notebooks/04/graph-coloring.ipynb b/_sources/notebooks/04/04-exam-room-scheduling.ipynb
similarity index 99%
rename from _sources/notebooks/04/graph-coloring.ipynb
rename to _sources/notebooks/04/04-exam-room-scheduling.ipynb
index 6496259a..759d9628 100644
--- a/_sources/notebooks/04/graph-coloring.ipynb
+++ b/_sources/notebooks/04/04-exam-room-scheduling.ipynb
@@ -17,7 +17,7 @@
     "```{index} networkx\n",
     "```\n",
     "\n",
-    "# Exam room scheduling"
+    "# 4.4 Exam room scheduling"
    ]
   },
   {
diff --git a/_sources/notebooks/04/04.00.md b/_sources/notebooks/04/04.00.md
index d6e491b5..7e4e32b2 100644
--- a/_sources/notebooks/04/04.00.md
+++ b/_sources/notebooks/04/04.00.md
@@ -4,12 +4,12 @@ In the previous chapters, we dealt with general problems by first formulating al
 
 This chapter includes several examples with companion Pyomo implementation that explore various modeling and implementation aspects of network optimization:
 
-* [A dinner seating arrangement problem](dinner-seat-allocation.ipynb)
-* [A transportation problem](mincost-flow.ipynb), using the minimum-cost flow formulation.
-* [A franchise gasoline distribution problem](gasoline-distribution.ipynb)
-* [A scheduling problem](graph-coloring.ipynb) formulated and solved as a graph coloring problem.
-* [A cryptocurrency arbitrage problem](cryptocurrency-arbitrage.ipynb)
-* [Extra material: Energy dispatch problem](power-network.ipynb)
-* [Extra material: Forex arbitrage](forex-arbitrage.ipynb)
+* [A dinner seating arrangement problem](01-dinner-seat-allocation.ipynb)
+* [A transportation problem](02-mincost-flow.ipynb), using the minimum-cost flow formulation.
+* [A franchise gasoline distribution problem](03-gasoline-distribution.ipynb)
+* [A scheduling problem](04-exam-room-scheduling.ipynb) formulated and solved as a graph coloring problem.
+* [A cryptocurrency arbitrage problem](05-cryptocurrency-arbitrage.ipynb)
+* [Extra material: Energy dispatch problem](06-power-network.ipynb)
+* [Extra material: Forex arbitrage](07-forex-arbitrage.ipynb)
 
 Go to the [next chapter](../05/05.00.md) about convex optimization.
\ No newline at end of file
diff --git a/_sources/notebooks/04/cryptocurrency-arbitrage.ipynb b/_sources/notebooks/04/05-cryptocurrency-arbitrage.ipynb
similarity index 99%
rename from _sources/notebooks/04/cryptocurrency-arbitrage.ipynb
rename to _sources/notebooks/04/05-cryptocurrency-arbitrage.ipynb
index c7bf30d6..1bb4c582 100644
--- a/_sources/notebooks/04/cryptocurrency-arbitrage.ipynb
+++ b/_sources/notebooks/04/05-cryptocurrency-arbitrage.ipynb
@@ -19,7 +19,7 @@
     "```{index} network optimization\n",
     "```\n",
     "\n",
-    "# Cryptocurrency arbitrage search"
+    "# 4.5 Cryptocurrency arbitrage search"
    ]
   },
   {
diff --git a/_sources/notebooks/04/power-network.ipynb b/_sources/notebooks/04/06-power-network.ipynb
similarity index 100%
rename from _sources/notebooks/04/power-network.ipynb
rename to _sources/notebooks/04/06-power-network.ipynb
diff --git a/_sources/notebooks/04/forex-arbitrage.ipynb b/_sources/notebooks/04/07-forex-arbitrage.ipynb
similarity index 100%
rename from _sources/notebooks/04/forex-arbitrage.ipynb
rename to _sources/notebooks/04/07-forex-arbitrage.ipynb
diff --git a/_sources/notebooks/05/milk-pooling.ipynb b/_sources/notebooks/05/01-milk-pooling.ipynb
similarity index 99%
rename from _sources/notebooks/05/milk-pooling.ipynb
rename to _sources/notebooks/05/01-milk-pooling.ipynb
index cc630809..62095ddc 100644
--- a/_sources/notebooks/05/milk-pooling.ipynb
+++ b/_sources/notebooks/05/01-milk-pooling.ipynb
@@ -19,7 +19,7 @@
     "```\n",
     "```{index} single: solver; ipopt\n",
     "```\n",
-    "# Milk pooling and blending\n",
+    "# 5.1 Milk pooling and blending\n",
     "\n",
     "Pooling and blending operations involve the \"pooling\" of various streams to create intermediate mixtures that are subsequently blended with other streams to meet final product specifications. These operations are common to the chemical processing and petroleum sectors where limited tankage may be available, or when it is necessary to transport materials by train, truck, or pipeline to remote blending terminals. Similar applications arise in agriculture, food, mining, wastewater treatment, and other industries.\n",
     "\n",
diff --git a/_sources/notebooks/05/ols-regression.ipynb b/_sources/notebooks/05/02-ols-regression.ipynb
similarity index 99%
rename from _sources/notebooks/05/ols-regression.ipynb
rename to _sources/notebooks/05/02-ols-regression.ipynb
index 3e2cdc11..a5900861 100644
--- a/_sources/notebooks/05/ols-regression.ipynb
+++ b/_sources/notebooks/05/02-ols-regression.ipynb
@@ -11,9 +11,9 @@
     "```{index} scatterplot\n",
     "```\n",
     "\n",
-    "# Ordinary Least Squares (OLS) Regression\n",
+    "# 5.2 Ordinary Least Squares (OLS) Regression\n",
     "\n",
-    "In Chapter 2 we introduced linear regression with least absolute deviations (LAD), see [this notebook](../02/lad-regression.ipynb). Here we consider the same problem setting, but slightly change the underlying optimization problem, in particular its objective function, obtaining the classical ordinary least squares (OLS) regression."
+    "In Chapter 2 we introduced linear regression with least absolute deviations (LAD), see [this notebook](../02/02-lad-regression.ipynb). Here we consider the same problem setting, but slightly change the underlying optimization problem, in particular its objective function, obtaining the classical ordinary least squares (OLS) regression."
    ]
   },
   {
diff --git a/_sources/notebooks/05/markowitz_portfolio.ipynb b/_sources/notebooks/05/03-markowitz-portfolio.ipynb
similarity index 99%
rename from _sources/notebooks/05/markowitz_portfolio.ipynb
rename to _sources/notebooks/05/03-markowitz-portfolio.ipynb
index e772fd79..6f2b6349 100644
--- a/_sources/notebooks/05/markowitz_portfolio.ipynb
+++ b/_sources/notebooks/05/03-markowitz-portfolio.ipynb
@@ -14,7 +14,7 @@
     "```{index} single: solver; cplex\n",
     "```\n",
     "\n",
-    "# Markowitz portfolio optimization"
+    "# 5.3 Markowitz portfolio optimization"
    ]
   },
   {
diff --git a/_sources/notebooks/05/svm.ipynb b/_sources/notebooks/05/04-svm-binary-classification.ipynb
similarity index 99%
rename from _sources/notebooks/05/svm.ipynb
rename to _sources/notebooks/05/04-svm-binary-classification.ipynb
index 2504fc5f..4cac9604 100644
--- a/_sources/notebooks/05/svm.ipynb
+++ b/_sources/notebooks/05/04-svm-binary-classification.ipynb
@@ -15,7 +15,7 @@
     "```\n",
     "```{index} single: application; counterfeit banknotes\n",
     "```\n",
-    "# Support Vector Machines for Binary Classification\n",
+    "# 5.4 Support Vector Machines for Binary Classification\n",
     "\n",
     "Support Vector Machines (SVM) are a type of supervised machine learning model. Similar to other machine learning techniques based on regression, training an SVM classifier uses examples with known outcomes, and involves optimization some measure of performance. The resulting classifier can then be applied to classify data with unknown outcomes.\n",
     "\n",
diff --git a/_sources/notebooks/05/refinery-production.ipynb b/_sources/notebooks/05/05-refinery-production.ipynb
similarity index 100%
rename from _sources/notebooks/05/refinery-production.ipynb
rename to _sources/notebooks/05/05-refinery-production.ipynb
diff --git a/_sources/notebooks/05/05.00.md b/_sources/notebooks/05/05.00.md
index ab96d63e..d78bdb1c 100644
--- a/_sources/notebooks/05/05.00.md
+++ b/_sources/notebooks/05/05.00.md
@@ -6,11 +6,11 @@ Luckily, it will turn out that the real separation between simple and difficult
 
 This chapter includes several examples with companion Pyomo implementation that explore various modeling and implementation aspects of convex optimization:
 
-* [Milk pooling and blending](milk-pooling.ipynb)
-* [Ordinary Least Squares (OLS) regression](ols-regression.ipynb)
-* [Markowitz portfolio optimization problem](markowitz_portfolio.ipynb)
-* [Support Vector Machines for binary classification](svm.ipynb)
-* [Extra material: Refinery production](refinery-production.ipynb)
-* [Extra material: Cutting stock](cutting-stock.ipynb)
+* [Milk pooling and blending](01-milk-pooling.ipynb)
+* [Ordinary Least Squares (OLS) regression](02-ols-regression.ipynb)
+* [Markowitz portfolio optimization problem](03-markowitz-portfolio.ipynb)
+* [Support Vector Machines for binary classification](04-svm-binary-classification.ipynb)
+* [Extra material: Refinery production](05-refinery-production.ipynb)
+* [Extra material: Cutting stock](06-cutting-stock.ipynb)
 
 Go to the [next chapter](../06/06.00.md) about conic optimization.
\ No newline at end of file
diff --git a/_sources/notebooks/05/cutting-stock.ipynb b/_sources/notebooks/05/06-cutting-stock.ipynb
similarity index 100%
rename from _sources/notebooks/05/cutting-stock.ipynb
rename to _sources/notebooks/05/06-cutting-stock.ipynb
diff --git a/_sources/notebooks/06/economic-order-quantity.ipynb b/_sources/notebooks/06/01-economic-order-quantity.ipynb
similarity index 100%
rename from _sources/notebooks/06/economic-order-quantity.ipynb
rename to _sources/notebooks/06/01-economic-order-quantity.ipynb
diff --git a/_sources/notebooks/06/kelly-criterion.ipynb b/_sources/notebooks/06/02-kelly-criterion.ipynb
similarity index 100%
rename from _sources/notebooks/06/kelly-criterion.ipynb
rename to _sources/notebooks/06/02-kelly-criterion.ipynb
diff --git a/_sources/notebooks/06/markowitz_portfolio_revisited.ipynb b/_sources/notebooks/06/03-markowitz-portfolio-revisited.ipynb
similarity index 100%
rename from _sources/notebooks/06/markowitz_portfolio_revisited.ipynb
rename to _sources/notebooks/06/03-markowitz-portfolio-revisited.ipynb
diff --git a/_sources/notebooks/06/building-insulation.ipynb b/_sources/notebooks/06/04-building-insulation.ipynb
similarity index 100%
rename from _sources/notebooks/06/building-insulation.ipynb
rename to _sources/notebooks/06/04-building-insulation.ipynb
diff --git a/_sources/notebooks/06/svm-conic.ipynb b/_sources/notebooks/06/05-svm-conic.ipynb
similarity index 99%
rename from _sources/notebooks/06/svm-conic.ipynb
rename to _sources/notebooks/06/05-svm-conic.ipynb
index cd22f02c..6ff94fa1 100644
--- a/_sources/notebooks/06/svm-conic.ipynb
+++ b/_sources/notebooks/06/05-svm-conic.ipynb
@@ -19,7 +19,7 @@
     "```\n",
     "```{index} single: application; counterfeit banknotes\n",
     "```\n",
-    "# Training Support Vector Machines with Conic Optimization\n"
+    "# Support Vector Machines with Conic Optimization\n"
    ]
   },
   {
diff --git a/_sources/notebooks/06/investment-wheel.ipynb b/_sources/notebooks/06/06-investment-wheel.ipynb
similarity index 100%
rename from _sources/notebooks/06/investment-wheel.ipynb
rename to _sources/notebooks/06/06-investment-wheel.ipynb
diff --git a/_sources/notebooks/06/06.00.md b/_sources/notebooks/06/06.00.md
index dc5c0921..bd60ff51 100644
--- a/_sources/notebooks/06/06.00.md
+++ b/_sources/notebooks/06/06.00.md
@@ -2,12 +2,12 @@
 
 In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of conic problems:
 
-* [Economic order quantity](economic-order-quantity.ipynb)
-* [The Kelly Criterion](kelly-criterion.ipynb)
-* [Markowitz portfolio optimization problem revisited](markowitz_portfolio_revisited.ipynb)
-* [Optimal design of multi-layered building insulation](building-insulation.ipynb)
-* [Training Support Vector Machines with Conic Programming](svm-conic.ipynb)
-* [Extra material: Luenberger's Investment Wheel](investment-wheel.ipynb)
-* [Extra material: Optimal Growth Portfolio](optimal-growth-portfolios.ipynb)
+* [Economic order quantity](01-economic-order-quantity.ipynb)
+* [The Kelly criterion](02-kelly-criterion.ipynb)
+* [Markowitz portfolio optimization problem revisited](03-markowitz-portfolio-revisited.ipynb)
+* [Optimal design of multi-layered building insulation](04-building-insulation.ipynb)
+* [Support Vector Machines with conic optimization](05-svm-conic.ipynb)
+* [Extra material: Luenberger's investment wheel](06-investment-wheel.ipynb)
+* [Extra material: Optimal growth portfolio](07-optimal-growth-portfolios.ipynb)
 
 Go to the [next chapter](../07/07.00.md) about what happens when optimization meets reality and needs to account for uncertainty.
\ No newline at end of file
diff --git a/_sources/notebooks/06/optimal-growth-portfolios.ipynb b/_sources/notebooks/06/07-optimal-growth-portfolios.ipynb
similarity index 100%
rename from _sources/notebooks/06/optimal-growth-portfolios.ipynb
rename to _sources/notebooks/06/07-optimal-growth-portfolios.ipynb
diff --git a/_sources/notebooks/07/fleet-assignment.ipynb b/_sources/notebooks/07/01-fleet-assignment.ipynb
similarity index 100%
rename from _sources/notebooks/07/fleet-assignment.ipynb
rename to _sources/notebooks/07/01-fleet-assignment.ipynb
diff --git a/_sources/notebooks/07/bim-robustness-analysis.ipynb b/_sources/notebooks/07/02-bim-robustness-analysis.ipynb
similarity index 100%
rename from _sources/notebooks/07/bim-robustness-analysis.ipynb
rename to _sources/notebooks/07/02-bim-robustness-analysis.ipynb
diff --git a/_sources/notebooks/07/07.00.md b/_sources/notebooks/07/07.00.md
index 18211ca6..28ea3dc9 100644
--- a/_sources/notebooks/07/07.00.md
+++ b/_sources/notebooks/07/07.00.md
@@ -22,7 +22,7 @@ Since there is so much uncertainty, natural questions are:
 
 We will show three examples of how one can inspect solutions for their sensitivity to data changes.
 
-* [Fleet assignment problem](fleet-assignment.ipynb)
-* [Robustness analysis of BIM production plan via simulations](bim-robustness-analysis.ipynb)
+* [Fleet assignment problem](01-fleet-assignment.ipynb)
+* [Robustness analysis of BIM production plan via simulations](02-bim-robustness-analysis.ipynb)
 
 Go to the [next chapter](../08/08.00.md) about robust optimization and to [Chapter 9](../09/09.00.md) about stochastic optimization.
\ No newline at end of file
diff --git a/_sources/notebooks/08/bim-robust-optimization.ipynb b/_sources/notebooks/08/01-bim-robust-optimization.ipynb
similarity index 100%
rename from _sources/notebooks/08/bim-robust-optimization.ipynb
rename to _sources/notebooks/08/01-bim-robust-optimization.ipynb
diff --git a/_sources/notebooks/08/08.00.md b/_sources/notebooks/08/08.00.md
index 44d8d3bd..2c20c746 100644
--- a/_sources/notebooks/08/08.00.md
+++ b/_sources/notebooks/08/08.00.md
@@ -2,6 +2,6 @@
 
 In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of robust optimization:
 
-* [Robust version of BIM production problem](bim-robust-optimization.ipynb)
+* [Robust version of BIM production problem](01-bim-robust-optimization.ipynb)
 
 Go to the [next chapter](../09/09.00.md) about stochastic optimization.
\ No newline at end of file
diff --git a/_sources/notebooks/09/pop-up_shop.ipynb b/_sources/notebooks/09/01-pop-up-shop.ipynb
similarity index 100%
rename from _sources/notebooks/09/pop-up_shop.ipynb
rename to _sources/notebooks/09/01-pop-up-shop.ipynb
diff --git a/_sources/notebooks/09/markowitz_portfolio_with_chance_constraint.ipynb b/_sources/notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb
similarity index 100%
rename from _sources/notebooks/09/markowitz_portfolio_with_chance_constraint.ipynb
rename to _sources/notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb
diff --git a/_sources/notebooks/09/seafood.ipynb b/_sources/notebooks/09/03-seafood-distribution-center.ipynb
similarity index 100%
rename from _sources/notebooks/09/seafood.ipynb
rename to _sources/notebooks/09/03-seafood-distribution-center.ipynb
diff --git a/_sources/notebooks/09/economicdispatch.ipynb b/_sources/notebooks/09/04-economic-dispatch.ipynb
similarity index 99%
rename from _sources/notebooks/09/economicdispatch.ipynb
rename to _sources/notebooks/09/04-economic-dispatch.ipynb
index 948c9396..3f66ce2c 100644
--- a/_sources/notebooks/09/economicdispatch.ipynb
+++ b/_sources/notebooks/09/04-economic-dispatch.ipynb
@@ -173,7 +173,9 @@
    "outputs": [],
    "source": [
     "def read_economic_dispatch_data():\n",
-    "    base_url = \"https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/09/\"\n",
+    "    base_url = (\n",
+    "        \"https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/09/data/\"\n",
+    "    )\n",
     "    nodes_df = pd.read_csv(base_url + \"nodes.csv\", index_col=0)[\n",
     "        [\"node_id\", \"d\", \"p_min\", \"p_max\", \"c_var\"]\n",
     "    ]\n",
diff --git a/_sources/notebooks/09/09.00.md b/_sources/notebooks/09/09.00.md
index 6578af9a..6bb75ccc 100644
--- a/_sources/notebooks/09/09.00.md
+++ b/_sources/notebooks/09/09.00.md
@@ -2,9 +2,9 @@
 
 In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of stochastic optimization:
 
-* [Optimal management of a pop-up shop](pop-up_shop.ipynb)
-* [Markowitz portfolio with chance constraints](markowitz_portfolio_with_chance_constraint.ipynb)
-* [Stock optimization for seafood distribution center](seafood.ipynb)
-* [Economic dispatch in energy systems](economicdispatch.ipynb)
+* [Optimal management of a pop-up shop](01-pop-up-shop.ipynb)
+* [Markowitz portfolio with chance constraints](02-markowitz-portfolio-with-chance-constraint.ipynb)
+* [Stock optimization for seafood distribution center](03-seafood-distribution-center.ipynb)
+* [Economic dispatch in energy systems](04-economic-dispatch.ipynb)
 
 Go to the [next chapter](../10/10.00.md) about two-stage (robust and stochastic) optimization.
\ No newline at end of file
diff --git a/_sources/notebooks/10/opf-ldr.ipynb b/_sources/notebooks/10/02-opf-linear-decision-rule.ipynb
similarity index 96%
rename from _sources/notebooks/10/opf-ldr.ipynb
rename to _sources/notebooks/10/02-opf-linear-decision-rule.ipynb
index 1f9fd469..f79b82da 100644
--- a/_sources/notebooks/10/opf-ldr.ipynb
+++ b/_sources/notebooks/10/02-opf-linear-decision-rule.ipynb
@@ -277,9 +277,7 @@
     "    )\n",
     "    model.NG = pyo.Set(\n",
     "        initialize=[\n",
-    "            i\n",
-    "            for i, data in network[\"nodes\"].items()\n",
-    "            if pd.isna(data[\"energy_type\"])\n",
+    "            i for i, data in network[\"nodes\"].items() if pd.isna(data[\"energy_type\"])\n",
     "        ]\n",
     "    )\n",
     "\n",
@@ -307,10 +305,7 @@
     "        / len(model.T)\n",
     "        * sum(\n",
     "            sum(\n",
-    "                2\n",
-    "                * data[\"c_var\"]\n",
-    "                * model.alpha[i]\n",
-    "                * model.abs_total_imbalance[t]\n",
+    "                2 * data[\"c_var\"] * model.alpha[i] * model.abs_total_imbalance[t]\n",
     "                for i, data in network[\"nodes\"].items()\n",
     "                if data[\"energy_type\"] in [\"coal\", \"gas\"]\n",
     "            )\n",
@@ -339,9 +334,7 @@
     "    )\n",
     "\n",
     "    # Participation factors must sum to one\n",
-    "    model.sum_one = pyo.Constraint(\n",
-    "        rule=sum(model.alpha[i] for i in model.V) == 1\n",
-    "    )\n",
+    "    model.sum_one = pyo.Constraint(rule=sum(model.alpha[i] for i in model.V) == 1)\n",
     "\n",
     "    if uniformparticipationfactors:\n",
     "        # Participation factors must be equal\n",
@@ -353,8 +346,7 @@
     "    model.power_withrecourse = pyo.Constraint(\n",
     "        model.V,\n",
     "        model.T,\n",
-    "        rule=lambda m, i, t: m.r[i, t]\n",
-    "        == m.p[i] - m.alpha[i] * m.total_imbalance[t],\n",
+    "        rule=lambda m, i, t: m.r[i, t] == m.p[i] - m.alpha[i] * m.total_imbalance[t],\n",
     "    )\n",
     "    model.generation_upper_bound_withrecourse = pyo.Constraint(\n",
     "        model.CG,\n",
@@ -371,16 +363,12 @@
     "    model.outgoing_flow = pyo.Expression(\n",
     "        model.V,\n",
     "        model.T,\n",
-    "        rule=lambda m, i, t: sum(\n",
-    "            m.f[i, j, t] for j in model.V if (i, j) in model.E\n",
-    "        ),\n",
+    "        rule=lambda m, i, t: sum(m.f[i, j, t] for j in model.V if (i, j) in model.E),\n",
     "    )\n",
     "    model.incoming_flow = pyo.Expression(\n",
     "        model.V,\n",
     "        model.T,\n",
-    "        rule=lambda m, i, t: sum(\n",
-    "            m.f[j, i, t] for j in model.V if (j, i) in model.E\n",
-    "        ),\n",
+    "        rule=lambda m, i, t: sum(m.f[j, i, t] for j in model.V if (j, i) in model.E),\n",
     "    )\n",
     "\n",
     "    # Net power production at each node after recourse actions\n",
@@ -401,14 +389,12 @@
     "    model.flows_upper_bound = pyo.Constraint(\n",
     "        model.E,\n",
     "        model.T,\n",
-    "        rule=lambda m, i, j, t: m.f[(i, j), t]\n",
-    "        <= network[\"edges\"][(i, j)][\"f_max\"],\n",
+    "        rule=lambda m, i, j, t: m.f[(i, j), t] <= network[\"edges\"][(i, j)][\"f_max\"],\n",
     "    )\n",
     "    model.flows_lower_bound = pyo.Constraint(\n",
     "        model.E,\n",
     "        model.T,\n",
-    "        rule=lambda m, i, j, t: -m.f[(i, j), t]\n",
-    "        <= network[\"edges\"][(i, j)][\"f_max\"],\n",
+    "        rule=lambda m, i, j, t: -m.f[(i, j), t] <= network[\"edges\"][(i, j)][\"f_max\"],\n",
     "    )\n",
     "\n",
     "    # Solve the model\n",
@@ -454,12 +440,8 @@
     "    }\n",
     "    for t in range(T)\n",
     "]\n",
-    "totalimbalances = {\n",
-    "    t: sum(imbalances[t].values()) for t in range(len(imbalances))\n",
-    "}\n",
-    "abstotalimbalances = {\n",
-    "    t: abs(totalimbalances[t]) for t in range(len(totalimbalances))\n",
-    "}"
+    "totalimbalances = {t: sum(imbalances[t].values()) for t in range(len(imbalances))}\n",
+    "abstotalimbalances = {t: abs(totalimbalances[t]) for t in range(len(totalimbalances))}"
    ]
   },
   {
diff --git a/_sources/notebooks/10/ccg.ipynb b/_sources/notebooks/10/03-two-stage-production-planning.ipynb
similarity index 99%
rename from _sources/notebooks/10/ccg.ipynb
rename to _sources/notebooks/10/03-two-stage-production-planning.ipynb
index 959361aa..940fa60a 100644
--- a/_sources/notebooks/10/ccg.ipynb
+++ b/_sources/notebooks/10/03-two-stage-production-planning.ipynb
@@ -10,7 +10,7 @@
     "```\n",
     "```{index} single: application; production planning\n",
     "```\n",
-    "```{index} single: solver; cbc\n",
+    "```{index} single: solver; highs\n",
     "```\n",
     "```{index} single: Pyomo; persistent solvers\n",
     "```\n",
@@ -21,7 +21,7 @@
     "```{index} constraint and column generation\n",
     "```\n",
     "\n",
-    "# Two-stage Production Planning\n",
+    "# Two-stage production planning using constraint and column generation\n",
     "\n",
     "The purpose of this notebook is to demonstrate a range of techniques for two-stage optimization (robust and stochastic) using a range of techniques:\n",
     "* Robust Optimization (including Constraint and Column Generation)\n",
@@ -645,9 +645,7 @@
     "worst_case_ps = np.zeros(len(Z))\n",
     "for s in range(len(Z)):\n",
     "    c, q, R, S, t = model_params(**Z[s])\n",
-    "    worst_case_ps[s] = (\n",
-    "        sum(c[i] * m.x[i]() for i in c.keys()) + m.scenario[s].y[\"y3\"]()\n",
-    "    )\n",
+    "    worst_case_ps[s] = sum(c[i] * m.x[i]() for i in c.keys()) + m.scenario[s].y[\"y3\"]()\n",
     "\n",
     "xopt_avg = 637.08\n",
     "print(\n",
@@ -685,7 +683,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -881,10 +879,7 @@
     "        def at_least_one_violated(\n",
     "            b,\n",
     "        ):  # only care for violations related to the `actual' constraints, not the profit constraint\n",
-    "            return (\n",
-    "                sum(b.u[k] for k in L.keys() if k != \"profit\")\n",
-    "                <= len(L.keys()) - 2\n",
-    "            )\n",
+    "            return sum(b.u[k] for k in L.keys() if k != \"profit\") <= len(L.keys()) - 2\n",
     "\n",
     "        @b.Constraint(L.keys())\n",
     "        def model_constraints(b, k):\n",
diff --git a/_sources/notebooks/10/farmer.ipynb b/_sources/notebooks/10/04-farmer-problem.ipynb
similarity index 99%
rename from _sources/notebooks/10/farmer.ipynb
rename to _sources/notebooks/10/04-farmer-problem.ipynb
index 4560b1a3..760bd86f 100644
--- a/_sources/notebooks/10/farmer.ipynb
+++ b/_sources/notebooks/10/04-farmer-problem.ipynb
@@ -435,20 +435,12 @@
     "        df[\"plant [acres]\"] = pd.Series({c: m.acres[c]() for c in m.CROPS})\n",
     "        df[\"grow [tons]\"] = pd.Series({c: m.grow[s, c]() for c in m.CROPS})\n",
     "        df[\"buy [tons]\"] = pd.Series({c: m.buy[s, c]() for c in m.CROPS})\n",
-    "        df[\"feed [tons]\"] = pd.Series(\n",
-    "            {c: crops.loc[c, \"required\"] for c in m.CROPS}\n",
-    "        )\n",
-    "        df[\"quota [tons]\"] = pd.Series(\n",
-    "            {c: crops.loc[c, \"quota\"] for c in m.CROPS}\n",
-    "        )\n",
+    "        df[\"feed [tons]\"] = pd.Series({c: crops.loc[c, \"required\"] for c in m.CROPS})\n",
+    "        df[\"quota [tons]\"] = pd.Series({c: crops.loc[c, \"quota\"] for c in m.CROPS})\n",
     "        df[\"sell [tons]\"] = pd.Series({c: m.sell[s, c]() for c in m.CROPS})\n",
     "        df[\"excess [tons]\"] = pd.Series({c: m.excess[s, c]() for c in m.CROPS})\n",
-    "        df[\"revenue [euro]\"] = pd.Series(\n",
-    "            {c: m.revenue[s, c]() for c in m.CROPS}\n",
-    "        )\n",
-    "        df[\"expense [euro]\"] = pd.Series(\n",
-    "            {c: m.expense[s, c]() for c in m.CROPS}\n",
-    "        )\n",
+    "        df[\"revenue [euro]\"] = pd.Series({c: m.revenue[s, c]() for c in m.CROPS})\n",
+    "        df[\"expense [euro]\"] = pd.Series({c: m.expense[s, c]() for c in m.CROPS})\n",
     "        df[\"profit [euro]\"] = pd.Series(\n",
     "            {c: m.revenue[s, c]() - m.expense[s, c]() for c in m.CROPS}\n",
     "        )\n",
@@ -580,10 +572,12 @@
    "source": [
     "m = farmer(crops, pd.DataFrame(yields.mean(), columns=[\"mean\"]).T)\n",
     "\n",
+    "\n",
     "@m.Objective(sense=pyo.maximize)\n",
     "def objective(m):\n",
     "    return pyo.summation(m.scenario_profit)\n",
     "\n",
+    "\n",
     "pyo.SolverFactory(SOLVER).solve(m)\n",
     "farm_report(m)"
    ]
@@ -1673,7 +1667,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1702,9 +1696,7 @@
     "    df.loc[wc, \"return\"] = m.objective()\n",
     "    df.loc[wc, \"wc\"] = wc\n",
     "\n",
-    "df.plot(\n",
-    "    x=\"wc\", y=\"return\", xlabel=\"worst case\", ylabel=\"mean return\", grid=True\n",
-    ")"
+    "df.plot(x=\"wc\", y=\"return\", xlabel=\"worst case\", ylabel=\"mean return\", grid=True)"
    ]
   },
   {
@@ -1733,7 +1725,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1762,9 +1754,7 @@
     "    df.loc[wc, \"return\"] = m.objective()\n",
     "    df.loc[wc, \"wc\"] = wc\n",
     "\n",
-    "df.plot(\n",
-    "    x=\"wc\", y=\"return\", xlabel=\"worst case\", ylabel=\"mean return\", grid=True\n",
-    ")"
+    "df.plot(x=\"wc\", y=\"return\", xlabel=\"worst case\", ylabel=\"mean return\", grid=True)"
    ]
   },
   {
diff --git a/_sources/notebooks/10/opf-wind-curtailment.ipynb b/_sources/notebooks/10/05-opf-wind-curtailment.ipynb
similarity index 95%
rename from _sources/notebooks/10/opf-wind-curtailment.ipynb
rename to _sources/notebooks/10/05-opf-wind-curtailment.ipynb
index 124d8e7d..c454e5e0 100644
--- a/_sources/notebooks/10/opf-wind-curtailment.ipynb
+++ b/_sources/notebooks/10/05-opf-wind-curtailment.ipynb
@@ -28,12 +28,7 @@
    "source": [
     "## Preamble: Install Pyomo and a solver\n",
     "\n",
-    "This cell selects and verifies a global SOLVER for the notebook.\n",
-    "\n",
-    "If run on Google Colab, the cell installs Pyomo and HiGHS, then sets SOLVER to \n",
-    "use the Highs solver via the appsi module. If run elsewhere, it assumes Pyomo and CBC\n",
-    "have been previously installed and sets SOLVER to use the CBC solver via the Pyomo \n",
-    "SolverFactory. It then verifies that SOLVER is available."
+    "The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object `SOLVER` for later use."
    ]
   },
   {
@@ -43,14 +38,17 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "import os\n",
-    "\n",
-    "if \"google.colab\" in sys.modules:\n",
-    "    !pip install pyomo >/dev/null 2>/dev/null\n",
-    "    !pip install highspy >/dev/null 2>/dev/null\n",
-    "    SOLVER = \"appsi_highs\"\n",
-    "else:\n",
-    "    SOLVER = \"cbc\""
+    " \n",
+    "if 'google.colab' in sys.modules:\n",
+    "    %pip install pyomo >/dev/null 2>/dev/null\n",
+    "    %pip install highspy >/dev/null 2>/dev/null\n",
+    " \n",
+    "solver = 'appsi_highs'\n",
+    " \n",
+    "import pyomo.environ as pyo\n",
+    "SOLVER = pyo.SolverFactory(solver)\n",
+    " \n",
+    "assert SOLVER.available(), f\"Solver {solver} is not available.\""
    ]
   },
   {
@@ -169,13 +167,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "id": "bxwjVztvKQpk"
    },
    "outputs": [],
    "source": [
-    "# Load packages\n",
     "import pyomo.environ as pyo\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -185,7 +182,7 @@
     "import time\n",
     "\n",
     "# Download the data\n",
-    "base_url = \"https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/10/\"\n",
+    "base_url = \"https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/10/data/\"\n",
     "nodes_df = pd.read_csv(base_url + \"nodes.csv\", index_col=0)\n",
     "edges_df = pd.read_csv(base_url + \"edges.csv\", index_col=0)\n",
     "\n",
@@ -891,10 +888,16 @@
    ],
    "source": [
     "def g_offshore(v):\n",
-    "    return np.piecewise(v, [v <= 3.5, v > 15], [0, 600, lambda v: 0.18007 * v**3 - 7.72049])\n",
+    "    return np.piecewise(\n",
+    "        v, [v <= 3.5, v > 15], [0, 600, lambda v: 0.18007 * v**3 - 7.72049]\n",
+    "    )\n",
+    "\n",
     "\n",
     "def g_onshore(v):\n",
-    "    return np.piecewise(v, [v <= 3, v > 14], [0, 450, lambda v: 0.16563 * v**3 - 4.4718])\n",
+    "    return np.piecewise(\n",
+    "        v, [v <= 3, v > 14], [0, 450, lambda v: 0.16563 * v**3 - 4.4718]\n",
+    "    )\n",
+    "\n",
     "\n",
     "# assign wind generators\n",
     "g = {64: g_onshore, 65: g_offshore}\n",
@@ -906,7 +909,7 @@
     "\n",
     "plt.legend()\n",
     "plt.xlabel(\"Wind Speed\")\n",
-    "plt.ylabel(\"Power Output\") "
+    "plt.ylabel(\"Power Output\")"
    ]
   },
   {
@@ -928,38 +931,61 @@
     "    model.T = pyo.Set(initialize=range(len(samples)))\n",
     "    model.V = pyo.Set(initialize=network[\"nodes\"].keys())\n",
     "    model.E = pyo.Set(initialize=network[\"edges\"].keys())\n",
-    "    model.W = pyo.Set(initialize=[i for i, data in network[\"nodes\"].items() if data[\"energy_type\"] == \"wind\"])\n",
-    "    \n",
+    "    model.W = pyo.Set(\n",
+    "        initialize=[\n",
+    "            i for i, data in network[\"nodes\"].items() if data[\"energy_type\"] == \"wind\"\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
     "    # Set including all generator nodes except wind generators\n",
-    "    model.NW = pyo.Set(initialize=[i for i, data in network[\"nodes\"].items() if data[\"energy_type\"] != \"wind\"])  \n",
-    "    \n",
+    "    model.NW = pyo.Set(\n",
+    "        initialize=[\n",
+    "            i for i, data in network[\"nodes\"].items() if data[\"energy_type\"] != \"wind\"\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
     "    model.M = 1000\n",
     "\n",
     "    # Declare decision variables\n",
-    "    \n",
+    "\n",
     "    # Binary variable indicating whether a generator is on or off\n",
-    "    model.x = pyo.Var(model.V, domain=pyo.Binary)  \n",
-    "    \n",
+    "    model.x = pyo.Var(model.V, domain=pyo.Binary)\n",
+    "\n",
     "    # Power generation of each generator\n",
-    "    model.p = pyo.Var(model.V, model.T, domain=pyo.NonNegativeReals) \n",
-    "    \n",
+    "    model.p = pyo.Var(model.V, model.T, domain=pyo.NonNegativeReals)\n",
+    "\n",
     "    # Voltage angle of each node\n",
-    "    model.theta = pyo.Var(model.V, model.T) \n",
-    "    \n",
+    "    model.theta = pyo.Var(model.V, model.T)\n",
+    "\n",
     "    # Power flow on each edge\n",
-    "    model.f = pyo.Var(model.E, model.T, bounds=lambda m, i, j, t: (-network[\"edges\"][(i, j)][\"f_max\"], network[\"edges\"][(i, j)][\"f_max\"]))\n",
-    "    \n",
+    "    model.f = pyo.Var(\n",
+    "        model.E,\n",
+    "        model.T,\n",
+    "        bounds=lambda m, i, j, t: (\n",
+    "            -network[\"edges\"][(i, j)][\"f_max\"],\n",
+    "            network[\"edges\"][(i, j)][\"f_max\"],\n",
+    "        ),\n",
+    "    )\n",
+    "\n",
     "    # Binary variable indicating whether a wind turbine is curtailed (0) or not (1)\n",
-    "    model.y = pyo.Var(model.W, model.T, domain=pyo.Binary)  \n",
+    "    model.y = pyo.Var(model.W, model.T, domain=pyo.Binary)\n",
     "\n",
     "    # Objective function, with the goal of minimizing the total cost of generation\n",
     "    @model.Expression(model.T)\n",
     "    def term2(model, t):\n",
-    "        return sum(data[\"c_var\"] * model.p[i, t] for i, data in network[\"nodes\"].items() if data[\"is_generator\"])\n",
-    "    \n",
+    "        return sum(\n",
+    "            data[\"c_var\"] * model.p[i, t]\n",
+    "            for i, data in network[\"nodes\"].items()\n",
+    "            if data[\"is_generator\"]\n",
+    "        )\n",
+    "\n",
     "    @model.Objective(sense=pyo.minimize)\n",
     "    def objective(model):\n",
-    "        term1 = sum(1000 * model.x[i] for i, data in network[\"nodes\"].items() if data[\"energy_type\"] in [\"coal\", \"gas\"])\n",
+    "        term1 = sum(\n",
+    "            1000 * model.x[i]\n",
+    "            for i, data in network[\"nodes\"].items()\n",
+    "            if data[\"energy_type\"] in [\"coal\", \"gas\"]\n",
+    "        )\n",
     "        return term1 + sum(model.term2[t] for t in model.T) / len(model.T)\n",
     "\n",
     "    # Declare constraints\n",
@@ -968,7 +994,7 @@
     "    @model.Constraint(model.W, model.T)\n",
     "    def wind_speed_to_power(m, i, t):\n",
     "        return model.p[i, t] == (1 - model.y[i, t]) * g[i](samples[t][i])\n",
-    "    \n",
+    "\n",
     "    @model.Constraint(model.W, model.T)\n",
     "    def wind_curtailment(m, i, t):\n",
     "        return samples[t][i] <= vmax[i] + model.y[i, t] * model.M\n",
@@ -976,7 +1002,7 @@
     "    @model.Constraint(model.NW, model.T)\n",
     "    def generation_upper_bound(m, i, t):\n",
     "        return m.p[i, t] <= m.x[i] * network[\"nodes\"][i][\"p_max\"]\n",
-    "    \n",
+    "\n",
     "    @model.Constraint(model.NW, model.T)\n",
     "    def generation_lower_bound(m, i, t):\n",
     "        return m.p[i, t] >= m.x[i] * network[\"nodes\"][i][\"p_min\"]\n",
@@ -987,10 +1013,12 @@
     "        incoming_flow = sum(m.f[j, i, t] for j in model.V if (j, i) in model.E)\n",
     "        outgoing_flow = sum(m.f[i, j, t] for j in model.V if (i, j) in model.E)\n",
     "        return incoming_flow - outgoing_flow == m.p[i, t] - nodes[i][\"d\"]\n",
-    "    \n",
+    "\n",
     "    @model.Constraint(model.E, model.T)\n",
     "    def susceptance(m, i, j, t):\n",
-    "        return m.f[(i, j), t] == network[\"edges\"][(i, j)][\"b\"] * (m.theta[i, t] - m.theta[j, t])\n",
+    "        return m.f[(i, j), t] == network[\"edges\"][(i, j)][\"b\"] * (\n",
+    "            m.theta[i, t] - m.theta[j, t]\n",
+    "        )\n",
     "\n",
     "    return model"
    ]
@@ -1028,15 +1056,24 @@
     "scale65 = 18\n",
     "shape65 = 3\n",
     "\n",
-    "samples = [{64: scale64 * rng.weibull(shape64), 65: scale65 * rng.weibull(shape65)} for i in range(100)]\n",
+    "samples = [\n",
+    "    {64: scale64 * rng.weibull(shape64), 65: scale65 * rng.weibull(shape65)}\n",
+    "    for i in range(100)\n",
+    "]\n",
     "\n",
     "model = UC_windcurtailment(network, samples)\n",
-    "results = pyo.SolverFactory(SOLVER).solve(model)\n",
+    "results = SOLVER.solve(model)\n",
     "\n",
     "print(f\"Objective value: {model.objective():.2f}\")\n",
-    "turbineactivity = np.sum([[model.y[x, t].value for x in model.W] for t in model.T], axis=0)\n",
-    "print(f\"Number of scenarios wind tubine 64 is active: {turbineactivity[0]:2.0f} out of {len(model.T)}\")\n",
-    "print(f\"Number of scenarios wind tubine 65 is active: {turbineactivity[1]:2.0f} out of {len(model.T)}\")"
+    "turbineactivity = np.sum(\n",
+    "    [[model.y[x, t].value for x in model.W] for t in model.T], axis=0\n",
+    ")\n",
+    "print(\n",
+    "    f\"Number of scenarios wind tubine 64 is active: {turbineactivity[0]:2.0f} out of {len(model.T)}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Number of scenarios wind tubine 65 is active: {turbineactivity[1]:2.0f} out of {len(model.T)}\"\n",
+    ")"
    ]
   },
   {
@@ -1057,14 +1094,19 @@
    "outputs": [],
    "source": [
     "def UC_windcurtailment_fixed_x(network, samples, fixed_x=None):\n",
-    "\n",
     "    model = UC_windcurtailment(network, samples)\n",
-    "    model.G = pyo.Set(initialize=[i for i, data in network[\"nodes\"].items() if data[\"is_generator\"] and data[\"energy_type\"] != \"wind\"])\n",
+    "    model.G = pyo.Set(\n",
+    "        initialize=[\n",
+    "            i\n",
+    "            for i, data in network[\"nodes\"].items()\n",
+    "            if data[\"is_generator\"] and data[\"energy_type\"] != \"wind\"\n",
+    "        ]\n",
+    "    )\n",
     "\n",
     "    if fixed_x is not None:\n",
     "        for i in model.G:\n",
     "            model.x[i].fix(fixed_x[i]())\n",
-    "        \n",
+    "\n",
     "    return model"
    ]
   },
@@ -1091,10 +1133,12 @@
    "source": [
     "T = len(samples)\n",
     "\n",
-    "mean_sample = {i: np.array([samples[t][i] for t in range(T)]).mean() for i in samples[0].keys()}\n",
+    "mean_sample = {\n",
+    "    i: np.array([samples[t][i] for t in range(T)]).mean() for i in samples[0].keys()\n",
+    "}\n",
     "\n",
     "m_nominal = UC_windcurtailment_fixed_x(network, [mean_sample])\n",
-    "pyo.SolverFactory(SOLVER).solve(m_nominal)\n",
+    "SOLVER.solve(m_nominal)\n",
     "\n",
     "print(f\"Nominal objective = {m_nominal.objective():0.2f}\")\n",
     "fixed_x = m_nominal.x"
@@ -1124,17 +1168,16 @@
     "import logging\n",
     "\n",
     "# don't log pyomo warning messages\n",
-    "logging.getLogger('pyomo.core').setLevel(logging.ERROR)\n",
+    "logging.getLogger(\"pyomo.core\").setLevel(logging.ERROR)\n",
     "\n",
     "# Variable to count the number of infeasible second stage problems\n",
     "n_infeasible = 0\n",
     "\n",
-    "solver = pyo.SolverFactory(SOLVER)\n",
     "for t in range(T):\n",
     "    m_single = UC_windcurtailment_fixed_x(network, [samples[t]], fixed_x)\n",
-    "    results = solver.solve(m_single)\n",
+    "    results = SOLVER.solve(m_single)\n",
     "    n_infeasible += 1 if results.solver.termination_condition == \"infeasible\" else 0\n",
-    " \n",
+    "\n",
     "print(f\"The second stage problem is infeasible in {n_infeasible} out of {T} instances\")"
    ]
   },
@@ -1162,7 +1205,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.10"
   },
   "vscode": {
    "interpreter": {
diff --git a/_sources/notebooks/10/10.00.md b/_sources/notebooks/10/10.00.md
index 7c2877d4..96dbb288 100644
--- a/_sources/notebooks/10/10.00.md
+++ b/_sources/notebooks/10/10.00.md
@@ -2,8 +2,8 @@
 
 In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of two-stage problems affected by uncertainty:
 
-* [Airline seating splitting](airline-seating.ipynb)
-* [Optimal power flow problem with recourse actions](opf-ldr.ipynb), a two-stage revisitation of the problem from [Chapter 4](../04/04.00.md) that uses linear decision rules.
-* [Two-stage production planning](ccg.ipynb), a multi-stage revisitation of the introductory problem from [Chapter 1](../01/01.00.md) solved both with SAA and CCG.
-* [Extra: Farmer land allocation](farmer.ipynb)
-* [Extra: Two-stage energy dispatch optimization with wind curtailment](opf-wind-curtailment.ipynb), a two-stage revisitation of the problem from [Chapter 4](../04/04.00.md) with binary second-stage variables.
\ No newline at end of file
+* [Airline seating splitting](01-airline-seating.ipynb)
+* [Optimal power flow problem with recourse actions](02-opf-linear-decision-rule.ipynb), a two-stage variant of the problem from [Chapter 4](../04/04.00.md) that uses linear decision rules.
+* [Two-stage production planning](03-column-constraint-generation.ipynb), a multi-stage variant of the introductory problem from [Chapter 1](../01/01.00.md) solved both with SAA and CCG.
+* [Extra: Farmer land allocation](04-farmer-problem.ipynb)
+* [Extra: Two-stage energy dispatch optimization with wind curtailment](05-opf-wind-curtailment.ipynb), a two-stage variant of the problem from [Chapter 4](../04/04.00.md) with binary second-stage variables.
\ No newline at end of file
diff --git a/_sources/notebooks/10/airline-seating.ipynb b/_sources/notebooks/10/airline-seating.ipynb
deleted file mode 100644
index 38695c0e..00000000
--- a/_sources/notebooks/10/airline-seating.ipynb
+++ /dev/null
@@ -1,3061 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dcadad27-4c87-4222-b248-17b1068311ff",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "```{index} single: application; airline seating allocation\n",
-    "```\n",
-    "```{index} single: solver; cbc\n",
-    "```\n",
-    "```{index} pandas dataframe\n",
-    "```\n",
-    "```{index} single: Pyomo; sets\n",
-    "```\n",
-    "```{index} stochastic optimization\n",
-    "```\n",
-    "```{index} chance constraints\n",
-    "```\n",
-    "```{index} sample average approximation\n",
-    "```\n",
-    "```{index} two-stage problem\n",
-    "```\n",
-    "\n",
-    "# Airline seat allocation problem\n",
-    "\n",
-    "## Attribution\n",
-    "\n",
-    "The following problem statement is adapted from an exercise and examples presented by Birge and Louveaux (2011). \n",
-    "\n",
-    "* Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming. Springer Science & Business Media.\n",
-    "\n",
-    "The adaptations include a change to parameters for consistency among reformulations of the problem, and additional treatments for sample average approximation (SAA) with chance constraints."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78a7e349-0b92-4f23-b76b-b1d0e1e153f0",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Problem description\n",
-    "\n",
-    "An airline is deciding how to partition a new plane for the Amsterdam-Buenos Aires route. This plane can seat 200 economy-class passengers. A section can be created for first-class seats, but each of these seats takes the space of 2 economy-class seats. A business-class section can also be created, but each of these takes the space of 1.5 economy-class seats. The profit for a first-class ticket is three times the profit of an economy ticket, while a business-class ticket has a profit of two times an economy ticket's profit. Once the plane is partitioned into these seating classes, it cannot be changed. \n",
-    "\n",
-    "The airlines knows that the plane will not always be full in every section. The airline has initially identified three scenarios to consider with about equal frequency: \n",
-    "\n",
-    "1. Weekday morning and evening traffic;\n",
-    "2. Weekend traffic; and\n",
-    "3. Weekday midday traffic. \n",
-    "\n",
-    "Under Scenario 1 the airline thinks they can sell as many as 20 first-class tickets, 50 business-class tickets, and 200 economy tickets. Under Scenario 2 these figures are 10 , 24, and 175, while under Scenario 3, they are 6, 10, and 150, respectively. The following table summarizes the forecast demand for these three scenarios.\n",
-    "\n",
-    "<center>\n",
-    "\n",
-    "| Scenario | First-class seats | Business-class seats | Economy-class seats |\n",
-    "| :-- | :-: | :-: | :-: |\n",
-    "| (1) weekday morning and evening | 20 | 50 | 200 |\n",
-    "| (2) weekend | 10 | 24 | 175 |\n",
-    "| (3) weekday midday | 6 | 10 | 150 |\n",
-    "| **Average Scenario** | **12** | **28** | **175** |\n",
-    "\n",
-    "</center>\n",
-    "\n",
-    "The goal of the airline is to maximize ticket revenue. For marketing purposes, the airline will not sell more tickets than seats in each of the sections (hence no overbooking strategy). We further assume customers seeking a first-class or business-class seat will not downgrade if those seats are unavailable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "347cbd20-15e4-4634-8a05-e782dc0e0929",
-   "metadata": {},
-   "source": [
-    "## Installation and imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ea71de65",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-09-30T21:49:05.660595Z",
-     "start_time": "2022-09-30T21:49:05.457825Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# install pyomo and select solver\n",
-    "import sys\n",
-    "\n",
-    "SOLVER = \"cbc\"\n",
-    "\n",
-    "if \"google.colab\" in sys.modules:\n",
-    "    !pip install highspy >/dev/null\n",
-    "    SOLVER = \"appsi_highs\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e91fbe82",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-09-30T21:49:07.404490Z",
-     "start_time": "2022-09-30T21:49:05.663157Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pyomo.environ as pyo\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f7df9f7-9a96-4358-9e47-50cd63d7611a",
-   "metadata": {},
-   "source": [
-    "## Problem data\n",
-    "\n",
-    "Pandas DataFrames and Series are used to encode problem data in the following cell, and to encode problem solutions in subsequent cells."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "51e9fdd8-d586-48e6-be86-0d97fbad8a3f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# scenario data\n",
-    "demand = pd.DataFrame(\n",
-    "    {\n",
-    "        \"morning and evening\": {\"F\": 20, \"B\": 50, \"E\": 200},\n",
-    "        \"weekend\": {\"F\": 10, \"B\": 24, \"E\": 175},\n",
-    "        \"midday\": {\"F\": 6, \"B\": 10, \"E\": 150},\n",
-    "    }\n",
-    ").T\n",
-    "\n",
-    "# global revenue and seat factor data\n",
-    "capacity = 200\n",
-    "revenue_factor = pd.Series({\"F\": 3.0, \"B\": 2.0, \"E\": 1.0})\n",
-    "seat_factor = pd.Series({\"F\": 2.0, \"B\": 1.5, \"E\": 1.0})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfbd3423-f10d-49a4-b7f7-f6545729c9cc",
-   "metadata": {},
-   "source": [
-    "## Analytics\n",
-    "\n",
-    "Prior to optimization, a useful first step is to prepare an analytics function to display performance for any given allocation of seats. The first-stage decision variables are the number of seats allocated for each class $c\\in C$. We would like to provide a analysis showing the operational consequences for any proposed allocation of seats. For this purpose, we create a function ``seat_report()`` that show the tickets that can be sold in each scenario, the resulting revenue, and the unsatisfied demand ('spillage'). \n",
-    "\n",
-    "To establish a basis for analyzing possible solutions to the airline's problem, this function first is demonstrated for the case where the airplane is configured as entirely economy-class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3eed107a-1fec-4d6c-9b01-f399fbe3bb99",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Seat Allocation\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>F</th>\n",
-       "      <th>B</th>\n",
-       "      <th>E</th>\n",
-       "      <th>TOTAL</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>seat allocation</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>200.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>economy equivalent seat allocation</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>200.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      F    B      E  TOTAL\n",
-       "seat allocation                     0.0  0.0  200.0  200.0\n",
-       "economy equivalent seat allocation  0.0  0.0  200.0  200.0"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Tickets Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th>F</th>\n",
-       "      <th>B</th>\n",
-       "      <th>E</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>morning and evening</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>weekend</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>midday</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>150</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     F  B    E\n",
-       "morning and evening  0  0  200\n",
-       "weekend              0  0  175\n",
-       "midday               0  0  150"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Seats not Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "      <th>E</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>morning and evening</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>weekend</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>midday</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "  </tbody>\n",
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-      "text/plain": [
-       "                     F  B   E\n",
-       "morning and evening  0  0   0\n",
-       "weekend              0  0  25\n",
-       "midday               0  0  50"
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-     "metadata": {},
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Spillage (Unfulfilled Demand)\n"
-     ]
-    },
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-     "data": {
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-       "  <tbody>\n",
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-       "      <th>morning and evening</th>\n",
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-       "      <th>weekend</th>\n",
-       "      <td>10</td>\n",
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-       "      <th>midday</th>\n",
-       "      <td>6</td>\n",
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-      "text/plain": [
-       "                      F   B  E\n",
-       "morning and evening  20  50  0\n",
-       "weekend              10  24  0\n",
-       "midday                6  10  0"
-      ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Expected Revenue (in units of economy ticket price): 175.00\n"
-     ]
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# function to report analytics for any given seat allocations\n",
-    "def seat_report(seats, demand):\n",
-    "    classes = seats.index\n",
-    "\n",
-    "    # report seat allocation\n",
-    "    equivalent_seats = pd.DataFrame(\n",
-    "        {\n",
-    "            \"seat allocation\": {c: seats[c] for c in classes},\n",
-    "            \"economy equivalent seat allocation\": {\n",
-    "                c: seats[c] * seat_factor[c] for c in classes\n",
-    "            },\n",
-    "        }\n",
-    "    ).T\n",
-    "    equivalent_seats[\"TOTAL\"] = equivalent_seats.sum(axis=1)\n",
-    "    print(\"\\nSeat Allocation\")\n",
-    "    display(equivalent_seats)\n",
-    "\n",
-    "    # tickets sold is the minimum of available seats and demand\n",
-    "    tickets = pd.DataFrame()\n",
-    "    for c in classes:\n",
-    "        tickets[c] = np.minimum(seats[c], demand[c])\n",
-    "    print(\"\\nTickets Sold\")\n",
-    "    display(tickets)\n",
-    "\n",
-    "    # seats unsold\n",
-    "    unsold = pd.DataFrame()\n",
-    "    for c in classes:\n",
-    "        unsold[c] = seats[c] - tickets[c]\n",
-    "    print(\"\\nSeats not Sold\")\n",
-    "    display(unsold)\n",
-    "\n",
-    "    # spillage (unmet demand)\n",
-    "    spillage = demand - tickets\n",
-    "    print(\"\\nSpillage (Unfulfilled Demand)\")\n",
-    "    display(spillage)\n",
-    "\n",
-    "    # compute revenue\n",
-    "    revenue = tickets.dot(revenue_factor)\n",
-    "    print(\n",
-    "        f\"\\nExpected Revenue (in units of economy ticket price): {revenue.mean():.2f}\"\n",
-    "    )\n",
-    "\n",
-    "    # charts\n",
-    "    fig, ax = plt.subplots(2, 1, figsize=(8, 6))\n",
-    "    revenue.plot(ax=ax[0], kind=\"barh\", title=\"Revenue by Scenario\")\n",
-    "    ax[0].plot([revenue.mean()] * 2, ax[0].get_ylim(), \"--\", lw=1.4)\n",
-    "    ax[0].set_xlabel(\"Revenue\")\n",
-    "\n",
-    "    tickets[classes].plot(\n",
-    "        ax=ax[1], kind=\"barh\", rot=0, stacked=False, title=\"Demand Scenarios\"\n",
-    "    )\n",
-    "    demand[classes].plot(\n",
-    "        ax=ax[1],\n",
-    "        kind=\"barh\",\n",
-    "        rot=0,\n",
-    "        stacked=False,\n",
-    "        title=\"Demand Scenarios\",\n",
-    "        alpha=0.2,\n",
-    "    )\n",
-    "    for c in classes:\n",
-    "        ax[1].plot([seats[c]] * 2, ax[1].get_ylim(), \"--\", lw=1.4)\n",
-    "    ax[1].set_xlabel(\"Seats\")\n",
-    "    fig.tight_layout()\n",
-    "\n",
-    "    return\n",
-    "\n",
-    "\n",
-    "# a trial solution\n",
-    "seats_all_economy = pd.Series({\"F\": 0, \"B\": 0, \"E\": 200})\n",
-    "seat_report(seats_all_economy, demand)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "60309d8c-a2e4-48ee-9510-d2cccb2fd1af",
-   "metadata": {},
-   "source": [
-    "## Model 1. Deterministic solution for the average demand scenario\n",
-    "\n",
-    "A common starting point in stochastic optimization is to solve the deterministic problem where future demands are fixed at their mean values and compute the corresponding optimal solution. The resulting value of the objective has been called the *expectation of the expected value problem (EEV)* by Birge, or the *expected value of the mean (EVM)* solution by others.\n",
-    "\n",
-    "Let us introduce the set $C$ of the three possible classes, i.e., $C=\\{F,B,E\\}$. The objective function is to maximize ticket revenue.\n",
-    "\n",
-    "$$\n",
-    "\\max_{s_c, t_c} \\quad \\sum_{c\\in C} r_c t_c \n",
-    "$$\n",
-    "\n",
-    "where $r_c$ is the revenue from selling a ticket for a seat in class $c\\in C$.\n",
-    "\n",
-    "Let $s_c$ denote the number of seats of class $c \\in C$ installed in the new plane. Let $f_c$ be the scale factor denoting the number of economy seats displaced by one seat in class $c \\in C$. Then, since there is a total of 200 economy-class seats that could fit on the plane, the capacity constraint reads as\n",
-    "\n",
-    "$$\n",
-    "    \\sum_{c\\in C} f_c s_c \\leq 200,\n",
-    "$$\n",
-    "\n",
-    "Let $\\mu_c$ be the mean demand for seats of class $c\\in C$, and let $t_c$ be the number of tickets of class $c\\in C$ that are sold. To ensure we do not sell more tickets than available seats nor more than demand, we need to add two more constraints:\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "    t_c & \\leq s_c \\qquad \\forall \\, c\\in C \\\\\n",
-    "    t_c & \\leq \\mu_c \\qquad \\forall \\, c\\in C\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "Lastly, both ticket and seat variables need to be nonnegative integers, so we add the constraints $\\bm{t}, \\bm{s} \\in \\mathbb{Z}_+$. \n",
-    "\n",
-    "The following cell presents a Pyomo model implementing this model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "434a69fc-3bd2-4eb8-95f2-2613e82432b0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Seat Allocation\n"
-     ]
-    },
-    {
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-       "      <th></th>\n",
-       "      <th>F</th>\n",
-       "      <th>B</th>\n",
-       "      <th>E</th>\n",
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-       "      <th>seat allocation</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>174.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>economy equivalent seat allocation</th>\n",
-       "      <td>24.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>200.0</td>\n",
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-       "                                       F     B      E  TOTAL\n",
-       "seat allocation                     12.0  28.0  134.0  174.0\n",
-       "economy equivalent seat allocation  24.0  42.0  134.0  200.0"
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-     "output_type": "stream",
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-      "\n",
-      "Tickets Sold\n"
-     ]
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-      "\n",
-      "Seats not Sold\n"
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-       "                       F     B    E\n",
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-     "output_type": "stream",
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-      "\n",
-      "Spillage (Unfulfilled Demand)\n"
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-     "text": [
-      "\n",
-      "Expected Revenue (in units of economy ticket price): 203.33\n"
-     ]
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def airline_deterministic(demand):\n",
-    "    m = pyo.ConcreteModel(\"Airline seating (deterministic)\")\n",
-    "\n",
-    "    m.CLASSES = pyo.Set(initialize=demand.columns)\n",
-    "\n",
-    "    # first stage variables and constraints\n",
-    "    m.seats = pyo.Var(m.CLASSES, domain=pyo.NonNegativeIntegers)\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES)\n",
-    "    def plane_seats(m, c):\n",
-    "        return sum(m.seats[c] * seat_factor[c] for c in m.CLASSES) <= capacity\n",
-    "\n",
-    "    # second stage variable and constraints\n",
-    "    m.tickets = pyo.Var(m.CLASSES, domain=pyo.NonNegativeIntegers)\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES)\n",
-    "    def demand_limits(m, c):\n",
-    "        return m.tickets[c] <= demand[c].mean()\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES)\n",
-    "    def seat_limits(m, c):\n",
-    "        return m.tickets[c] <= m.seats[c]\n",
-    "\n",
-    "    # objective\n",
-    "    @m.Objective(sense=pyo.maximize)\n",
-    "    def revenue(m):\n",
-    "        return sum(m.tickets[c] * revenue_factor[c] for c in m.CLASSES)\n",
-    "\n",
-    "    return m\n",
-    "\n",
-    "\n",
-    "def airline_solve(m):\n",
-    "    pyo.SolverFactory(SOLVER).solve(m)\n",
-    "    return pd.Series({c: m.seats[c]() for c in m.CLASSES})\n",
-    "\n",
-    "\n",
-    "# Solve deterministic model to obtain the expectation of the expected value problem (EEV)\n",
-    "model_eev = airline_deterministic(demand)\n",
-    "seats_eev = airline_solve(model_eev)\n",
-    "seat_report(seats_eev, demand)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1e11c9c-1ab7-4f33-938b-ca6b927640cb",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Model 2. Two-stage stochastic optimization and its extensive form\n",
-    "\n",
-    "If we assume demand is not certain, we can formulate a two-stage stochastic optimization problem. The first-stage or here-and-now variables are the $s_c$'s, those related to the seat allocations. Due to their dependence on the realized demand $\\boldsymbol{z}$, the variables $t_c$'s describing the number of tickets sold, are second-stage or recourse decision variables. The full problem formulation is as follows: the first stage problem is\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "    \\max \\quad & \\mathbb E_{z} Q(\\boldsymbol{s},\\boldsymbol{z}) \\\\\n",
-    "    \\text{s.t.} \\quad & \\sum_{c\\in C} f_c s_c \\leq 200,\\\\\n",
-    "    & \\boldsymbol{s} \\in \\mathbb{Z}_+,\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "where $Q(\\boldsymbol{s},\\boldsymbol{z})$ is the value of the second-stage problem, defined as\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "    Q(\\boldsymbol{s},\\boldsymbol{z}) := \\max \\quad & \\sum_{c\\in C} r_c t_{c}\\\\\n",
-    "    \\text{s.t.} \\quad \n",
-    "    & t_c \\leq s_c & \\forall \\, c\\in C \\\\\n",
-    "    & t_c \\leq z_c & \\forall \\, c\\in C \\\\\n",
-    "    & \\boldsymbol{t} \\in \\mathbb{Z}_+.\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "In view of the assumption that there is only a finite number $N=|S|$ of scenarios for ticket demand, we can write the extensive form of the two-stage stochastic optimization problem above and solve it exactly. To do so, we modify the second-stage variables $t_{c,s}$ so that they are indexed by both class $c$ and scenario $s$. The expectation can thus be replaced with the average revenue over the $N$ scenarios, that is\n",
-    "\n",
-    "$$\n",
-    "\\max \\quad \\sum_{s \\in S} \\frac{1}{N} \\sum_{c\\in C}  r_c t_{c, s},\n",
-    "$$\n",
-    "\n",
-    "where the fraction $\\frac{1}{N}$ appears since we assume that all $N$ scenarios are equally likely. The first stage constraint remains unchanged, while the second stage constraints are tuplicated for each scenario $s \\in S$, namely\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "t_{c,s} & \\leq s_c & \\forall \\, c\\in C \\\\\n",
-    "t_{c,s} & \\leq z_{c, s} & \\forall \\, (c, s) \\in C \\times S\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "The following cell presents a Pyomo model implementing this model and solving it for the $N=3$ equiprobable scenarios introduced above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "be868fbe-efcf-4970-beaa-fd85718c8914",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Seat Allocation\n"
-     ]
-    },
-    {
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-       "      <th>seat allocation</th>\n",
-       "      <td>10.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>180.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>economy equivalent seat allocation</th>\n",
-       "      <td>20.0</td>\n",
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-       "                                       F     B      E  TOTAL\n",
-       "seat allocation                     10.0  20.0  150.0  180.0\n",
-       "economy equivalent seat allocation  20.0  30.0  150.0  200.0"
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-      "\n",
-      "Tickets Sold\n"
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-      "\n",
-      "Seats not Sold\n"
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-     "output_type": "stream",
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-      "\n",
-      "Spillage (Unfulfilled Demand)\n"
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-     "text": [
-      "\n",
-      "Expected Revenue (in units of economy ticket price): 209.33\n"
-     ]
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def airline_stochastic(demand):\n",
-    "    m = pyo.ConcreteModel(\"Airline seating (stochastic)\")\n",
-    "\n",
-    "    m.CLASSES = pyo.Set(initialize=demand.columns)\n",
-    "    m.SCENARIOS = pyo.Set(initialize=demand.index)\n",
-    "\n",
-    "    # first stage variables and constraints\n",
-    "    m.seats = pyo.Var(m.CLASSES, domain=pyo.NonNegativeIntegers)\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES)\n",
-    "    def plane_seats(m, c):\n",
-    "        return sum(m.seats[c] * seat_factor[c] for c in m.CLASSES) <= capacity\n",
-    "\n",
-    "    # second stage variable and constraints\n",
-    "    m.tickets = pyo.Var(m.CLASSES, m.SCENARIOS, domain=pyo.NonNegativeIntegers)\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES, m.SCENARIOS)\n",
-    "    def demand_limits(m, c, s):\n",
-    "        return m.tickets[c, s] <= demand[c][s]\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES, m.SCENARIOS)\n",
-    "    def seat_limits(m, c, s):\n",
-    "        return m.tickets[c, s] <= m.seats[c]\n",
-    "\n",
-    "    # objective\n",
-    "    @m.Objective(sense=pyo.maximize)\n",
-    "    def revenue(m):\n",
-    "        return sum(\n",
-    "            m.tickets[c, s] * revenue_factor[c]\n",
-    "            for c in m.CLASSES\n",
-    "            for s in m.SCENARIOS\n",
-    "        )\n",
-    "\n",
-    "    return m\n",
-    "\n",
-    "\n",
-    "# create and solve model for the three scenarios defined above\n",
-    "model_stochastic = airline_stochastic(demand)\n",
-    "seats_stochastic = airline_solve(model_stochastic)\n",
-    "seat_report(seats_stochastic, demand)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "875bd08b",
-   "metadata": {},
-   "source": [
-    "## Model 3. Adding chance constraints\n",
-    "\n",
-    "The airline wishes a special guarantee for its clients enrolled in its loyalty program. In particular, it wants $98\\%$ probability to cover the demand of first-class seats and $95\\%$ probability to cover the demand of business-class seats (by clients of the loyalty program). First-class passengers are covered if they can purchase a first-class seat. Business-class passengers are covered if they purchase a business-class seat or upgrade to a first-class seat. \n",
-    "\n",
-    "Assume the demand of loyalty-program passengers is normally distributed as $z_F \\sim \\mathcal N(\\mu_F, \\sigma_F^2)$ and $z_B \\sim \\mathcal N(\\mu_B, \\sigma_B^2)$ for first-class and business, respectively, where the parameters are given in the table below. For completeness, we also include the parameters for economy-class passengers.\n",
-    "\n",
-    "<center>\n",
-    "\n",
-    "| | $\\mu_{\\cdot}$ | $\\sigma_{\\cdot}$ |\n",
-    "| :--: | :--: | :--: |\n",
-    "| F | 12 | 4 |\n",
-    "| B | 28 | 8 |\n",
-    "| E | 175 | 20 |\n",
-    "\n",
-    "</center>\n",
-    "\n",
-    "We further assume that the demands for first-class and business-class seats are independent of each other and of the scenario (time of the week).\n",
-    "\n",
-    "Let $s_F$ be the number of first-class seats and $s_B$ the number of business seats. The probabilistic constraints are\n",
-    "\n",
-    "$$\n",
-    "\\mathbb P(s_F \\geq z_F ) \\geq 0.98, \\qquad \\text{ and } \\qquad \\mathbb P(s_F + s_B \\geq z_F + z_B ) \\geq 0.95.\n",
-    "$$\n",
-    "\n",
-    "These are can be written equivalently as linear constraints, specifically \n",
-    "\n",
-    "$$\n",
-    "\\frac{s_F - \\mu_F}{\\sigma_F} \\geq \\Phi^{-1}(0.98) \\approx 2.054 \\qquad \\text{ and } \\qquad \\frac{(s_F + s_B) - (\\mu_F + \\mu_B)}{\\sqrt{\\sigma_F^2 + \\sigma_B^2}} \\geq \\Phi^{-1}(0.95) \\approx 1.645.\n",
-    "$$\n",
-    "\n",
-    "For the second constraint, we use the fact that the sum of the two independent random variables $z_F$ and $z_B$ is normally distributed with mean $\\mu_F + \\mu_B$ and variance $\\sigma_F^2 + \\sigma_B^2$. \n",
-    "\n",
-    "We add these equivalent linear counterparts of the two chance constraints to the stochastic optimization model. Rather than writing a function to create a whole new model, we can use the prior function to create and add the two chance constraints using decorators."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2a3aeb53-b57f-4e00-91ed-1fb1ac4b75b8",
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-       "      mu  sigma\n",
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-      "Seat Allocation\n"
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-       "      <th>seat allocation</th>\n",
-       "      <td>21.0</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>155.0</td>\n",
-       "    </tr>\n",
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-       "      <th>economy equivalent seat allocation</th>\n",
-       "      <td>42.0</td>\n",
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-       "                                       F     B     E  TOTAL\n",
-       "seat allocation                     21.0  47.0  87.0  155.0\n",
-       "economy equivalent seat allocation  42.0  70.5  87.0  199.5"
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-      "\n",
-      "Tickets Sold\n"
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-       "      <td>20.0</td>\n",
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-       "      <th>weekend</th>\n",
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-       "                        F     B     E\n",
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-      "Seats not Sold\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "\n",
-      "Spillage (Unfulfilled Demand)\n"
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-       "                       F    B      E\n",
-       "morning and evening  0.0  3.0  113.0\n",
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-     "text": [
-      "\n",
-      "Expected Revenue (in units of economy ticket price): 177.00\n"
-     ]
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# import the scipy.stats.norm object needed to use the quantile function of the standard normal distribution\n",
-    "from scipy.stats import norm\n",
-    "\n",
-    "# define the mean and standard deviation of the demand for each class\n",
-    "mu = demand.mean()\n",
-    "sigma = {\"F\": 4, \"B\": 16, \"E\": 20}\n",
-    "display(pd.DataFrame({\"mu\": mu, \"sigma\": sigma}))\n",
-    "\n",
-    "\n",
-    "# create a new model with chance constraints that takes as input also\n",
-    "# the target quality of service (QoS) levels for classes F and B\n",
-    "def airline_cc(demand, QoSF=0.98, QoSFB=0.95):\n",
-    "    # create two-stage stochastic model as before\n",
-    "    m = airline_stochastic(demand)\n",
-    "\n",
-    "    # add equivalent counterparts of the two chance constraints to the first stage problem\n",
-    "    # the two coefficients related the inverse CDF of the standard normal are computed using the norm.ppf function\n",
-    "    @m.Constraint()\n",
-    "    def first_class(m):\n",
-    "        return (m.seats[\"F\"] - mu[\"F\"]) >= norm.ppf(QoSF) * sigma[\"F\"]\n",
-    "\n",
-    "    @m.Constraint()\n",
-    "    def business_class(m):\n",
-    "        return m.seats[\"F\"] + m.seats[\"B\"] - (mu[\"F\"] + mu[\"B\"]) >= norm.ppf(\n",
-    "            QoSFB\n",
-    "        ) * np.sqrt(sigma[\"F\"] ** 2 + sigma[\"B\"] ** 2)\n",
-    "\n",
-    "    return m\n",
-    "\n",
-    "\n",
-    "# create and solve model\n",
-    "model_cc = airline_cc(demand)\n",
-    "seats_cc = airline_solve(model_cc)\n",
-    "seat_report(seats_cc, demand)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "801f475e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Model 4. Solving the case of continuous demand distributions using the SAA method \n",
-    "\n",
-    "Let us now move past the simplifying assumption that there are only three equally likely scenarios and consider the case where the demand is described by a random vector $(z_F, z_B, z_E)$, where $z_c$ is the demand for seats of class $c\\in C$. The demand for class $c$ is assumed to be independent and normally distributed with mean $\\mu_c$ and variance $\\sigma_c^2$ as reported in the following table\n",
-    "\n",
-    "<center>\n",
-    "\n",
-    "| | $\\mu$ | $\\sigma$ |\n",
-    "| :--: | :--: | :--: |\n",
-    "| F | 12 | 4 |\n",
-    "| B | 28 | 8 |\n",
-    "| E | 175 | 20 |\n",
-    "\n",
-    "</center>\n",
-    "\n",
-    "Note that we model the demand for each class using a continuous random variable, which is a simplification of the real world, where the ticket demand is always a discrete nonnegative number. Therefore, we round down all the obtained random numbers.\n",
-    "\n",
-    "However, now that the number of scenarios is not finite anymore, we cannot solve the problem exactly. Instead, we can use the SAA method to approximate the expected value appearing in the objective function. The first step of the SAA is to generate a collection of $N$ scenarios $(z_{F,s}, z_{B,s}, z_{E,s})$ for $s=1,\\ldots,N$. We can do this by sampling from the normal distributions with the given means and variances. \n",
-    "\n",
-    "For sake of generality, we create a script to generate scenarios in which the three demands have a general correlation structure captured by a correlation matrix $\\bm{\\rho}$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10a9b698-59b8-42a9-ae7e-9c37a32d8a0c",
-   "metadata": {},
-   "source": [
-    "### Scenario generation (uncorrelated case)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d537f6ac-407c-4d73-8f52-5f4ac31b8ec8",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-09-30T21:49:08.708126Z",
-     "start_time": "2022-09-30T21:49:07.626836Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Model Covariance\n"
-     ]
-    },
-    {
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-       "    F    B    E\n",
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-       "   mu (mean)  sample mean  sigma (std)  sample std\n",
-       "F       12.0       11.889            4    4.088936\n",
-       "B       28.0       28.626           16   15.051534\n",
-       "E      175.0      172.966           20   19.839669"
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\n",
-      "text/plain": [
-       "<Figure size 1000x300 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# sample size\n",
-    "N = 1000\n",
-    "\n",
-    "# define the mean mu and standard deviation sigma of the demand for each class\n",
-    "mu = demand.mean()\n",
-    "sigma = {\"F\": 4, \"B\": 16, \"E\": 20}\n",
-    "classes = demand.columns\n",
-    "\n",
-    "# build covariance matrix from covariances and correlations\n",
-    "s = np.array(list(sigma.values()))\n",
-    "S = np.diag(s) @ np.diag(s)\n",
-    "print(\"\\nModel Covariance\")\n",
-    "df = pd.DataFrame(S, index=classes, columns=classes)\n",
-    "display(df)\n",
-    "\n",
-    "# generate N samples, round each demand entry to nearest integer, and correct non-negative values\n",
-    "seed = 0\n",
-    "rng = np.random.default_rng(seed)\n",
-    "samples = rng.multivariate_normal(list(mu), S, N).round()\n",
-    "demand_saa = pd.DataFrame(samples, columns=classes)\n",
-    "demand_saa[demand_saa < 0] = 0\n",
-    "\n",
-    "# report sample means and standard deviations for each class\n",
-    "demand_saa_stats = pd.DataFrame(\n",
-    "    {\n",
-    "        \"mu (mean)\": mu,\n",
-    "        \"sample mean\": demand_saa.mean(),\n",
-    "        \"sigma (std)\": sigma,\n",
-    "        \"sample std\": demand_saa.std(),\n",
-    "    }\n",
-    ")\n",
-    "display(demand_saa_stats)\n",
-    "\n",
-    "fig, ax = plt.subplots(1, 3, figsize=(10, 3))\n",
-    "for i, ci in enumerate(classes):\n",
-    "    demand_saa[ci].hist(ax=ax[i], bins=20)\n",
-    "    ax[i].set_title(f\"Histogram demand class {ci}\")\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5050323e",
-   "metadata": {},
-   "source": [
-    "We also introduce a new function to report the solution and its features in this more general continuous demand case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "6e70be11",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# function to report analytics for SAA case\n",
-    "def seat_report_saa(seats, demand):\n",
-    "    classes = seats.index\n",
-    "\n",
-    "    # report seat allocation\n",
-    "    equivalent_seats = pd.DataFrame(\n",
-    "        {\n",
-    "            \"seat allocation\": {c: seats[c] for c in classes},\n",
-    "            \"economy equivalent seat allocation\": {\n",
-    "                c: seats[c] * seat_factor[c] for c in classes\n",
-    "            },\n",
-    "        }\n",
-    "    ).T\n",
-    "    equivalent_seats[\"TOTAL\"] = equivalent_seats.sum(axis=1)\n",
-    "    print(\"\\nSeat Allocation\")\n",
-    "    display(equivalent_seats)\n",
-    "\n",
-    "    # tickets sold\n",
-    "    tickets = pd.DataFrame()\n",
-    "    for c in classes:\n",
-    "        tickets[c] = np.minimum(seats[c], demand[c])\n",
-    "\n",
-    "    print(\"\\nMean Tickets Sold\")\n",
-    "    display(tickets.mean())\n",
-    "\n",
-    "    # seats unsold\n",
-    "    unsold = pd.DataFrame()\n",
-    "    for c in classes:\n",
-    "        unsold[c] = seats[c] - tickets[c]\n",
-    "    print(\"\\nMean Seats not Sold\")\n",
-    "    display(unsold.mean())\n",
-    "\n",
-    "    # spillage (unmet demand)\n",
-    "    spillage = demand - tickets\n",
-    "    print(\"\\nMean Spillage (Unfulfilled Demand)\")\n",
-    "    display(spillage.mean())\n",
-    "\n",
-    "    # compute revenue\n",
-    "    revenue = tickets.dot(revenue_factor)\n",
-    "    print(\n",
-    "        f\"\\nExpected Revenue (in units of economy ticket price): {revenue.mean():.2f}\"\n",
-    "    )\n",
-    "\n",
-    "    # charts\n",
-    "    fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
-    "    revenue.hist(ax=ax, bins=20)\n",
-    "    ax.set_title(\"Revenue Histogram\")\n",
-    "\n",
-    "    fig, ax = plt.subplots(1, 3, figsize=(12, 3))\n",
-    "    for i, c in enumerate(classes):\n",
-    "        tickets[c].hist(ax=ax[i], bins=20, alpha=0.4)\n",
-    "        demand[c].hist(ax=ax[i], alpha=0.4)\n",
-    "        ax[i].legend([\"Tickets Sold\", \"Demand\"])\n",
-    "        ax[i].set_xlim(0, 300)\n",
-    "\n",
-    "    fig.tight_layout()\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4b47726",
-   "metadata": {},
-   "source": [
-    "We can now solve the stochastic optimization problem using the generated $N=1000$ scenarios. Note that we can use the previously defined function ``airline_stochastic`` to solve the model by simply calling it with a different dataframe as argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "7f100de8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Seat Allocation\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>F</th>\n",
-       "      <th>B</th>\n",
-       "      <th>E</th>\n",
-       "      <th>TOTAL</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>seat allocation</th>\n",
-       "      <td>11.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>179.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>economy equivalent seat allocation</th>\n",
-       "      <td>22.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>200.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       F     B      E  TOTAL\n",
-       "seat allocation                     11.0  20.0  148.0  179.0\n",
-       "economy equivalent seat allocation  22.0  30.0  148.0  200.0"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Tickets Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F      9.764\n",
-       "B     17.416\n",
-       "E    147.056\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Seats not Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F    1.236\n",
-       "B    2.584\n",
-       "E    0.944\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Spillage (Unfulfilled Demand)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F     2.125\n",
-       "B    11.210\n",
-       "E    25.910\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Expected Revenue (in units of economy ticket price): 211.18\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x300 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model_ssa = airline_stochastic(demand_saa)\n",
-    "seats_saa = airline_solve(model_ssa)\n",
-    "seat_report_saa(seats_saa, demand_saa)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3e8e43c",
-   "metadata": {},
-   "source": [
-    "## Model 5. Adding correlations between different demand types\n",
-    "\n",
-    "Now assume the ticket demand for the three categories is captured by a $3$-dimensional multivariate normal distribution mean $\\mu=(\\mu_F, \\mu_B, \\mu_E)$, variances $(\\sigma_F^2, \\sigma_B^2, \\sigma_E^2)$ and a symmetric correlation matrix \n",
-    "\n",
-    "$$\n",
-    "P = \\left(\n",
-    "\\begin{array}{ccc}\n",
-    "1 & \\rho_{FB} & \\rho_{FE} \\\\\n",
-    "\\rho_{BF} & 1 & \\rho_{BE}\\\\\n",
-    "\\rho_{EF} & \\rho_{EB} & 1 \\\\\n",
-    "\\end{array}\n",
-    "\\right)\n",
-    "$$\n",
-    "\n",
-    "The covariance matrix is given by $\\Sigma = \\text{diag}(\\sigma)\\ P\\ \\text{diag}(\\sigma)$ or\n",
-    "\n",
-    "$$\n",
-    "\\Sigma= \\left(\n",
-    "\\begin{array}{ccc}\n",
-    " \\sigma_F^2 & \\rho_{FB}\\sigma_F\\sigma_B & \\rho_{FE}\\sigma_F\\sigma_E \\\\\n",
-    "\\rho_{BF}\\sigma_B\\sigma_F & \\sigma_B^2 & \\rho_{BE}\\sigma_B\\sigma_E\\\\\n",
-    "\\rho_{EF}\\sigma_E\\sigma_F & \\rho_{EB}\\sigma_E\\sigma_B & \\sigma_E^2 \\\\\n",
-    "\\end{array}\n",
-    "\\right)\n",
-    "$$\n",
-    "\n",
-    "We assume $\\rho_{FB} = 0.6$, $\\rho_{BE} = 0.4$ and $\\rho_{FE} = 0.2$.\n",
-    " \n",
-    "We now sample $N=1000$ scenarios from this multivariate correlated normal distribution and use the SAA method to approximate the solution of the two-stage stochastic optimization problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "79e75010",
-   "metadata": {},
-   "outputs": [
-    {
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>F</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>16</td>\n",
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-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <td>175.0</td>\n",
-       "      <td>20</td>\n",
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-      ],
-      "text/plain": [
-       "      mu  sigma\n",
-       "F   12.0      4\n",
-       "B   28.0     16\n",
-       "E  175.0     20"
-      ]
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>F</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>12.012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>28.590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <td>175.0</td>\n",
-       "      <td>175.301</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-      "text/plain": [
-       "      mu  sample means\n",
-       "F   12.0        12.012\n",
-       "B   28.0        28.590\n",
-       "E  175.0       175.301"
-      ]
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-       "      <th>sigma</th>\n",
-       "      <th>sample std dev</th>\n",
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-       "  <tbody>\n",
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-       "      <th>F</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3.951388</td>\n",
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-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>16</td>\n",
-       "      <td>15.139984</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <td>20</td>\n",
-       "      <td>20.147365</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-      "text/plain": [
-       "   sigma  sample std dev\n",
-       "F      4        3.951388\n",
-       "B     16       15.139984\n",
-       "E     20       20.147365"
-      ]
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-    {
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-     "text": [
-      "\n",
-      "Model Covariance\n"
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>F</th>\n",
-       "      <td>16.0</td>\n",
-       "      <td>38.4</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>38.4</td>\n",
-       "      <td>256.0</td>\n",
-       "      <td>128.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <td>16.0</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>400.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      F      B      E\n",
-       "F  16.0   38.4   16.0\n",
-       "B  38.4  256.0  128.0\n",
-       "E  16.0  128.0  400.0"
-      ]
-     },
-     "metadata": {},
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Sample Covariance\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>F</th>\n",
-       "      <td>15.613469</td>\n",
-       "      <td>36.044965</td>\n",
-       "      <td>12.434823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>36.044965</td>\n",
-       "      <td>229.219119</td>\n",
-       "      <td>107.216627</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <td>12.434823</td>\n",
-       "      <td>107.216627</td>\n",
-       "      <td>405.916315</td>\n",
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-       "           F           B           E\n",
-       "F  15.613469   36.044965   12.434823\n",
-       "B  36.044965  229.219119  107.216627\n",
-       "E  12.434823  107.216627  405.916315"
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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# sample size\n",
-    "N = 1000\n",
-    "\n",
-    "# define the mean mu and standard deviation sigma of the demand for each class\n",
-    "mu = demand.mean()\n",
-    "sigma = {\"F\": 4, \"B\": 16, \"E\": 20}\n",
-    "display(pd.DataFrame({\"mu\": mu, \"sigma\": sigma}))\n",
-    "\n",
-    "# correlation matrix\n",
-    "P = np.array([[1, 0.6, 0.2], [0.6, 1, 0.4], [0.2, 0.4, 1]])\n",
-    "\n",
-    "# build covariance matrix from covariances and correlations\n",
-    "s = np.array(list(sigma.values()))\n",
-    "S = np.diag(s) @ P @ np.diag(s)\n",
-    "\n",
-    "# create samples\n",
-    "np.random.seed(1)\n",
-    "samples = np.random.multivariate_normal(list(mu), S, N).round()\n",
-    "\n",
-    "# truncate to integers and non-negative values\n",
-    "classes = demand.columns\n",
-    "demand_saa = pd.DataFrame(samples, columns=classes)\n",
-    "demand_saa[demand_saa < 0] = 0\n",
-    "\n",
-    "df = pd.DataFrame(mu, columns=[\"mu\"])\n",
-    "df[\"sample means\"] = demand_saa.mean()\n",
-    "display(df)\n",
-    "\n",
-    "df = pd.DataFrame(pd.Series(sigma), columns=[\"sigma\"])\n",
-    "df[\"sample std dev\"] = demand_saa.std()\n",
-    "display(df)\n",
-    "\n",
-    "print(\"\\nModel Covariance\")\n",
-    "df = pd.DataFrame(S, index=classes, columns=classes)\n",
-    "display(df)\n",
-    "\n",
-    "print(\"\\nSample Covariance\")\n",
-    "display(pd.DataFrame(demand_saa.cov()))\n",
-    "\n",
-    "fig, ax = plt.subplots(3, 3, figsize=(10, 10))\n",
-    "for i, ci in enumerate(classes):\n",
-    "    for j, cj in enumerate(classes):\n",
-    "        if i == j:\n",
-    "            demand_saa[ci].hist(ax=ax[i, i], bins=20)\n",
-    "            ax[i, i].set_title(f\"Histogram {ci}\")\n",
-    "        else:\n",
-    "            ax[i, j].plot(demand_saa[ci], demand_saa[cj], \".\", alpha=0.4)\n",
-    "            ax[i, j].set_xlabel(ci)\n",
-    "            ax[i, j].set_ylabel(cj)\n",
-    "            ax[i, j].set_title(f\"Convariance: {ci} vs {cj}\")\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2c9fdfc2-7f45-4a08-ab45-6d8715443743",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Seat Allocation\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>F</th>\n",
-       "      <th>B</th>\n",
-       "      <th>E</th>\n",
-       "      <th>TOTAL</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>seat allocation</th>\n",
-       "      <td>11.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>179.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>economy equivalent seat allocation</th>\n",
-       "      <td>22.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>200.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       F     B      E  TOTAL\n",
-       "seat allocation                     11.0  20.0  148.0  179.0\n",
-       "economy equivalent seat allocation  22.0  30.0  148.0  200.0"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Tickets Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F      9.863\n",
-       "B     17.303\n",
-       "E    147.184\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Seats not Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F    1.137\n",
-       "B    2.697\n",
-       "E    0.816\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Spillage (Unfulfilled Demand)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F     2.149\n",
-       "B    11.287\n",
-       "E    28.117\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Expected Revenue (in units of economy ticket price): 211.38\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x300 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model_ssa = airline_stochastic(demand_saa)\n",
-    "seats_saa = airline_solve(model_ssa)\n",
-    "seat_report_saa(seats_saa, demand_saa)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d20a5abd-2b54-436d-a171-b8f1fa3048dd",
-   "metadata": {},
-   "source": [
-    "## Model 6. Tackling chance constraints using SAA in the case of correlated demand\n",
-    "\n",
-    "The linear counterparts of the chance constraints used above in Model 3 were derived under the assumption of independent normal distributions of demand for first-class and business travel. That assumption no longer holds for the case where demand scenarios are sampled from correlated distributions.\n",
-    "\n",
-    "This final model replaces the chance constraints by approximating them using two linear constraints that explicitly track unsatisfied demand. In doing so, we introduce two new sets of integer variables $y_s$ and $w_s$ and a big-M constant and approximate the true multivariate distribution with the empirical one obtained from the sample. \n",
-    "\n",
-    "The first stage remains unchanged and so does the objective value of the second stage. The adjusted second-stage constraints are:\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "    t_c & \\leq s_c & \\forall c\\in C \\\\\n",
-    "    t_c & \\leq z_{c, s} & \\forall (c, s) \\in C \\times S \\\\\n",
-    "    s_F + M y_s & \\geq z_{F, s} & \\forall s \\in  S\\\\\n",
-    "    s_F + s_B + M w_s & \\geq z_{F, s} + z_{B,s} & \\forall s \\in  S \\\\\n",
-    "    \\frac{1}{N} \\sum_{s\\in S} y_s & \\leq 1 - 0.98 \\\\\n",
-    "    \\frac{1}{N} \\sum_{s\\in S} z_s & \\leq 1 - 0.95\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "where $y_s$ and $w_s$ are binary variables indicating those scenarios which do not satisfy the requirements of the airline's loyalty programs for first-class and business-class passengers. \n",
-    "\n",
-    "The following cell implements this new model. Note that the running time for the cell can be up to a few minutes for a large number of scenarios."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f10836c0-a503-4b06-b450-a9731e1fd55d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Seat Allocation\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>F</th>\n",
-       "      <th>B</th>\n",
-       "      <th>E</th>\n",
-       "      <th>TOTAL</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>seat allocation</th>\n",
-       "      <td>20.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>155.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>economy equivalent seat allocation</th>\n",
-       "      <td>40.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>200.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       F     B     E  TOTAL\n",
-       "seat allocation                     20.0  50.0  85.0  155.0\n",
-       "economy equivalent seat allocation  40.0  75.0  85.0  200.0"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Tickets Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F    11.995\n",
-       "B    28.031\n",
-       "E    85.000\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Seats not Sold\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F     8.005\n",
-       "B    21.969\n",
-       "E     0.000\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Mean Spillage (Unfulfilled Demand)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "F     0.017\n",
-       "B     0.559\n",
-       "E    90.301\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Expected Revenue (in units of economy ticket price): 177.05\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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hbAIAAMCasNpeAAAAZyNx8srzur9dcwad1/0BDQVnNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWBFQ2y8rK9PjjjyspKUmRkZG66KKLNHPmTJWXl3vmGGPkcrmUkJCgyMhI9enTR9u2bavxhQMAAKDuC6hszp07VwsXLtT8+fO1fft2ZWZmat68eXruuec8czIzM5WVlaX58+crLy9PcXFx6tevn44ePVrjiwcAAEDdFlDZ3Lx5s2666SYNGjRIiYmJuvXWW5WSkqKPP/5Y0k9nNbOzszVt2jSlpqYqOTlZS5YsUUlJiXJycqy8AAAAANRdAZXNXr166b333tPOnTslSZ999pk2btyogQMHSpIKCgpUWFiolJQUz3OcTqd69+6tTZs21eCyAQAAUB+EBTJ50qRJKioqUseOHRUaGqrTp09r1qxZGjZsmCSpsLBQkhQbG+v1vNjYWO3evdvnNktLS1VaWuq5X1xcLElyu91yu91nXE/F41XNCzbk4h/Z+EYu/tVUNs5QUxPLCYjNP8+68DVzvjOt7mutC9nUReTiX33MJpC1Oowx1f7bunTpUj322GOaN2+eOnfurE8//VTp6enKysrSyJEjtWnTJvXs2VP79+9XfHy853n33nuv9u7dq1WrVlXapsvl0owZMyqN5+TkKCoqqtovBAAAAOdHSUmJ0tLSVFRUpJiYmDPODahstm3bVpMnT9aYMWM8Y08//bReeeUVffXVV/r222/1q1/9Svn5+eratatnzk033aSmTZtqyZIllbbp68xm27Zt9cMPP1S5eLfbrdzcXPXr10/h4eHVfRkNHrn4Rza+kYt/NZVNsmt1Da6qer509be27brwNXO+M61unnUhm7qIXPyrj9kUFxerZcuW1SqbAV1GLykpUUiI9495hoaGen71UVJSkuLi4pSbm+spm6dOndL69es1d+5cn9t0Op1yOp2VxsPDw6sdeCBzgwm5+Ec2vpGLf+eaTelpRw2upnrOx59lbX7NnO9MA32d/H3yjVz8q0/ZBLLOgMrmjTfeqFmzZqldu3bq3Lmztm7dqqysLP3hD3+QJDkcDqWnpysjI0MdOnRQhw4dlJGRoaioKKWlpQX2KgAAAFDvBVQ2n3vuOT3xxBMaPXq0Dh48qISEBN1///168sknPXMmTpyoEydOaPTo0Tpy5Ii6d++uNWvWKDo6usYXDwAAgLotoLIZHR2t7OxsZWdn+53jcDjkcrnkcrnOcWkAAACo7/hsdAAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWBNW2wsAANiROHmltW07Q40yr5WSXatVetohSdo1Z5C1/QGovzizCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABr+AQhAECNsPmJRQDqL85sAgAAwBrKJgAAAKzhMjqAoFTdS77OUKPMa6Vk12qVnnZYXhUANDyc2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANXyCEACfqvsJOzVl15xB53V/AGofx5ngwJlNAAAAWEPZBAAAgDVcRgdQJ5zvy2lAXVcbfye4zAwbOLMJAAAAayibAAAAsIayCQAAAGsomwAAALAm4LK5b98+3XnnnWrRooWioqJ05ZVX6pNPPvE8boyRy+VSQkKCIiMj1adPH23btq1GFw0AAID6IaB3ox85ckQ9e/ZU37599fe//12tWrXSN998o6ZNm3rmZGZmKisrSy+99JIuueQSPf300+rXr5927Nih6Ojoml4/AADnRXXfHe4MNcq8Vkp2rVbpaYflVQF1X0Blc+7cuWrbtq0WL17sGUtMTPT8vzFG2dnZmjZtmlJTUyVJS5YsUWxsrHJycnT//ffXzKoBAABQLwR0GX3FihXq1q2b/vM//1OtWrVS165d9ec//9nzeEFBgQoLC5WSkuIZczqd6t27tzZt2lRzqwYAAEC9ENCZzW+//VYLFizQhAkTNHXqVH300Ud66KGH5HQ6NWLECBUWFkqSYmNjvZ4XGxur3bt3+9xmaWmpSktLPfeLi4slSW63W263+4zrqXi8qnnBhlz8IxvffOXiDDW1tZw6xRlivP6Ln5CLf/U5G5vHxrpwnKmrx/76+L0pkLU6jDHV/pNu1KiRunXr5nWW8qGHHlJeXp42b96sTZs2qWfPntq/f7/i4+M9c+69917t3btXq1atqrRNl8ulGTNmVBrPyclRVFRUtV8IAAAAzo+SkhKlpaWpqKhIMTExZ5wb0JnN+Ph4XXbZZV5jnTp10htvvCFJiouLkyQVFhZ6lc2DBw9WOttZYcqUKZowYYLnfnFxsdq2bauUlJQqF+92u5Wbm6t+/fopPDw8kJfSoJGLf2Tjm69ckl2ra3lVdYMzxOipbuV64uMQlZbzZo8K5OJffc7mS1d/a9uuC8cZm6/vXNTH700VV6KrI6Cy2bNnT+3YscNrbOfOnWrfvr0kKSkpSXFxccrNzVXXrl0lSadOndL69es1d+5cn9t0Op1yOp2VxsPDw6sdeCBzgwm5+Ec2vv08F95F66203EEmPpCLf/Uxm/NxXKzN40xdP+7Xp+9NgawzoLI5fvx49ejRQxkZGbrtttv00UcfadGiRVq0aJEkyeFwKD09XRkZGerQoYM6dOigjIwMRUVFKS0tLbBXAQAAgHovoLJ5zTXX6M0339SUKVM0c+ZMJSUlKTs7W3fccYdnzsSJE3XixAmNHj1aR44cUffu3bVmzRp+xyYAAEAQCqhsStLgwYM1ePBgv487HA65XC65XK5zWRcAAAAaAD4bHQAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGBNWG0vAAAA1A2Jk1da27Yz1CjzWinZtVqlpx3W9oO6hzObAAAAsIayCQAAAGsomwAAALCGn9kEAABBwebPpPqya86g87q/uoozmwAAALCGsgkAAABruIwO1BP8ShIAQH3EmU0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANedUNmfPni2Hw6H09HTPmDFGLpdLCQkJioyMVJ8+fbRt27ZzXScAAADqobMum3l5eVq0aJG6dOniNZ6ZmamsrCzNnz9feXl5iouLU79+/XT06NFzXiwAAADql7Mqm8eOHdMdd9yhP//5z2rWrJln3Bij7OxsTZs2TampqUpOTtaSJUtUUlKinJycGls0AAAA6oezKptjxozRoEGDdMMNN3iNFxQUqLCwUCkpKZ4xp9Op3r17a9OmTee2UgAAANQ7YYE+YenSpcrPz1deXl6lxwoLCyVJsbGxXuOxsbHavXu3z+2VlpaqtLTUc7+4uFiS5Ha75Xa7z7iWisermhdsyMW/+pyNM9TY23aI8fov/j+y8Y1c/CMb34Ixl+p+r6mP35sCWWtAZXPv3r16+OGHtWbNGkVERPid53A4vO4bYyqNVZg9e7ZmzJhRaXzNmjWKioqq1rpyc3OrNS/YkIt/9TGbzGvt7+OpbuX2d1JPkY1v5OIf2fgWTLm88847Ac2vT9+bSkpKqj3XYYyp9j8xli9frltuuUWhoaGesdOnT8vhcCgkJEQ7duzQxRdfrPz8fHXt2tUz56abblLTpk21ZMmSStv0dWazbdu2+uGHHxQTE3PG9bjdbuXm5qpfv34KDw+v7sto8MjFv5rMJtm1uoZWVfucIUZPdSvXEx+HqLTc9z8MgxXZ+EYu/pGNb8GYy5eu/tWaVx+/bxcXF6tly5YqKiqqsq8FdGbzt7/9rb744guvsbvuuksdO3bUpEmTdNFFFykuLk65ubmesnnq1CmtX79ec+fO9blNp9Mpp9NZaTw8PLzagQcyN5iQi381kU3p6YZ3sCwtdzTI11UTyMY3cvGPbHwLplwC/T5Tn75vB7LOgMpmdHS0kpOTvcYaN26sFi1aeMbT09OVkZGhDh06qEOHDsrIyFBUVJTS0tIC2RUAAAAagIDfIFSViRMn6sSJExo9erSOHDmi7t27a82aNYqOjq7pXQEAAKCOO+ey+f7773vddzgccrlccrlc57ppAAAA1HN8NjoAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKwJq+0FAAAANESJk1dWa54z1CjzWinZtVqlpx1nvb9dcwad9XNt4swmAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAmrDaXgAapsTJK8/r/nbNGXRe9wcAAKqHM5sAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrAiqbs2fP1jXXXKPo6Gi1atVKN998s3bs2OE1xxgjl8ulhIQERUZGqk+fPtq2bVuNLhoAAAD1Q0Blc/369RozZoy2bNmi3NxclZWVKSUlRcePH/fMyczMVFZWlubPn6+8vDzFxcWpX79+Onr0aI0vHgAAAHVbWCCTV61a5XV/8eLFatWqlT755BNdf/31MsYoOztb06ZNU2pqqiRpyZIlio2NVU5Oju6///6aWzkAAADqvIDK5i8VFRVJkpo3by5JKigoUGFhoVJSUjxznE6nevfurU2bNvksm6WlpSotLfXcLy4uliS53W653e4z7r/i8armBZu6kIsz1JzX/VX3tdZkNuf7NdrkDDFe/8X/Rza+kYt/ZOMbufhXU9mcz+/7gezLYYw5q1dmjNFNN92kI0eOaMOGDZKkTZs2qWfPntq3b58SEhI8c++77z7t3r1bq1evrrQdl8ulGTNmVBrPyclRVFTU2SwNAAAAFpWUlCgtLU1FRUWKiYk549yzPrM5duxYff7559q4cWOlxxwOh9d9Y0ylsQpTpkzRhAkTPPeLi4vVtm1bpaSkVLl4t9ut3Nxc9evXT+Hh4WfxKhqmupBLsqvyPyzqAmeI0VPdyvXExyEqLff9NRmMyMU/svGNXPwjG9/Ixb+ayuZLV/8aXNWZVVyJro6zKpvjxo3TihUr9MEHH6hNmzae8bi4OElSYWGh4uPjPeMHDx5UbGysz205nU45nc5K4+Hh4dUuSoHMDSa1mUvp6bp9ICktd9T5NdYGcvGPbHwjF//Ixjdy8e9cszmf3/MD2VdA70Y3xmjs2LFatmyZ1q5dq6SkJK/Hk5KSFBcXp9zcXM/YqVOntH79evXo0SOQXQEAAKABCOjM5pgxY5STk6O33npL0dHRKiwslCQ1adJEkZGRcjgcSk9PV0ZGhjp06KAOHTooIyNDUVFRSktLs/ICAAAAUHcFVDYXLFggSerTp4/X+OLFizVq1ChJ0sSJE3XixAmNHj1aR44cUffu3bVmzRpFR0fXyIIBAABQfwRUNqvzxnWHwyGXyyWXy3W2awIAAEADwWejAwAAwBrKJgAAAKyhbAIAAMCac/q4Spy9xMkrrW3bGWqUee1Pv1i94vd17ZozyNr+AAAA/OHMJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACs4ROEgoTNTywCAADwhzObAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrwmp7AXVF4uSVtb0EAACABoczmwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsMZa2Xz++eeVlJSkiIgIXX311dqwYYOtXQEAAKCOslI2X3vtNaWnp2vatGnaunWrfvOb3+h3v/ud9uzZY2N3AAAAqKOslM2srCzdfffduueee9SpUydlZ2erbdu2WrBggY3dAQAAoI4Kq+kNnjp1Sp988okmT57sNZ6SkqJNmzZVml9aWqrS0lLP/aKiIknS4cOH5Xa7z7gvt9utkpISHTp0SOHh4ee07rCy4+f0/LokrNyopKRcYe4QnS531PZy6hSy8Y1c/CMb38jFP7LxjVz8q6lsDh06VIOrOrOjR49KkowxVU82NWzfvn1Gkvnwww+9xmfNmmUuueSSSvOnT59uJHHjxo0bN27cuHGrZ7e9e/dW2Q1r/MxmBYfDu5kbYyqNSdKUKVM0YcIEz/3y8nIdPnxYLVq08Dn/54qLi9W2bVvt3btXMTExNbPwBoBc/CMb38jFP7LxjVz8IxvfyMW/+piNMUZHjx5VQkJClXNrvGy2bNlSoaGhKiws9Bo/ePCgYmNjK813Op1yOp1eY02bNg1onzExMfXmD+d8Ihf/yMY3cvGPbHwjF//Ixjdy8a++ZdOkSZNqzavxNwg1atRIV199tXJzc73Gc3Nz1aNHj5reHQAAAOowK5fRJ0yYoOHDh6tbt2769a9/rUWLFmnPnj164IEHbOwOAAAAdZSVsnn77bfr0KFDmjlzpg4cOKDk5GS98847at++fY3ux+l0avr06ZUuwwc7cvGPbHwjF//Ixjdy8Y9sfCMX/xp6Ng5jqvOedQAAACBwfDY6AAAArKFsAgAAwBrKJgAAAKyhbAIAAMCaOl82y8rK9PjjjyspKUmRkZG66KKLNHPmTJWXl3vmGGPkcrmUkJCgyMhI9enTR9u2bavFVdvxwQcf6MYbb1RCQoIcDoeWL1/u9Xh1cigtLdW4cePUsmVLNW7cWEOGDNF33313Hl9FzTtTLm63W5MmTdLll1+uxo0bKyEhQSNGjND+/fu9ttEQc5Gq/pr5ufvvv18Oh0PZ2dle4w0xm+rksn37dg0ZMkRNmjRRdHS0rrvuOu3Zs8fzeEPMRao6m2PHjmns2LFq06aNIiMj1alTJy1YsMBrTkPLZvbs2brmmmsUHR2tVq1a6eabb9aOHTu85gTr8beqbIL1GFydr5mfa+jH3zpfNufOnauFCxdq/vz52r59uzIzMzVv3jw999xznjmZmZnKysrS/PnzlZeXp7i4OPXr18/zIfENxfHjx3XFFVdo/vz5Ph+vTg7p6el68803tXTpUm3cuFHHjh3T4MGDdfr06fP1MmrcmXIpKSlRfn6+nnjiCeXn52vZsmXauXOnhgwZ4jWvIeYiVf01U2H58uX6xz/+4fNjxxpiNlXl8s0336hXr17q2LGj3n//fX322Wd64oknFBER4ZnTEHORqs5m/PjxWrVqlV555RVt375d48eP17hx4/TWW2955jS0bNavX68xY8Zoy5Ytys3NVVlZmVJSUnT8+HHPnGA9/laVTbAeg6vzNVMhKI6/VX56ei0bNGiQ+cMf/uA1lpqaau68805jjDHl5eUmLi7OzJkzx/P4yZMnTZMmTczChQvP61rPJ0nmzTff9NyvTg4//vijCQ8PN0uXLvXM2bdvnwkJCTGrVq06b2u36Ze5+PLRRx8ZSWb37t3GmODIxRj/2Xz33XemdevW5ssvvzTt27c3//3f/+15LBiy8ZXL7bff7jnG+BIMuRjjO5vOnTubmTNneo1dddVV5vHHHzfGBEc2Bw8eNJLM+vXrjTEcf3/ul9n4EozHYH+5BMvxt86f2ezVq5fee+897dy5U5L02WefaePGjRo4cKAkqaCgQIWFhUpJSfE8x+l0qnfv3tq0aVOtrLk2VCeHTz75RG6322tOQkKCkpOTgyqroqIiORwONW3aVFJw51JeXq7hw4frscceU+fOnSs9HozZlJeXa+XKlbrkkkvUv39/tWrVSt27d/e6nByMuVTo1auXVqxYoX379skYo3Xr1mnnzp3q37+/pODIpqioSJLUvHlzSRx/f+6X2fibE2zHYF+5BNPxt86XzUmTJmnYsGHq2LGjwsPD1bVrV6Wnp2vYsGGSpMLCQklSbGys1/NiY2M9jwWD6uRQWFioRo0aqVmzZn7nNHQnT57U5MmTlZaWppiYGEnBncvcuXMVFhamhx56yOfjwZjNwYMHdezYMc2ZM0cDBgzQmjVrdMsttyg1NVXr16+XFJy5VHj22Wd12WWXqU2bNmrUqJEGDBig559/Xr169ZLU8LMxxmjChAnq1auXkpOTJXH8reArm18KxmOwv1yC6fhr5eMqa9Jrr72mV155RTk5OercubM+/fRTpaenKyEhQSNHjvTMczgcXs8zxlQaCwZnk0OwZOV2uzV06FCVl5fr+eefr3J+Q8/lk08+0TPPPKP8/PyAX2dDzqbizYc33XSTxo8fL0m68sortWnTJi1cuFC9e/f2+9yGnEuFZ599Vlu2bNGKFSvUvn17ffDBBxo9erTi4+N1ww03+H1eQ8lm7Nix+vzzz7Vx48ZKjwX78fdM2UjBewz2lUuwHX/r/JnNxx57TJMnT9bQoUN1+eWXa/jw4Ro/frxmz54tSYqLi5OkSi3/4MGDlf6V2ZBVJ4e4uDidOnVKR44c8TunoXK73brttttUUFCg3Nxcz7+opeDNZcOGDTp48KDatWunsLAwhYWFaffu3XrkkUeUmJgoKTizadmypcLCwnTZZZd5jXfq1MnzbvRgzEWSTpw4oalTpyorK0s33nijunTporFjx+r222/Xf/3Xf0lq2NmMGzdOK1as0Lp169SmTRvPOMdf/9lUCNZjsL9cgu34W+fLZklJiUJCvJcZGhrqOfuQlJSkuLg45ebmeh4/deqU1q9frx49epzXtdam6uRw9dVXKzw83GvOgQMH9OWXXzborCoOcl9//bXeffddtWjRwuvxYM1l+PDh+vzzz/Xpp596bgkJCXrssce0evVqScGZTaNGjXTNNddU+jUlO3fuVPv27SUFZy7ST3+X3G73GY/JDTEbY4zGjh2rZcuWae3atUpKSvJ6PJiPv1VlIwXnMbiqXILu+Hu+35EUqJEjR5rWrVubt99+2xQUFJhly5aZli1bmokTJ3rmzJkzxzRp0sQsW7bMfPHFF2bYsGEmPj7eFBcX1+LKa97Ro0fN1q1bzdatW40kk5WVZbZu3ep5R191cnjggQdMmzZtzLvvvmvy8/PNf/zHf5grrrjClJWV1dbLOmdnysXtdpshQ4aYNm3amE8//dQcOHDAcystLfVsoyHmYkzVXzO/9Mt3QxrTMLOpKpdly5aZ8PBws2jRIvP111+b5557zoSGhpoNGzZ4ttEQczGm6mx69+5tOnfubNatW2e+/fZbs3jxYhMREWGef/55zzYaWjYPPvigadKkiXn//fe9jiElJSWeOcF6/K0qm2A9Blfna+aXGvLxt86XzeLiYvPwww+bdu3amYiICHPRRReZadOmeX2RlpeXm+nTp5u4uDjjdDrN9ddfb7744otaXLUd69atM5Iq3UaOHGmMqV4OJ06cMGPHjjXNmzc3kZGRZvDgwWbPnj218GpqzplyKSgo8PmYJLNu3TrPNhpiLsZU/TXzS74Odg0xm+rk8uKLL5qLL77YREREmCuuuMIsX77caxsNMRdjqs7mwIEDZtSoUSYhIcFERESYSy+91Pzxj3805eXlnm00tGz8HUMWL17smROsx9+qsgnWY3B1vmZ+qSEffx3GGFMz50gBAAAAb3X+ZzYBAABQf1E2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgzf8DRIrv0NXkLJUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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dz5dhGMb9999vfs5ddNFFxoABA8xEYRj2PF9WIm+EBvKGfT6HyBv2Ol/kjMZH3ggN5A37fA6RN+x1vkItb7gMwzDqf50VAAAAAAAAcH5sNcYUAAAAAAAAnIPCFAAAAAAAACxBYQoAAAAAAACWoDAFAAAAAAAAS1CYAgAAAAAAgCUoTAEAAAAAAMASFKYAAAAAAABgCQpTAAAAAAAAsASFKQAAAAAAAFiCwhQAAAAAAAAsQWEKAAAAAAAAlvj/UhmubGD//t8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1200x300 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# define big M constant for chance constraints counterparts\n",
-    "bigM = 100\n",
-    "\n",
-    "\n",
-    "def airline_final(demand):\n",
-    "    m = pyo.ConcreteModel()\n",
-    "\n",
-    "    m.CLASSES = pyo.Set(initialize=demand.columns)\n",
-    "    m.SCENARIOS = pyo.Set(initialize=demand.index)\n",
-    "\n",
-    "    # first stage variables and constraints\n",
-    "    m.seats = pyo.Var(m.CLASSES, domain=pyo.NonNegativeIntegers)\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES)\n",
-    "    def plane_seats(m, c):\n",
-    "        return sum(m.seats[c] * seat_factor[c] for c in m.CLASSES) <= capacity\n",
-    "\n",
-    "    # second stage variable and constraints\n",
-    "    m.tickets = pyo.Var(m.CLASSES, m.SCENARIOS, domain=pyo.NonNegativeIntegers)\n",
-    "    m.first_class = pyo.Var(m.SCENARIOS, domain=pyo.Binary)\n",
-    "    m.business_class = pyo.Var(m.SCENARIOS, domain=pyo.Binary)\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES, m.SCENARIOS)\n",
-    "    def demand_limits(m, c, s):\n",
-    "        return m.tickets[c, s] <= demand[c][s]\n",
-    "\n",
-    "    @m.Constraint(m.CLASSES, m.SCENARIOS)\n",
-    "    def seat_limits(m, c, s):\n",
-    "        return m.tickets[c, s] <= m.seats[c]\n",
-    "\n",
-    "    @m.Constraint(m.SCENARIOS)\n",
-    "    def first_class_loyality(m, s):\n",
-    "        return m.seats[\"F\"] + bigM * m.first_class[s] >= demand[\"F\"][s]\n",
-    "\n",
-    "    @m.Constraint()\n",
-    "    def first_class_loyality_rate(m):\n",
-    "        return sum(m.first_class[s] for s in m.SCENARIOS) <= 0.02 * len(\n",
-    "            m.SCENARIOS\n",
-    "        )\n",
-    "\n",
-    "    @m.Constraint(m.SCENARIOS)\n",
-    "    def business_class_loyality(m, s):\n",
-    "        return (\n",
-    "            m.seats[\"F\"] + m.seats[\"B\"] + bigM * m.business_class[s]\n",
-    "            >= demand[\"B\"][s] + demand[\"F\"][s]\n",
-    "        )\n",
-    "\n",
-    "    @m.Constraint()\n",
-    "    def business_class_loyality_rate(m):\n",
-    "        return sum(m.business_class[s] for s in m.SCENARIOS) <= 0.05 * len(\n",
-    "            m.SCENARIOS\n",
-    "        )\n",
-    "\n",
-    "    # objective\n",
-    "    @m.Objective(sense=pyo.maximize)\n",
-    "    def revenue(m):\n",
-    "        return sum(\n",
-    "            m.tickets[c, s] * revenue_factor[c]\n",
-    "            for c in m.CLASSES\n",
-    "            for s in m.SCENARIOS\n",
-    "        )\n",
-    "\n",
-    "    return m\n",
-    "\n",
-    "\n",
-    "# create model\n",
-    "model = airline_final(demand_saa)\n",
-    "seats = airline_solve(model)\n",
-    "seat_report_saa(seats, demand_saa)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1f7f29d-0373-4b4e-a1c1-27594905464a",
-   "metadata": {},
-   "source": [
-    "**Exercise**\n",
-    "\n",
-    "Compared the results of using positive correlation in demand for first and business-class tickets results in lower expected revenue. What happens if there is no correlation, or there is negative correlation? Verify your predictions by simulation."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
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-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
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-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
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-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/genindex.html b/genindex.html
index 731b4d60..2d0981fb 100644
--- a/genindex.html
+++ b/genindex.html
@@ -185,9 +185,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -196,6 +196,10 @@
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="notebooks/03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -203,62 +207,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -443,49 +446,47 @@ <h2 id="A">A</h2>
     application
 
       <ul>
-        <li><a href="notebooks/10/airline-seating.html#index-0">airline seating allocation</a>
-</li>
-        <li><a href="notebooks/05/svm.html#index-3">binary classification</a>, <a href="notebooks/06/svm-conic.html#index-4">[1]</a>
+        <li><a href="notebooks/05/04-svm-binary-classification.html#index-3">binary classification</a>, <a href="notebooks/06/05-svm-conic.html#index-4">[1]</a>
 </li>
-        <li><a href="notebooks/06/building-insulation.html#index-4">building insulation</a>
+        <li><a href="notebooks/06/04-building-insulation.html#index-4">building insulation</a>
 </li>
-        <li><a href="notebooks/05/svm.html#index-4">counterfeit banknotes</a>, <a href="notebooks/06/svm-conic.html#index-5">[1]</a>
+        <li><a href="notebooks/05/04-svm-binary-classification.html#index-4">counterfeit banknotes</a>, <a href="notebooks/06/05-svm-conic.html#index-5">[1]</a>
 </li>
-        <li><a href="notebooks/04/cryptocurrency-arbitrage.html#index-0">cryptocurrency arbitrage</a>
+        <li><a href="notebooks/04/05-cryptocurrency-arbitrage.html#index-0">cryptocurrency arbitrage</a>
 </li>
-        <li><a href="notebooks/02/07-bim-rawmaterialplanning.html#index-5">demand forecasts</a>
+        <li><a href="notebooks/02/07-bim-demand-forecast.html#index-5">demand forecasts</a>
 </li>
         <li><a href="notebooks/03/03-recharging-electric-vehicle.html#index-1">electric vehicles</a>
 </li>
-        <li><a href="notebooks/04/power-network.html#index-0">energy systems</a>, <a href="notebooks/10/opf-ldr.html#index-0">[1]</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-0">[2]</a>
+        <li><a href="notebooks/04/06-power-network.html#index-0">energy systems</a>, <a href="notebooks/10/02-opf-linear-decision-rule.html#index-0">[1]</a>, <a href="notebooks/10/05-opf-wind-curtailment.html#index-0">[2]</a>
 </li>
-        <li><a href="notebooks/07/fleet-assignment.html#index-0">fleet assignment</a>
+        <li><a href="notebooks/07/01-fleet-assignment.html#index-0">fleet assignment</a>
 </li>
-        <li><a href="notebooks/06/economic-order-quantity.html#index-4">inventory management</a>, <a href="notebooks/09/seafood.html#index-0">[1]</a>
+        <li><a href="notebooks/06/01-economic-order-quantity.html#index-4">inventory management</a>, <a href="notebooks/09/03-seafood-distribution-center.html#index-0">[1]</a>
 </li>
-        <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-1">investment</a>, <a href="notebooks/05/markowitz_portfolio.html#index-1">[1]</a>, <a href="notebooks/06/investment-wheel.html#index-5">[2]</a>, <a href="notebooks/06/kelly-criterion.html#index-5">[3]</a>, <a href="notebooks/06/markowitz_portfolio_revisited.html#index-1">[4]</a>, <a href="notebooks/09/markowitz_portfolio_with_chance_constraint.html#index-1">[5]</a>
+        <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-1">investment</a>, <a href="notebooks/05/03-markowitz-portfolio.html#index-1">[1]</a>, <a href="notebooks/06/02-kelly-criterion.html#index-5">[2]</a>, <a href="notebooks/06/03-markowitz-portfolio-revisited.html#index-1">[3]</a>, <a href="notebooks/06/06-investment-wheel.html#index-5">[4]</a>, <a href="notebooks/09/02-markowitz-portfolio-with-chance-constraint.html#index-1">[5]</a>
 </li>
         <li><a href="notebooks/03/10-job-shop-scheduling.html#index-2">job shop</a>
 </li>
         <li><a href="notebooks/03/11-maintenance-planning.html#index-2">maintenance planning</a>
 </li>
-        <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-0">portfolio</a>, <a href="notebooks/05/markowitz_portfolio.html#index-0">[1]</a>, <a href="notebooks/06/investment-wheel.html#index-4">[2]</a>, <a href="notebooks/06/kelly-criterion.html#index-4">[3]</a>, <a href="notebooks/06/markowitz_portfolio_revisited.html#index-0">[4]</a>, <a href="notebooks/06/optimal-growth-portfolios.html#index-4">[5]</a>, <a href="notebooks/09/markowitz_portfolio_with_chance_constraint.html#index-0">[6]</a>
+        <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-0">portfolio</a>, <a href="notebooks/05/03-markowitz-portfolio.html#index-0">[1]</a>, <a href="notebooks/06/02-kelly-criterion.html#index-4">[2]</a>, <a href="notebooks/06/03-markowitz-portfolio-revisited.html#index-0">[3]</a>, <a href="notebooks/06/06-investment-wheel.html#index-4">[4]</a>, <a href="notebooks/06/07-optimal-growth-portfolios.html#index-4">[5]</a>, <a href="notebooks/09/02-markowitz-portfolio-with-chance-constraint.html#index-0">[6]</a>
 </li>
-        <li><a href="notebooks/02/01-bim.html#index-3">production planning</a>, <a href="notebooks/02/04-bim-dual.html#index-2">[1]</a>, <a href="notebooks/02/07-bim-rawmaterialplanning.html#index-4">[2]</a>, <a href="notebooks/02/09-multiproductionfaciliity_worstcase.html#index-2">[3]</a>, <a href="notebooks/03/01-bim-perturbed.html#index-1">[4]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-0">[5]</a>, <a href="notebooks/10/ccg.html#index-1">[6]</a>
+        <li><a href="notebooks/02/01-bim.html#index-3">production planning</a>, <a href="notebooks/02/04-bim-dual.html#index-2">[1]</a>, <a href="notebooks/02/07-bim-demand-forecast.html#index-4">[2]</a>, <a href="notebooks/02/09-production-faciliity-worst-case.html#index-2">[3]</a>, <a href="notebooks/03/01-bim-perturbed.html#index-1">[4]</a>, <a href="notebooks/03/07-bim-production-revisited.html#index-4">[5]</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-0">[6]</a>, <a href="notebooks/10/03-two-stage-production-planning.html#index-1">[7]</a>
 </li>
-        <li><a href="notebooks/09/pop-up_shop.html#index-0">profit maximization</a>
+        <li><a href="notebooks/09/01-pop-up-shop.html#index-0">profit maximization</a>
 </li>
-        <li><a href="notebooks/02/02-lad-regression.html#index-0">regression</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-0">[1]</a>, <a href="notebooks/05/ols-regression.html#index-0">[2]</a>
+        <li><a href="notebooks/02/02-lad-regression.html#index-0">regression</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-0">[1]</a>, <a href="notebooks/05/02-ols-regression.html#index-0">[2]</a>
 </li>
-        <li><a href="notebooks/10/farmer.html#index-0">resource allocation</a>
+        <li><a href="notebooks/10/04-farmer-problem.html#index-0">resource allocation</a>
 </li>
         <li><a href="notebooks/03/02-shift-scheduling.html#index-3">scheduling</a>, <a href="notebooks/03/05-machine-scheduling.html#index-1">[1]</a>, <a href="notebooks/03/10-job-shop-scheduling.html#index-3">[2]</a>
 </li>
-        <li><a href="notebooks/04/dinner-seat-allocation.html#index-0">seating allocation</a>
+        <li><a href="notebooks/04/01-dinner-seat-allocation.html#index-0">seating allocation</a>
 </li>
-        <li><a href="notebooks/05/svm.html#index-2">support vector machines</a>, <a href="notebooks/06/svm-conic.html#index-3">[1]</a>
+        <li><a href="notebooks/05/04-svm-binary-classification.html#index-2">support vector machines</a>, <a href="notebooks/06/05-svm-conic.html#index-3">[1]</a>
 </li>
-        <li><a href="notebooks/04/gasoline-distribution.html#index-0">transportation</a>
+        <li><a href="notebooks/04/03-gasoline-distribution.html#index-0">transportation</a>
 </li>
       </ul></li>
   </ul></td>
@@ -494,7 +495,7 @@ <h2 id="A">A</h2>
 <h2 id="B">B</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/05/milk-pooling.html#index-1">bilinear constraints</a>
+      <li><a href="notebooks/05/01-milk-pooling.html#index-1">bilinear constraints</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -506,19 +507,19 @@ <h2 id="B">B</h2>
 <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/09/economicdispatch.html#index-4">chance constraints</a>, <a href="notebooks/09/markowitz_portfolio_with_chance_constraint.html#index-3">[1]</a>, <a href="notebooks/10/airline-seating.html#index-5">[2]</a>
+      <li><a href="notebooks/09/02-markowitz-portfolio-with-chance-constraint.html#index-3">chance constraints</a>, <a href="notebooks/09/04-economic-dispatch.html#index-4">[1]</a>
 </li>
-      <li><a href="notebooks/06/svm-conic.html#index-2">conic optimization</a>
+      <li><a href="notebooks/06/05-svm-conic.html#index-2">conic optimization</a>
 
       <ul>
-        <li><a href="notebooks/06/investment-wheel.html#index-2">exponential cones</a>, <a href="notebooks/06/kelly-criterion.html#index-2">[1]</a>, <a href="notebooks/06/optimal-growth-portfolios.html#index-2">[2]</a>
+        <li><a href="notebooks/06/02-kelly-criterion.html#index-2">exponential cones</a>, <a href="notebooks/06/06-investment-wheel.html#index-2">[1]</a>, <a href="notebooks/06/07-optimal-growth-portfolios.html#index-2">[2]</a>
 </li>
-        <li><a href="notebooks/06/building-insulation.html#index-2">second order cones</a>, <a href="notebooks/06/economic-order-quantity.html#index-1">[1]</a>
+        <li><a href="notebooks/06/01-economic-order-quantity.html#index-1">second order cones</a>, <a href="notebooks/06/04-building-insulation.html#index-2">[1]</a>
 </li>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/10/ccg.html#index-6">constraint and column generation</a>
+      <li><a href="notebooks/10/03-two-stage-production-planning.html#index-6">constraint and column generation</a>
 </li>
   </ul></td>
 </tr></table>
@@ -538,7 +539,7 @@ <h2 id="D">D</h2>
 <h2 id="F">F</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/04/dinner-seat-allocation.html#index-7">feasibility problem</a>
+      <li><a href="notebooks/04/01-dinner-seat-allocation.html#index-7">feasibility problem</a>
 </li>
   </ul></td>
 </tr></table>
@@ -546,11 +547,11 @@ <h2 id="F">F</h2>
 <h2 id="G">G</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/03/02-shift-scheduling.html#index-4">Gantt charts</a>, <a href="notebooks/03/05-machine-scheduling.html#index-3">[1]</a>, <a href="notebooks/03/10-job-shop-scheduling.html#index-0">[2]</a>, <a href="notebooks/07/fleet-assignment.html#index-9">[3]</a>
+      <li><a href="notebooks/03/02-shift-scheduling.html#index-4">Gantt charts</a>, <a href="notebooks/03/05-machine-scheduling.html#index-3">[1]</a>, <a href="notebooks/03/10-job-shop-scheduling.html#index-0">[2]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-9">[3]</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/04/graph-coloring.html#index-3">graph coloring</a>
+      <li><a href="notebooks/04/04-exam-room-scheduling.html#index-3">graph coloring</a>
 </li>
   </ul></td>
 </tr></table>
@@ -558,11 +559,11 @@ <h2 id="G">G</h2>
 <h2 id="L">L</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/10/opf-ldr.html#index-6">linear decision rules</a>
+      <li><a href="notebooks/10/02-opf-linear-decision-rule.html#index-6">linear decision rules</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/04/graph-coloring.html#index-0">logistics</a>, <a href="notebooks/04/mincost-flow.html#index-0">[1]</a>
+      <li><a href="notebooks/04/02-mincost-flow.html#index-0">logistics</a>, <a href="notebooks/04/04-exam-room-scheduling.html#index-0">[1]</a>
 </li>
   </ul></td>
 </tr></table>
@@ -570,15 +571,15 @@ <h2 id="L">L</h2>
 <h2 id="M">M</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/04/dinner-seat-allocation.html#index-5">max flow problem</a>
+      <li><a href="notebooks/04/01-dinner-seat-allocation.html#index-5">max flow problem</a>
 </li>
-      <li><a href="notebooks/05/milk-pooling.html#index-2">McCormick envelopes</a>
+      <li><a href="notebooks/05/01-milk-pooling.html#index-2">McCormick envelopes</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/04/mincost-flow.html#index-3">min-cost flow problem</a>
+      <li><a href="notebooks/04/02-mincost-flow.html#index-3">min-cost flow problem</a>
 </li>
-      <li><a href="notebooks/09/economicdispatch.html#index-5">mutable parameters</a>
+      <li><a href="notebooks/09/04-economic-dispatch.html#index-5">mutable parameters</a>
 </li>
   </ul></td>
 </tr></table>
@@ -586,11 +587,11 @@ <h2 id="M">M</h2>
 <h2 id="N">N</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/04/cryptocurrency-arbitrage.html#index-5">network optimization</a>, <a href="notebooks/04/dinner-seat-allocation.html#index-4">[1]</a>, <a href="notebooks/04/gasoline-distribution.html#index-3">[2]</a>, <a href="notebooks/04/graph-coloring.html#index-2">[3]</a>, <a href="notebooks/04/mincost-flow.html#index-2">[4]</a>, <a href="notebooks/04/power-network.html#index-3">[5]</a>, <a href="notebooks/10/opf-ldr.html#index-3">[6]</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-3">[7]</a>
+      <li><a href="notebooks/04/01-dinner-seat-allocation.html#index-4">network optimization</a>, <a href="notebooks/04/02-mincost-flow.html#index-2">[1]</a>, <a href="notebooks/04/03-gasoline-distribution.html#index-3">[2]</a>, <a href="notebooks/04/04-exam-room-scheduling.html#index-2">[3]</a>, <a href="notebooks/04/05-cryptocurrency-arbitrage.html#index-5">[4]</a>, <a href="notebooks/04/06-power-network.html#index-3">[5]</a>, <a href="notebooks/10/02-opf-linear-decision-rule.html#index-3">[6]</a>, <a href="notebooks/10/05-opf-wind-curtailment.html#index-3">[7]</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/04/cryptocurrency-arbitrage.html#index-4">networkx</a>, <a href="notebooks/04/dinner-seat-allocation.html#index-6">[1]</a>, <a href="notebooks/04/graph-coloring.html#index-4">[2]</a>, <a href="notebooks/04/mincost-flow.html#index-4">[3]</a>, <a href="notebooks/04/power-network.html#index-4">[4]</a>, <a href="notebooks/07/fleet-assignment.html#index-8">[5]</a>
+      <li><a href="notebooks/04/01-dinner-seat-allocation.html#index-6">networkx</a>, <a href="notebooks/04/02-mincost-flow.html#index-4">[1]</a>, <a href="notebooks/04/04-exam-room-scheduling.html#index-4">[2]</a>, <a href="notebooks/04/05-cryptocurrency-arbitrage.html#index-4">[3]</a>, <a href="notebooks/04/06-power-network.html#index-4">[4]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-8">[5]</a>
 </li>
   </ul></td>
 </tr></table>
@@ -598,35 +599,35 @@ <h2 id="N">N</h2>
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-4">pandas dataframe</a>, <a href="notebooks/02/07-bim-rawmaterialplanning.html#index-6">[1]</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-1">[2]</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-2">[3]</a>, <a href="notebooks/02/09-multiproductionfaciliity_worstcase.html#index-5">[4]</a>, <a href="notebooks/03/03-recharging-electric-vehicle.html#index-4">[5]</a>, <a href="notebooks/04/cryptocurrency-arbitrage.html#index-3">[6]</a>, <a href="notebooks/04/gasoline-distribution.html#index-2">[7]</a>, <a href="notebooks/04/power-network.html#index-2">[8]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-3">[9]</a>, <a href="notebooks/07/fleet-assignment.html#index-3">[10]</a>, <a href="notebooks/09/economicdispatch.html#index-1">[11]</a>, <a href="notebooks/09/pop-up_shop.html#index-2">[12]</a>, <a href="notebooks/09/seafood.html#index-2">[13]</a>, <a href="notebooks/10/airline-seating.html#index-2">[14]</a>, <a href="notebooks/10/farmer.html#index-2">[15]</a>, <a href="notebooks/10/opf-ldr.html#index-2">[16]</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-2">[17]</a>
+      <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-4">pandas dataframe</a>, <a href="notebooks/02/07-bim-demand-forecast.html#index-6">[1]</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-1">[2]</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-2">[3]</a>, <a href="notebooks/02/09-production-faciliity-worst-case.html#index-5">[4]</a>, <a href="notebooks/03/03-recharging-electric-vehicle.html#index-4">[5]</a>, <a href="notebooks/03/07-bim-production-revisited.html#index-5">[6]</a>, <a href="notebooks/04/03-gasoline-distribution.html#index-2">[7]</a>, <a href="notebooks/04/05-cryptocurrency-arbitrage.html#index-3">[8]</a>, <a href="notebooks/04/06-power-network.html#index-2">[9]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-3">[10]</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-3">[11]</a>, <a href="notebooks/09/01-pop-up-shop.html#index-2">[12]</a>, <a href="notebooks/09/03-seafood-distribution-center.html#index-2">[13]</a>, <a href="notebooks/09/04-economic-dispatch.html#index-1">[14]</a>, <a href="notebooks/10/02-opf-linear-decision-rule.html#index-2">[15]</a>, <a href="notebooks/10/04-farmer-problem.html#index-2">[16]</a>, <a href="notebooks/10/05-opf-wind-curtailment.html#index-2">[17]</a>
 </li>
       <li>
     Pyomo
 
       <ul>
-        <li><a href="notebooks/05/milk-pooling.html#index-0">block</a>, <a href="notebooks/05/milk-pooling.html#index-3">[1]</a>, <a href="notebooks/05/milk-pooling.html#index-4">[2]</a>, <a href="notebooks/06/building-insulation.html#index-0">[3]</a>, <a href="notebooks/06/economic-order-quantity.html#index-0">[4]</a>, <a href="notebooks/06/investment-wheel.html#index-0">[5]</a>, <a href="notebooks/06/kelly-criterion.html#index-0">[6]</a>, <a href="notebooks/06/optimal-growth-portfolios.html#index-0">[7]</a>, <a href="notebooks/10/ccg.html#index-0">[8]</a>
+        <li><a href="notebooks/03/07-bim-production-revisited.html#index-0">block</a>, <a href="notebooks/05/01-milk-pooling.html#index-0">[1]</a>, <a href="notebooks/05/01-milk-pooling.html#index-3">[2]</a>, <a href="notebooks/05/01-milk-pooling.html#index-4">[3]</a>, <a href="notebooks/06/01-economic-order-quantity.html#index-0">[4]</a>, <a href="notebooks/06/02-kelly-criterion.html#index-0">[5]</a>, <a href="notebooks/06/04-building-insulation.html#index-0">[6]</a>, <a href="notebooks/06/06-investment-wheel.html#index-0">[7]</a>, <a href="notebooks/06/07-optimal-growth-portfolios.html#index-0">[8]</a>, <a href="notebooks/10/03-two-stage-production-planning.html#index-0">[9]</a>
 </li>
         <li><a href="notebooks/03/09-strip-packing.html#index-2">boolean</a>
 </li>
         <li><a href="notebooks/01/02-production-planning-basic.html#index-3">constraints</a>
 </li>
-        <li><a href="notebooks/01/01-production-planning.html#index-3">decorators</a>, <a href="notebooks/01/02-production-planning-basic.html#index-4">[1]</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-3">[2]</a>, <a href="notebooks/02/07-bim-rawmaterialplanning.html#index-1">[3]</a>
+        <li><a href="notebooks/01/01-production-planning.html#index-3">decorators</a>, <a href="notebooks/01/02-production-planning-basic.html#index-4">[1]</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-3">[2]</a>, <a href="notebooks/02/07-bim-demand-forecast.html#index-1">[3]</a>
 </li>
-        <li><a href="notebooks/04/gasoline-distribution.html#index-4">display</a>
+        <li><a href="notebooks/04/03-gasoline-distribution.html#index-4">display</a>
 </li>
-        <li><a href="notebooks/01/01-production-planning.html#index-1">expressions</a>, <a href="notebooks/01/02-production-planning-basic.html#index-1">[1]</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-1">[2]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-6">[3]</a>, <a href="notebooks/07/fleet-assignment.html#index-6">[4]</a>
+        <li><a href="notebooks/01/01-production-planning.html#index-1">expressions</a>, <a href="notebooks/01/02-production-planning-basic.html#index-1">[1]</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-1">[2]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-6">[3]</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-6">[4]</a>
 </li>
-        <li><a href="notebooks/05/svm.html#index-0">kernel library</a>, <a href="notebooks/06/building-insulation.html#index-1">[1]</a>, <a href="notebooks/06/economic-order-quantity.html#index-2">[2]</a>, <a href="notebooks/06/investment-wheel.html#index-1">[3]</a>, <a href="notebooks/06/kelly-criterion.html#index-1">[4]</a>, <a href="notebooks/06/optimal-growth-portfolios.html#index-1">[5]</a>, <a href="notebooks/06/svm-conic.html#index-1">[6]</a>
+        <li><a href="notebooks/05/04-svm-binary-classification.html#index-0">kernel library</a>, <a href="notebooks/06/01-economic-order-quantity.html#index-2">[1]</a>, <a href="notebooks/06/02-kelly-criterion.html#index-1">[2]</a>, <a href="notebooks/06/04-building-insulation.html#index-1">[3]</a>, <a href="notebooks/06/05-svm-conic.html#index-1">[4]</a>, <a href="notebooks/06/06-investment-wheel.html#index-1">[5]</a>, <a href="notebooks/06/07-optimal-growth-portfolios.html#index-1">[6]</a>
 </li>
         <li><a href="notebooks/01/02-production-planning-basic.html#index-2">objectives</a>
 </li>
-        <li><a href="notebooks/02/09-multiproductionfaciliity_worstcase.html#index-1">parameters</a>, <a href="notebooks/03/02-shift-scheduling.html#index-1">[1]</a>, <a href="notebooks/04/dinner-seat-allocation.html#index-2">[2]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-4">[3]</a>, <a href="notebooks/07/fleet-assignment.html#index-4">[4]</a>
+        <li><a href="notebooks/02/09-production-faciliity-worst-case.html#index-1">parameters</a>, <a href="notebooks/03/02-shift-scheduling.html#index-1">[1]</a>, <a href="notebooks/03/07-bim-production-revisited.html#index-2">[2]</a>, <a href="notebooks/04/01-dinner-seat-allocation.html#index-2">[3]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-4">[4]</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-4">[5]</a>
 </li>
-        <li><a href="notebooks/10/ccg.html#index-3">persistent solvers</a>
+        <li><a href="notebooks/10/03-two-stage-production-planning.html#index-3">persistent solvers</a>
 </li>
         <li><a href="notebooks/03/03-recharging-electric-vehicle.html#index-3">RangeSet</a>
 </li>
-        <li><a href="notebooks/01/01-production-planning.html#index-2">sets</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-2">[1]</a>, <a href="notebooks/02/07-bim-rawmaterialplanning.html#index-0">[2]</a>, <a href="notebooks/02/09-multiproductionfaciliity_worstcase.html#index-0">[3]</a>, <a href="notebooks/03/02-shift-scheduling.html#index-0">[4]</a>, <a href="notebooks/04/dinner-seat-allocation.html#index-3">[5]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-5">[6]</a>, <a href="notebooks/07/fleet-assignment.html#index-5">[7]</a>, <a href="notebooks/10/airline-seating.html#index-3">[8]</a>, <a href="notebooks/10/farmer.html#index-3">[9]</a>
+        <li><a href="notebooks/01/01-production-planning.html#index-2">sets</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-2">[1]</a>, <a href="notebooks/02/07-bim-demand-forecast.html#index-0">[2]</a>, <a href="notebooks/02/09-production-faciliity-worst-case.html#index-0">[3]</a>, <a href="notebooks/03/02-shift-scheduling.html#index-0">[4]</a>, <a href="notebooks/03/07-bim-production-revisited.html#index-1">[5]</a>, <a href="notebooks/04/01-dinner-seat-allocation.html#index-3">[6]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-5">[7]</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-5">[8]</a>, <a href="notebooks/10/04-farmer-problem.html#index-3">[9]</a>
 </li>
         <li><a href="notebooks/01/01-production-planning.html#index-0">variables</a>, <a href="notebooks/01/02-production-planning-basic.html#index-0">[1]</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-0">[2]</a>
 </li>
@@ -637,7 +638,7 @@ <h2 id="P">P</h2>
 <h2 id="Q">Q</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/05/svm.html#index-1">quadratic optimization</a>
+      <li><a href="notebooks/05/04-svm-binary-classification.html#index-1">quadratic optimization</a>
 </li>
   </ul></td>
 </tr></table>
@@ -645,7 +646,7 @@ <h2 id="Q">Q</h2>
 <h2 id="R">R</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/10/ccg.html#index-4">robust optimization</a>
+      <li><a href="notebooks/10/03-two-stage-production-planning.html#index-4">robust optimization</a>
 </li>
   </ul></td>
 </tr></table>
@@ -653,33 +654,33 @@ <h2 id="R">R</h2>
 <h2 id="S">S</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/10/opf-ldr.html#index-5">SAA</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-5">[1]</a>
+      <li><a href="notebooks/10/02-opf-linear-decision-rule.html#index-5">SAA</a>, <a href="notebooks/10/05-opf-wind-curtailment.html#index-5">[1]</a>
 </li>
-      <li><a href="notebooks/09/economicdispatch.html#index-2">sample average approximation</a>, <a href="notebooks/09/seafood.html#index-3">[1]</a>, <a href="notebooks/10/airline-seating.html#index-6">[2]</a>
+      <li><a href="notebooks/09/03-seafood-distribution-center.html#index-3">sample average approximation</a>, <a href="notebooks/09/04-economic-dispatch.html#index-2">[1]</a>
 </li>
-      <li><a href="notebooks/05/ols-regression.html#index-1">scatterplot</a>
+      <li><a href="notebooks/05/02-ols-regression.html#index-1">scatterplot</a>
 </li>
-      <li><a href="notebooks/04/power-network.html#index-5">scenario analysis</a>
+      <li><a href="notebooks/04/06-power-network.html#index-5">scenario analysis</a>
 </li>
-      <li><a href="notebooks/07/bim-robustness-analysis.html#index-7">simulations</a>, <a href="notebooks/07/fleet-assignment.html#index-7">[1]</a>, <a href="notebooks/09/seafood.html#index-5">[2]</a>
+      <li><a href="notebooks/07/01-fleet-assignment.html#index-7">simulations</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-7">[1]</a>, <a href="notebooks/09/03-seafood-distribution-center.html#index-5">[2]</a>
 </li>
       <li>
     solver
 
       <ul>
-        <li><a href="notebooks/01/01-production-planning.html#index-4">cbc</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-4">[1]</a>, <a href="notebooks/02/01-bim.html#index-1">[2]</a>, <a href="notebooks/02/03-mad-portfolio-optimization.html#index-2">[3]</a>, <a href="notebooks/02/04-bim-dual.html#index-1">[4]</a>, <a href="notebooks/02/05-bim-maxmin.html#index-0">[5]</a>, <a href="notebooks/02/06-bim-fractional.html#index-0">[6]</a>, <a href="notebooks/02/07-bim-rawmaterialplanning.html#index-2">[7]</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-3">[8]</a>, <a href="notebooks/02/09-multiproductionfaciliity_worstcase.html#index-3">[9]</a>, <a href="notebooks/03/01-bim-perturbed.html#index-0">[10]</a>, <a href="notebooks/03/02-shift-scheduling.html#index-2">[11]</a>, <a href="notebooks/03/03-recharging-electric-vehicle.html#index-2">[12]</a>, <a href="notebooks/03/10-job-shop-scheduling.html#index-4">[13]</a>, <a href="notebooks/03/11-maintenance-planning.html#index-1">[14]</a>, <a href="notebooks/04/cryptocurrency-arbitrage.html#index-1">[15]</a>, <a href="notebooks/04/dinner-seat-allocation.html#index-1">[16]</a>, <a href="notebooks/04/power-network.html#index-1">[17]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-1">[18]</a>, <a href="notebooks/07/fleet-assignment.html#index-1">[19]</a>, <a href="notebooks/09/economicdispatch.html#index-0">[20]</a>, <a href="notebooks/09/pop-up_shop.html#index-1">[21]</a>, <a href="notebooks/09/seafood.html#index-1">[22]</a>, <a href="notebooks/10/airline-seating.html#index-1">[23]</a>, <a href="notebooks/10/ccg.html#index-2">[24]</a>, <a href="notebooks/10/farmer.html#index-1">[25]</a>, <a href="notebooks/10/opf-ldr.html#index-1">[26]</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-1">[27]</a>
+        <li><a href="notebooks/01/01-production-planning.html#index-4">cbc</a>, <a href="notebooks/01/03-production-planning-advanced.html#index-4">[1]</a>, <a href="notebooks/02/01-bim.html#index-1">[2]</a>, <a href="notebooks/02/03-mad-portfolio-optimization.html#index-2">[3]</a>, <a href="notebooks/02/04-bim-dual.html#index-1">[4]</a>, <a href="notebooks/02/05-bim-maxmin.html#index-0">[5]</a>, <a href="notebooks/02/06-bim-fractional.html#index-0">[6]</a>, <a href="notebooks/02/07-bim-demand-forecast.html#index-2">[7]</a>, <a href="notebooks/02/08-L1-regression-wine-quality.html#index-3">[8]</a>, <a href="notebooks/02/09-production-faciliity-worst-case.html#index-3">[9]</a>, <a href="notebooks/03/01-bim-perturbed.html#index-0">[10]</a>, <a href="notebooks/03/02-shift-scheduling.html#index-2">[11]</a>, <a href="notebooks/03/03-recharging-electric-vehicle.html#index-2">[12]</a>, <a href="notebooks/03/07-bim-production-revisited.html#index-3">[13]</a>, <a href="notebooks/03/10-job-shop-scheduling.html#index-4">[14]</a>, <a href="notebooks/03/11-maintenance-planning.html#index-1">[15]</a>, <a href="notebooks/04/01-dinner-seat-allocation.html#index-1">[16]</a>, <a href="notebooks/04/05-cryptocurrency-arbitrage.html#index-1">[17]</a>, <a href="notebooks/04/06-power-network.html#index-1">[18]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-1">[19]</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-1">[20]</a>, <a href="notebooks/09/01-pop-up-shop.html#index-1">[21]</a>, <a href="notebooks/09/03-seafood-distribution-center.html#index-1">[22]</a>, <a href="notebooks/09/04-economic-dispatch.html#index-0">[23]</a>, <a href="notebooks/10/02-opf-linear-decision-rule.html#index-1">[24]</a>, <a href="notebooks/10/04-farmer-problem.html#index-1">[25]</a>, <a href="notebooks/10/05-opf-wind-curtailment.html#index-1">[26]</a>
 </li>
-        <li><a href="notebooks/05/markowitz_portfolio.html#index-2">cplex</a>, <a href="notebooks/06/markowitz_portfolio_revisited.html#index-2">[1]</a>, <a href="notebooks/09/markowitz_portfolio_with_chance_constraint.html#index-2">[2]</a>
+        <li><a href="notebooks/05/03-markowitz-portfolio.html#index-2">cplex</a>, <a href="notebooks/06/03-markowitz-portfolio-revisited.html#index-2">[1]</a>, <a href="notebooks/09/02-markowitz-portfolio-with-chance-constraint.html#index-2">[2]</a>
 </li>
-        <li><a href="notebooks/05/refinery-production.html#index-0">cvxpy</a>
+        <li><a href="notebooks/05/05-refinery-production.html#index-0">cvxpy</a>
 </li>
         <li><a href="notebooks/01/02-production-planning-basic.html#index-5">HiGHS</a>
 </li>
-        <li><a href="notebooks/02/01-bim.html#index-2">highs</a>, <a href="notebooks/02/03-mad-portfolio-optimization.html#index-3">[1]</a>, <a href="notebooks/02/05-bim-maxmin.html#index-1">[2]</a>, <a href="notebooks/02/06-bim-fractional.html#index-1">[3]</a>, <a href="notebooks/02/07-bim-rawmaterialplanning.html#index-3">[4]</a>, <a href="notebooks/02/09-multiproductionfaciliity_worstcase.html#index-4">[5]</a>, <a href="notebooks/03/05-machine-scheduling.html#index-2">[6]</a>, <a href="notebooks/04/gasoline-distribution.html#index-1">[7]</a>, <a href="notebooks/04/graph-coloring.html#index-1">[8]</a>, <a href="notebooks/04/mincost-flow.html#index-1">[9]</a>, <a href="notebooks/05/milk-pooling.html#index-5">[10]</a>, <a href="notebooks/05/refinery-production.html#index-1">[11]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-2">[12]</a>, <a href="notebooks/07/fleet-assignment.html#index-2">[13]</a>
+        <li><a href="notebooks/02/01-bim.html#index-2">highs</a>, <a href="notebooks/02/03-mad-portfolio-optimization.html#index-3">[1]</a>, <a href="notebooks/02/05-bim-maxmin.html#index-1">[2]</a>, <a href="notebooks/02/06-bim-fractional.html#index-1">[3]</a>, <a href="notebooks/02/07-bim-demand-forecast.html#index-3">[4]</a>, <a href="notebooks/02/09-production-faciliity-worst-case.html#index-4">[5]</a>, <a href="notebooks/03/05-machine-scheduling.html#index-2">[6]</a>, <a href="notebooks/04/02-mincost-flow.html#index-1">[7]</a>, <a href="notebooks/04/03-gasoline-distribution.html#index-1">[8]</a>, <a href="notebooks/04/04-exam-room-scheduling.html#index-1">[9]</a>, <a href="notebooks/05/01-milk-pooling.html#index-5">[10]</a>, <a href="notebooks/05/05-refinery-production.html#index-1">[11]</a>, <a href="notebooks/07/01-fleet-assignment.html#index-2">[12]</a>, <a href="notebooks/07/02-bim-robustness-analysis.html#index-2">[13]</a>, <a href="notebooks/10/03-two-stage-production-planning.html#index-2">[14]</a>
 </li>
-        <li><a href="notebooks/05/milk-pooling.html#index-6">ipopt</a>
+        <li><a href="notebooks/05/01-milk-pooling.html#index-6">ipopt</a>
 </li>
-        <li><a href="notebooks/06/building-insulation.html#index-3">mosek</a>, <a href="notebooks/06/economic-order-quantity.html#index-3">[1]</a>, <a href="notebooks/06/investment-wheel.html#index-3">[2]</a>, <a href="notebooks/06/kelly-criterion.html#index-3">[3]</a>, <a href="notebooks/06/optimal-growth-portfolios.html#index-3">[4]</a>
+        <li><a href="notebooks/06/01-economic-order-quantity.html#index-3">mosek</a>, <a href="notebooks/06/02-kelly-criterion.html#index-3">[1]</a>, <a href="notebooks/06/04-building-insulation.html#index-3">[2]</a>, <a href="notebooks/06/06-investment-wheel.html#index-3">[3]</a>, <a href="notebooks/06/07-optimal-growth-portfolios.html#index-3">[4]</a>
 </li>
       </ul></li>
   </ul></td>
@@ -688,10 +689,10 @@ <h2 id="S">S</h2>
     Solvers
 
       <ul>
-        <li><a href="notebooks/06/svm-conic.html#index-0">Mosek</a>
+        <li><a href="notebooks/06/05-svm-conic.html#index-0">Mosek</a>
 </li>
       </ul></li>
-      <li><a href="notebooks/09/economicdispatch.html#index-3">stochastic optimization</a>, <a href="notebooks/09/pop-up_shop.html#index-3">[1]</a>, <a href="notebooks/09/seafood.html#index-4">[2]</a>, <a href="notebooks/10/airline-seating.html#index-4">[3]</a>, <a href="notebooks/10/farmer.html#index-4">[4]</a>, <a href="notebooks/10/opf-ldr.html#index-4">[5]</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-4">[6]</a>
+      <li><a href="notebooks/09/01-pop-up-shop.html#index-3">stochastic optimization</a>, <a href="notebooks/09/03-seafood-distribution-center.html#index-4">[1]</a>, <a href="notebooks/09/04-economic-dispatch.html#index-3">[2]</a>, <a href="notebooks/10/02-opf-linear-decision-rule.html#index-4">[3]</a>, <a href="notebooks/10/04-farmer-problem.html#index-4">[4]</a>, <a href="notebooks/10/05-opf-wind-curtailment.html#index-4">[5]</a>
 </li>
   </ul></td>
 </tr></table>
@@ -699,7 +700,7 @@ <h2 id="S">S</h2>
 <h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/10/airline-seating.html#index-7">two-stage problem</a>, <a href="notebooks/10/ccg.html#index-5">[1]</a>, <a href="notebooks/10/farmer.html#index-5">[2]</a>
+      <li><a href="notebooks/10/03-two-stage-production-planning.html#index-5">two-stage problem</a>, <a href="notebooks/10/04-farmer-problem.html#index-5">[1]</a>
 </li>
   </ul></td>
 </tr></table>
@@ -707,7 +708,7 @@ <h2 id="T">T</h2>
 <h2 id="W">W</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-5">web scraping</a>, <a href="notebooks/04/cryptocurrency-arbitrage.html#index-2">[1]</a>
+      <li><a href="notebooks/02/03-mad-portfolio-optimization.html#index-5">web scraping</a>, <a href="notebooks/04/05-cryptocurrency-arbitrage.html#index-2">[1]</a>
 </li>
   </ul></td>
 </tr></table>
diff --git a/intro.html b/intro.html
index ef0d6731..c33cad6d 100644
--- a/intro.html
+++ b/intro.html
@@ -187,9 +187,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -198,6 +198,10 @@
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="notebooks/03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -205,62 +209,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/01/01-production-planning.html b/notebooks/01/01-production-planning.html
index ca1ae0b2..ad732de6 100644
--- a/notebooks/01/01-production-planning.html
+++ b/notebooks/01/01-production-planning.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/01/01.00.html b/notebooks/01/01.00.html
index 7612b43b..13aa6bac 100644
--- a/notebooks/01/01.00.html
+++ b/notebooks/01/01.00.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/01/02-production-planning-basic.html b/notebooks/01/02-production-planning-basic.html
index 4f66d504..2e6a7c91 100644
--- a/notebooks/01/02-production-planning-basic.html
+++ b/notebooks/01/02-production-planning-basic.html
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -807,7 +810,7 @@ <h2>Step 7. Solve the model<a class="headerlink" href="#step-7-solve-the-model"
 </section>
 <section id="step-8-reporting-the-solution">
 <h2>Step 8. Reporting the solution<a class="headerlink" href="#step-8-reporting-the-solution" title="Permalink to this heading">#</a></h2>
-<p>The final step in most applications is to report the solution in a suitable format. For this example, we demonstrate simple tabular and graphic reports using the Pandas library. For an overview of other ways to report and visualize the solutions, see also the appendix of <a class="reference internal" href="../04/gasoline-distribution.html"><span class="doc std std-doc">this notebook</span></a> from Chapter 4.</p>
+<p>The final step in most applications is to report the solution in a suitable format. For this example, we demonstrate simple tabular and graphic reports using the Pandas library. For an overview of other ways to report and visualize the solutions, see also the appendix of <span class="xref myst">this notebook</span> from Chapter 4.</p>
 <section id="pyomo-pprint">
 <h3>Pyomo <code class="docutils literal notranslate"><span class="pre">pprint()</span></code><a class="headerlink" href="#pyomo-pprint" title="Permalink to this heading">#</a></h3>
 <p>Pyomo provides several functions for creating model reports that contain solution values. The <code class="docutils literal notranslate"><span class="pre">pprint()</span></code> method can be applied to the entire model, or to individual components of the model, as shown in the following cells.</p>
diff --git a/notebooks/01/03-production-planning-advanced.html b/notebooks/01/03-production-planning-advanced.html
index 3f35cc6d..9d75c5b6 100644
--- a/notebooks/01/03-production-planning-advanced.html
+++ b/notebooks/01/03-production-planning-advanced.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/02/01-bim.html b/notebooks/02/01-bim.html
index 5d7ae7ad..bb49be58 100644
--- a/notebooks/02/01-bim.html
+++ b/notebooks/02/01-bim.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/02/02-lad-regression.html b/notebooks/02/02-lad-regression.html
index 0db79e1b..0870cdd8 100644
--- a/notebooks/02/02-lad-regression.html
+++ b/notebooks/02/02-lad-regression.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/02/02.00.html b/notebooks/02/02.00.html
index 3232046b..39cd3ead 100644
--- a/notebooks/02/02.00.html
+++ b/notebooks/02/02.00.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -484,15 +487,15 @@ <h1>2. Linear Optimization<a class="headerlink" href="#linear-optimization" titl
 <p>Linear problems can (i) be maximization problems, (ii) involve equality constraints and constraints of the form <span class="math notranslate nohighlight">\(\geq\)</span>, and (iii) have unbounded or non-positive decision variables <span class="math notranslate nohighlight">\(x_i\)</span>’s. In fact, any LO problem with such features can be easily converted to the “canonical” LO form above by adding/removing variables and/or multiplying specific inequalities by <span class="math notranslate nohighlight">\(-1\)</span>.</p>
 <p>This chapter includes several with companion Pyomo implementation that explore various modeling and implementation aspects of LOs:</p>
 <ul class="simple">
-<li><p>A first LO example, modelling <a class="reference internal" href="01-bim.html"><span class="doc std std-doc">the microchip production problem of company BIM</span></a></p></li>
+<li><p><a class="reference internal" href="01-bim.html"><span class="doc std std-doc">Microchip production problem of company BIM</span></a></p></li>
 <li><p><a class="reference internal" href="02-lad-regression.html"><span class="doc std std-doc">Least Absolute Deviation (LAD) Regression</span></a></p></li>
 <li><p><a class="reference internal" href="03-mad-portfolio-optimization.html"><span class="doc std std-doc">Mean Absolute Deviation (MAD) portfolio optimization</span></a></p></li>
 <li><p><a class="reference internal" href="04-bim-dual.html"><span class="doc std std-doc">The dual problem of the microchip production problem</span></a></p></li>
 <li><p><a class="reference internal" href="05-bim-maxmin.html"><span class="doc std std-doc">A variant of BIM problem: maximizing the lowest possible profit</span></a></p></li>
 <li><p><a class="reference internal" href="06-bim-fractional.html"><span class="doc std std-doc">Two variants of the BIM problem using fractional objective or additional fixed costs</span></a></p></li>
-<li><p><a class="reference internal" href="07-bim-rawmaterialplanning.html"><span class="doc std std-doc">The BIM production problem using demand forecasts</span></a></p></li>
+<li><p><span class="xref myst">The BIM production problem using demand forecasts</span></p></li>
 <li><p><a class="reference internal" href="08-L1-regression-wine-quality.html"><span class="doc std std-doc">Extra material: Wine quality prediction problem using <span class="math notranslate nohighlight">\(L_1\)</span> regression</span></a></p></li>
-<li><p><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html"><span class="doc std std-doc">Extra material: Multi-product facility production</span></a></p></li>
+<li><p><a class="reference internal" href="09-production-faciliity-worst-case.html"><span class="doc std std-doc">Extra material: Multi-product facility production</span></a></p></li>
 </ul>
 <p>Go to the <a class="reference internal" href="../03/03.00.html"><span class="doc std std-doc">next chapter</span></a> about mixed-integer linear optimization.</p>
 <div class="toctree-wrapper compound">
diff --git a/notebooks/02/03-mad-portfolio-optimization.html b/notebooks/02/03-mad-portfolio-optimization.html
index e7685860..902c3ede 100644
--- a/notebooks/02/03-mad-portfolio-optimization.html
+++ b/notebooks/02/03-mad-portfolio-optimization.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/02/04-bim-dual.html b/notebooks/02/04-bim-dual.html
index 5c0f1f52..869aadff 100644
--- a/notebooks/02/04-bim-dual.html
+++ b/notebooks/02/04-bim-dual.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/02/05-bim-maxmin.html b/notebooks/02/05-bim-maxmin.html
index b9cb8e8a..818c66ae 100644
--- a/notebooks/02/05-bim-maxmin.html
+++ b/notebooks/02/05-bim-maxmin.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/02/06-bim-fractional.html b/notebooks/02/06-bim-fractional.html
index a6644f1b..a1aef530 100644
--- a/notebooks/02/06-bim-fractional.html
+++ b/notebooks/02/06-bim-fractional.html
@@ -74,7 +74,7 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/02/06-bim-fractional';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="2.7 BIM production using demand forecasts" href="07-bim-rawmaterialplanning.html" />
+    <link rel="next" title="2.7 BIM production using demand forecasts" href="07-bim-demand-forecast.html" />
     <link rel="prev" title="2.5 BIM production for worst case" href="05-bim-maxmin.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -730,7 +733,7 @@ <h2>Two variants of the BIM problem: fractional objective and additional fixed c
       </div>
     </a>
     <a class="right-next"
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+       href="07-bim-demand-forecast.html"
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diff --git a/notebooks/02/07-bim-rawmaterialplanning.html b/notebooks/02/07-bim-demand-forecast.html
similarity index 97%
rename from notebooks/02/07-bim-rawmaterialplanning.html
rename to notebooks/02/07-bim-demand-forecast.html
index 66f52968..1a1ce28b 100644
--- a/notebooks/02/07-bim-rawmaterialplanning.html
+++ b/notebooks/02/07-bim-demand-forecast.html
@@ -71,7 +71,7 @@
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/02/07-bim-rawmaterialplanning';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/02/07-bim-demand-forecast';</script>
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@@ -192,7 +192,7 @@
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/02/07-bim-rawmaterialplanning.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/02/07-bim-demand-forecast.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/02/07-bim-rawmaterialplanning.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/02/07-bim-demand-forecast.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/02/07-bim-rawmaterialplanning.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/02/07-bim-demand-forecast.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
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diff --git a/notebooks/02/08-L1-regression-wine-quality.html b/notebooks/02/08-L1-regression-wine-quality.html
index e15622f6..65ef4d9d 100644
--- a/notebooks/02/08-L1-regression-wine-quality.html
+++ b/notebooks/02/08-L1-regression-wine-quality.html
@@ -74,8 +74,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/02/08-L1-regression-wine-quality';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra material: Multi-product facility production" href="09-multiproductionfaciliity_worstcase.html" />
-    <link rel="prev" title="2.7 BIM production using demand forecasts" href="07-bim-rawmaterialplanning.html" />
+    <link rel="next" title="Extra material: Multi-product facility production" href="09-production-faciliity-worst-case.html" />
+    <link rel="prev" title="2.7 BIM production using demand forecasts" href="07-bim-demand-forecast.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -1198,7 +1201,7 @@ <h2>How do these models perform?<a class="headerlink" href="#how-do-these-models
                   <!-- Previous / next buttons -->
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@@ -1207,7 +1210,7 @@ <h2>How do these models perform?<a class="headerlink" href="#how-do-these-models
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diff --git a/notebooks/02/09-multiproductionfaciliity_worstcase.html b/notebooks/02/09-production-faciliity-worst-case.html
similarity index 95%
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rename to notebooks/02/09-production-faciliity-worst-case.html
index 6153e7dd..19865c38 100644
--- a/notebooks/02/09-multiproductionfaciliity_worstcase.html
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@@ -71,7 +71,7 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/02/09-multiproductionfaciliity_worstcase';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/02/09-production-faciliity-worst-case';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="3. Mixed Integer Linear Optimization" href="../03/03.00.html" />
@@ -190,7 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Multi-product facility production</a></li>
 </ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/02/09-multiproductionfaciliity_worstcase.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/02/09-production-faciliity-worst-case.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/02/09-multiproductionfaciliity_worstcase.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/02/09-production-faciliity-worst-case.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/02/09-multiproductionfaciliity_worstcase.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/02/09-production-faciliity-worst-case.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
diff --git a/notebooks/03/01-bim-perturbed.html b/notebooks/03/01-bim-perturbed.html
index 182791d4..5677cdc7 100644
--- a/notebooks/03/01-bim-perturbed.html
+++ b/notebooks/03/01-bim-perturbed.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/03/02-shift-scheduling.html b/notebooks/03/02-shift-scheduling.html
index 27bc97cc..d2967cf1 100644
--- a/notebooks/03/02-shift-scheduling.html
+++ b/notebooks/03/02-shift-scheduling.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/03/03-recharging-electric-vehicle.html b/notebooks/03/03-recharging-electric-vehicle.html
index 4c7cc0a1..bc782602 100644
--- a/notebooks/03/03-recharging-electric-vehicle.html
+++ b/notebooks/03/03-recharging-electric-vehicle.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/03/03.00.html b/notebooks/03/03.00.html
index 48d5addf..107cfe66 100644
--- a/notebooks/03/03.00.html
+++ b/notebooks/03/03.00.html
@@ -75,7 +75,7 @@
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="3.1 BIM production with perturbed data" href="01-bim-perturbed.html" />
-    <link rel="prev" title="Extra material: Multi-product facility production" href="../02/09-multiproductionfaciliity_worstcase.html" />
+    <link rel="prev" title="Extra material: Multi-product facility production" href="../02/09-production-faciliity-worst-case.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -531,7 +534,7 @@ <h1>3. Mixed Integer Linear Optimization<a class="headerlink" href="#mixed-integ
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="../02/09-multiproductionfaciliity_worstcase.html"
+       href="../02/09-production-faciliity-worst-case.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
diff --git a/notebooks/03/04-simple-production-model-gdp.html b/notebooks/03/04-simple-production-model-gdp.html
index 71fd331f..d490517b 100644
--- a/notebooks/03/04-simple-production-model-gdp.html
+++ b/notebooks/03/04-simple-production-model-gdp.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/03/05-machine-scheduling.html b/notebooks/03/05-machine-scheduling.html
index b8c15a3c..1d03db51 100644
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@@ -74,7 +74,7 @@
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra material: Cryptarithms puzzle" href="08-cryptarithms.html" />
+    <link rel="next" title="3.6 Facility location problem" href="06-facility-location.html" />
     <link rel="prev" title="3.4 Production model using disjunctions" href="04-simple-production-model-gdp.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -1311,11 +1314,11 @@ <h2>Optimal scheduling using disjunctive programming<a class="headerlink" href="
       </div>
     </a>
     <a class="right-next"
-       href="08-cryptarithms.html"
+       href="06-facility-location.html"
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-        <p class="prev-next-title">Extra material: Cryptarithms puzzle</p>
+        <p class="prev-next-title">3.6 Facility location problem</p>
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diff --git a/notebooks/03/06-facility-location.html b/notebooks/03/06-facility-location.html
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.00.html">1. Mathematical Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../01/02-production-planning-basic.html">1.2 A basic Pyomo model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../01/03-production-planning-advanced.html">1.3 A data-driven Pyomo Model</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.00.html">2. Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../02/01-bim.html">2.1 BIM production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/02-lad-regression.html">2.2 Least Absolute Deviation (LAD) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/03-mad-portfolio-optimization.html">2.3 Mean Absolute Deviation (MAD) portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="01-bim-perturbed.html">3.1 BIM production with perturbed data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-shift-scheduling.html">3.2 Workforce shift scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="11-maintenance-planning.html">Extra material: Maintenance planning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
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+
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
+
+
+
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
+
+
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../appendix/appendix.html">Appendix: Working with Pyomo</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-11"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../appendix/pyomo-style-guide.html">Pyomo style guide</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../appendix/functional-programming-pyomo.html">Functional Programming with Pyomo</a></li>
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>3.6 Facility location problem</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">3.6 Facility location problem</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preamble-install-pyomo-and-solvers">Preamble: Install Pyomo and solvers</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#first-milo-formulation">First MILO formulation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#second-milo-formulation">Second MILO formulation</a></li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#solvers-and-options">Solvers and options</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#the-models">The models</a></li>
+</ul>
+
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <section class="tex2jax_ignore mathjax_ignore" id="facility-location-problem">
+<h1>3.6 Facility location problem<a class="headerlink" href="#facility-location-problem" title="Permalink to this heading">#</a></h1>
+<p>This notebook illustrates Example 20 from Chapter 3 of the textbook Hands-On Mathematical Optimization with Python, Cambridge University Press, 2024.</p>
+<p>For details about these models and more information we refer to the book.</p>
+<p>Also notice that at the end of Chapter 3 you may find a number of exercises that show that versions of this problem aiming at maximizing the importance of the customers served, when facing constraints that limit the possibility of serving all, leads to even more striking evidence for the importance of selecting the right model: it can offer large factors of speedup in solving the relevant instances!</p>
+<section id="preamble-install-pyomo-and-solvers">
+<h2>Preamble: Install Pyomo and solvers<a class="headerlink" href="#preamble-install-pyomo-and-solvers" title="Permalink to this heading">#</a></h2>
+<p>The following cell sets and verifies a Pyomo installation and the solver CBC. If run on Google Colab, the cell installs Pyomo and the CBC solver, while, if run elsewhere, it assumes they all have been previously installed.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
+
+<span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
+    <span class="o">%</span><span class="k">pip</span> install pyomo &gt;/dev/null 2&gt;/dev/null
+    <span class="o">!</span>apt-get<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span>-qq<span class="w"> </span>coinor-cbc<span class="w">    </span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="problem-description">
+<h2>Problem description<a class="headerlink" href="#problem-description" title="Permalink to this heading">#</a></h2>
+<p>Consider the problem of a supplier facing the task of fulfilling specific customer demands with minimal costs while simultaneously deciding how many facilities to build and where. In terms of data, we are given a set <span class="math notranslate nohighlight">\(I\)</span> of customers, a set <span class="math notranslate nohighlight">\(J\)</span> of possible locations, the cost <span class="math notranslate nohighlight">\(c_j\)</span> of building facility <span class="math notranslate nohighlight">\(j\)</span>, and the cost <span class="math notranslate nohighlight">\(h_{ij}\)</span> incurred to satisfy the demands of customer <span class="math notranslate nohighlight">\(i\)</span> at facility <span class="math notranslate nohighlight">\(j\)</span>.</p>
+<p>In this notebook we will formulate two equivalent models for this problem and compare their performance.</p>
+</section>
+<section id="first-milo-formulation">
+<h2>First MILO formulation<a class="headerlink" href="#first-milo-formulation" title="Permalink to this heading">#</a></h2>
+<p>Introduce two sets of binary variables,</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}
+\begin{equation*}
+    x_j:=
+    \begin{cases}
+    1 &amp; \text{ if facility $j$ is built,}\\
+    0 &amp; \text{ otherwise,}
+    \end{cases}
+\end{equation*}
+\end{split}\]</div>
+<p>and</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}
+\begin{equation*}
+    y_{ij}:=
+    \begin{cases}
+    1 &amp; \text{ if customer $i$ is served at facility $j$,}\\
+    0 &amp; \text{ otherwise.}
+    \end{cases}
+\end{equation*}
+\end{split}\]</div>
+<p>The resulting MILO is</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}
+\begin{align*}
+    \min \quad &amp; \sum_{j \in J} c_j x_j + \sum_{i \in I} \sum_{j \in J} h_{ij} y_{ij} \\
+    \text{s.t.} \quad &amp; \sum_{j \in J} y_{ij} =1 \qquad \forall \, i \in I &amp; \text{(every customer is served)}\\
+    &amp; y_{ij} \leq x_{j} \qquad \forall \, i\in I, \, \forall \, j \in J &amp; \text{(facility built before use)}\\
+    &amp; x_j \in \{0,1\} \qquad \forall \, i\in I\\
+    &amp; y_{ij} \in \{0,1\} \qquad \forall \, i\in I, \, \forall \, j \in J.
+\end{align*}
+\end{split}\]</div>
+</section>
+<section id="second-milo-formulation">
+<h2>Second MILO formulation<a class="headerlink" href="#second-milo-formulation" title="Permalink to this heading">#</a></h2>
+<p>Let <span class="math notranslate nohighlight">\(n=|J|\)</span> be the number of possible facility locations and <span class="math notranslate nohighlight">\(m=|I|\)</span> the number of customers. Note that, since <span class="math notranslate nohighlight">\(\sum_j y_{ij} =1\)</span> for every <span class="math notranslate nohighlight">\(i \in I\)</span>, we can replace the <span class="math notranslate nohighlight">\(n \times m\)</span> constraints <span class="math notranslate nohighlight">\(y_{ij} \leq x_j\)</span> by only <span class="math notranslate nohighlight">\(n\)</span> constraints, namely</p>
+<div class="math notranslate nohighlight">
+\[
+\begin{equation*}
+    \sum_i y_{ij} \leq n x_j \qquad \forall \, j \in J.
+\end{equation*}
+\]</div>
+<p>The second MILO is</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}
+\begin{align*}
+    \min \quad &amp; \sum_{j \in J} c_j x_j + \sum_{i \in I} \sum_{j \in J} h_{ij} y_{ij} \\
+    \text{s.t.} \quad &amp; \sum_{j \in J} y_{ij} =1 \qquad \forall \, i \in I &amp; \text{(every customer is served)}\\
+    &amp; \sum_i y_{ij} \leq n x_j \qquad \forall \, j \in J &amp; \text{(facility built before use)}\\
+    &amp; x_j \in \{0,1\} \qquad \forall \, i\in I\\
+    &amp; y_{ij} \in \{0,1\} \qquad \forall \, i\in I, \, \forall \, j \in J.
+\end{align*}
+\end{split}\]</div>
+<p>This approach leads to a more concise mathematical formulation of the model. However, it may not be a good idea if we want to solve the problem using its linear relaxation. Indeed, by reducing the number of constraints, we inadvertently made the feasible region of the relaxation larger and less tight around the feasible integer points. This fact becomes clearly evident in the increased run-time required to solve the optimization problem when working with these weaker constraints, see figures below.</p>
+<p>Instead of defining two separate Pyomo models, we will define a single common model and then add either the strong or weaker formulation of the “facility built before use” constraint.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">itertools</span> <span class="k">as</span> <span class="nn">it</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">perf_counter</span> <span class="k">as</span> <span class="n">pc</span>
+<span class="kn">from</span> <span class="nn">tqdm.notebook</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">subprocess</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section class="tex2jax_ignore mathjax_ignore" id="solvers-and-options">
+<h1>Solvers and options<a class="headerlink" href="#solvers-and-options" title="Permalink to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">ListAvailableSolvers</span><span class="p">():</span>
+    <span class="n">shell_command</span> <span class="o">=</span> <span class="s2">&quot;pyomo help --solvers&quot;</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">check_output</span><span class="p">(</span><span class="n">shell_command</span><span class="p">,</span> <span class="n">shell</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span>
+    <span class="k">return</span> <span class="p">[</span>
+        <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()[</span><span class="mi">1</span><span class="p">:]</span>
+        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;+&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">&quot;)&quot;</span><span class="p">)</span>
+    <span class="p">]</span>
+
+
+<span class="k">def</span> <span class="nf">GetSolverName</span><span class="p">(</span><span class="n">solver</span><span class="p">):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">solver</span><span class="o">.</span><span class="n">name</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="k">return</span> <span class="s2">&quot;appsi_highs&quot;</span>
+
+
+<span class="k">def</span> <span class="nf">SwitchCutsOff</span><span class="p">(</span><span class="n">solver</span><span class="p">):</span>
+    <span class="n">solver_name</span> <span class="o">=</span> <span class="n">GetSolverName</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span>
+    <span class="k">if</span> <span class="s2">&quot;cbc&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;Cuts&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;off&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;cplex&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_bqp&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_cliques&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_covers&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_disjunctive&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_flowcovers&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_pathcut&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_gomory&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_gubcovers&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_implied&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_localimplied&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_liftproj&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_mircut&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_mcfcut&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_rlt&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;mip_cuts_zerohalfcut&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+    <span class="k">elif</span> <span class="s2">&quot;gurobi&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;Cuts&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">elif</span> <span class="s2">&quot;highs&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="k">pass</span>  <span class="c1"># there is no way to do this, see</span>
+        <span class="c1"># https://ergo-code.github.io/HiGHS/dev/options/definitions/</span>
+    <span class="k">elif</span> <span class="s2">&quot;xpress&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;CUTSTRATEGY&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">pass</span>
+    <span class="k">return</span> <span class="n">solver</span>
+
+
+<span class="k">def</span> <span class="nf">SwitchPresolveOff</span><span class="p">(</span><span class="n">solver</span><span class="p">):</span>
+    <span class="n">solver_name</span> <span class="o">=</span> <span class="n">GetSolverName</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span>
+    <span class="k">if</span> <span class="s2">&quot;cbc&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;Presolve&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;off&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;cplex&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;preprocessing_presolve&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">elif</span> <span class="s2">&quot;gurobi&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;Presolve&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">elif</span> <span class="s2">&quot;highs&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;presolve&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;off&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;xpress&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;PRESOLVE&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">pass</span>
+    <span class="k">return</span> <span class="n">solver</span>
+
+
+<span class="k">def</span> <span class="nf">LimitSolveTime</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">max_in_seconds</span><span class="p">):</span>
+    <span class="n">solver_name</span> <span class="o">=</span> <span class="n">GetSolverName</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span>
+    <span class="k">if</span> <span class="s2">&quot;cbc&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;seconds&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_in_seconds</span>
+    <span class="k">elif</span> <span class="s2">&quot;cplex&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;timelimit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_in_seconds</span>
+    <span class="k">elif</span> <span class="s2">&quot;gurobi&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;TimeLimit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_in_seconds</span>
+    <span class="k">elif</span> <span class="s2">&quot;highs&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;time_limit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_in_seconds</span>
+    <span class="k">elif</span> <span class="s2">&quot;xpress&quot;</span> <span class="ow">in</span> <span class="n">solver_name</span><span class="p">:</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;MAXTIME&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_in_seconds</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">pass</span>
+    <span class="k">return</span> <span class="n">solver</span>
+
+
+<span class="k">def</span> <span class="nf">ClosureForMaxTime</span><span class="p">(</span><span class="n">max_in_seconds</span><span class="p">):</span>
+    <span class="k">return</span> <span class="k">lambda</span> <span class="n">solver</span><span class="p">:</span> <span class="n">LimitSolveTime</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">max_in_seconds</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compose</span><span class="p">(</span><span class="o">*</span><span class="n">functions</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">composed</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">functions</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">result</span>
+
+    <span class="k">return</span> <span class="n">composed</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">initial_solvers</span> <span class="o">=</span> <span class="n">ListAvailableSolvers</span><span class="p">()</span>
+<span class="n">initial_solvers</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;appsi_gurobi&#39;,
+ &#39;appsi_highs&#39;,
+ &#39;cbc&#39;,
+ &#39;gdpopt&#39;,
+ &#39;gdpopt.gloa&#39;,
+ &#39;gdpopt.lbb&#39;,
+ &#39;gdpopt.loa&#39;,
+ &#39;gdpopt.ric&#39;,
+ &#39;gurobi&#39;,
+ &#39;gurobi_direct&#39;,
+ &#39;gurobi_persistent&#39;,
+ &#39;ipopt&#39;,
+ &#39;mindtpy&#39;,
+ &#39;mindtpy.ecp&#39;,
+ &#39;mindtpy.fp&#39;,
+ &#39;mindtpy.goa&#39;,
+ &#39;mindtpy.oa&#39;,
+ &#39;mosek&#39;,
+ &#39;mosek_direct&#39;,
+ &#39;mosek_persistent&#39;,
+ &#39;mpec_minlp&#39;,
+ &#39;mpec_nlp&#39;,
+ &#39;multistart&#39;,
+ &#39;scipy.fsolve&#39;,
+ &#39;scipy.newton&#39;,
+ &#39;scipy.root&#39;,
+ &#39;scipy.secant-newton&#39;,
+ &#39;trustregion&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section class="tex2jax_ignore mathjax_ignore" id="the-models">
+<h1>The models<a class="headerlink" href="#the-models" title="Permalink to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">FacilityLocationCommon</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">):</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Facility location&quot;</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">facilities</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">installation</span><span class="p">)))</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">customers</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">service</span><span class="p">)))</span>
+
+    <span class="n">model</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">,</span> <span class="n">within</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">customers</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">,</span> <span class="n">within</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>
+
+    <span class="nd">@model</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">minimize</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">obj</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">installation</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">)</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">service</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">customers</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">facilities</span>
+        <span class="p">)</span>
+
+    <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">customers</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">ChooseOneFacility</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
+
+    <span class="k">return</span> <span class="n">model</span>
+
+
+<span class="k">def</span> <span class="nf">FacilityLocationWeak</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">):</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">FacilityLocationCommon</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">+=</span> <span class="s2">&quot; weak model&quot;</span>
+
+    <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">ServeIfOpen</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">customers</span><span class="p">)</span>
+            <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">customers</span><span class="p">)</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+        <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span>
+
+
+<span class="k">def</span> <span class="nf">FacilityLocationStrong</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">):</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">FacilityLocationCommon</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">+=</span> <span class="s2">&quot; strong model&quot;</span>
+
+    <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">customers</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">ServeIfOpen</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">model</span>
+
+
+<span class="k">def</span> <span class="nf">GetSolution</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">j</span><span class="p">]()</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">]</span>
+    <span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]()</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">facilities</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">customers</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">obj</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We introduce functions to generate random instances for the problem and to then visualize the optimal solution.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">GenerateFacilityLocationInstance</span><span class="p">(</span><span class="n">nofFacilities</span><span class="p">,</span> <span class="n">nofCustumers</span><span class="p">):</span>
+    <span class="n">facilities</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">nofFacilities</span><span class="p">)</span>
+    <span class="n">customers</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">nofCustumers</span><span class="p">)</span>
+    <span class="n">xC</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">nofCustumers</span><span class="p">)</span>
+    <span class="n">yC</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">nofCustumers</span><span class="p">)</span>
+    <span class="n">xF</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">nofFacilities</span><span class="p">)</span>
+    <span class="n">yF</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">nofFacilities</span><span class="p">)</span>
+
+    <span class="n">installation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">,</span> <span class="n">nofFacilities</span><span class="p">)</span>
+
+    <span class="n">dist</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="p">((</span><span class="n">xC</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">xF</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">yC</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">yF</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="n">service</span> <span class="o">=</span> <span class="p">[[</span><span class="n">dist</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">facilities</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">customers</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">,</span> <span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span>
+
+
+<span class="k">def</span> <span class="nf">ShowFacilityLocation</span><span class="p">(</span><span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="p">[],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[],</span> <span class="n">value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
+    <span class="p">[</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">xC</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">xF</span><span class="p">[</span><span class="n">j</span><span class="p">]],</span> <span class="p">[</span><span class="n">yC</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">yF</span><span class="p">[</span><span class="n">j</span><span class="p">]],</span> <span class="s2">&quot;g-&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">X</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">Y</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span>
+    <span class="p">]</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="s2">&quot;o&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xF</span><span class="p">,</span> <span class="n">yF</span><span class="p">,</span> <span class="s2">&quot;s&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">title</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">value</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Optimal value: </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We then generate an instance of the problem with 10 facilities and 100 customers and initialize both a strong and a weak Pyomo models.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">2023</span><span class="p">)</span>
+
+<span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">,</span> <span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span> <span class="o">=</span> <span class="n">GenerateFacilityLocationInstance</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">ShowFacilityLocation</span><span class="p">(</span><span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span><span class="p">)</span>
+
+<span class="n">weak</span> <span class="o">=</span> <span class="n">FacilityLocationWeak</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">)</span>
+<span class="n">strong</span> <span class="o">=</span> <span class="n">FacilityLocationStrong</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/4cdf618b7615a4d716ad49e6b58907c6aea22f61cc90ff0c0c3f2ae6d3f4956c.png" src="../../_images/4cdf618b7615a4d716ad49e6b58907c6aea22f61cc90ff0c0c3f2ae6d3f4956c.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">Solve</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+    <span class="n">t</span> <span class="o">=</span> <span class="n">pc</span><span class="p">()</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+    <span class="n">t</span> <span class="o">=</span> <span class="n">pc</span><span class="p">()</span> <span class="o">-</span> <span class="n">t</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">GetSolution</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+    <span class="n">title</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> solved by </span><span class="si">{</span><span class="n">GetSolverName</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span><span class="si">}</span><span class="s2"> in </span><span class="si">{</span><span class="n">t</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds </span><span class="si">{</span><span class="n">v</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">title</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">solver</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="s2">&quot;cbc&quot;</span><span class="p">)</span>
+<span class="n">ShowFacilityLocation</span><span class="p">(</span><span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span><span class="p">,</span> <span class="o">*</span><span class="n">Solve</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">weak</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/c2c525369fe62a83e70c49c29b29e5a36825c10a56f6a2b06d82951b6a8465b8.png" src="../../_images/c2c525369fe62a83e70c49c29b29e5a36825c10a56f6a2b06d82951b6a8465b8.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ShowFacilityLocation</span><span class="p">(</span><span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span><span class="p">,</span> <span class="o">*</span><span class="n">Solve</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">strong</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/934e12b9acf4efc9643d8116bb28841d13ba40a27196c42a92e863c5b2c96193.png" src="../../_images/934e12b9acf4efc9643d8116bb28841d13ba40a27196c42a92e863c5b2c96193.png" />
+</div>
+</div>
+<p>However, this is not a proper performance comparison between the two model formulations, since under the hood <code class="docutils literal notranslate"><span class="pre">cbc</span></code> is already solving the instance in a clever way. The commercial solver <code class="docutils literal notranslate"><span class="pre">Gurobi</span></code> and the open-source solver <code class="docutils literal notranslate"><span class="pre">HiGHS</span></code> also have a similar feature.</p>
+<p>If run on Google Colab, the following cell installs three additional commercial solvers; if run elsewhere, it assumes they all have been previously installed.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
+    <span class="o">%</span><span class="k">pip</span> install gurobipy &gt;/dev/null 2&gt;/dev/null
+    <span class="o">%</span><span class="k">pip</span> install cplex &gt;/dev/null 2&gt;/dev/null
+    <span class="o">%</span><span class="k">pip</span> install xpress &gt;/dev/null 2&gt;/dev/null
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">available_solvers</span> <span class="o">=</span> <span class="n">ListAvailableSolvers</span><span class="p">()</span>
+<span class="nb">set</span><span class="p">(</span><span class="n">available_solvers</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">initial_solvers</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>set()
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">,</span> <span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span> <span class="o">=</span> <span class="n">GenerateFacilityLocationInstance</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">88</span><span class="p">)</span>
+<span class="n">weak</span> <span class="o">=</span> <span class="n">FacilityLocationWeak</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">)</span>
+<span class="n">strong</span> <span class="o">=</span> <span class="n">FacilityLocationStrong</span><span class="p">(</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">)</span>
+
+<span class="n">solver_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;cplex_direct&quot;</span><span class="p">,</span> <span class="s2">&quot;gurobi_direct&quot;</span><span class="p">,</span> <span class="s2">&quot;xpress_direct&quot;</span><span class="p">,</span> <span class="s2">&quot;appsi_highs&quot;</span><span class="p">]</span>
+<span class="n">solver_names</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">solver_names</span><span class="p">)</span> <span class="o">&amp;</span> <span class="nb">set</span><span class="p">(</span><span class="n">ListAvailableSolvers</span><span class="p">()))</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">solver_names</span><span class="p">),</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">solver_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">solver_names</span><span class="p">):</span>
+    <span class="n">solver</span> <span class="o">=</span> <span class="n">SwitchCutsOff</span><span class="p">(</span><span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver_name</span><span class="p">))</span>
+    <span class="n">ShowFacilityLocation</span><span class="p">(</span><span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span><span class="p">,</span> <span class="o">*</span><span class="n">Solve</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">weak</span><span class="p">),</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+    <span class="n">ShowFacilityLocation</span><span class="p">(</span><span class="n">xC</span><span class="p">,</span> <span class="n">yC</span><span class="p">,</span> <span class="n">xF</span><span class="p">,</span> <span class="n">yF</span><span class="p">,</span> <span class="o">*</span><span class="n">Solve</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">strong</span><span class="p">),</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]
+Copyright (c) 2023 HiGHS under MIT licence terms
+Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]
+Copyright (c) 2023 HiGHS under MIT licence terms
+</pre></div>
+</div>
+<img alt="../../_images/649b01ceaaa3c4f3a92932ee53a1ca7dbddc1bc1d46690c87169c8f7b74bac00.png" src="../../_images/649b01ceaaa3c4f3a92932ee53a1ca7dbddc1bc1d46690c87169c8f7b74bac00.png" />
+</div>
+</div>
+<p>We now run a more extensive performance comparison considering increasingly larger instances of the problem. We will see that the strong formulation is consistently faster than the weak formulation.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">SolveInstances</span><span class="p">(</span><span class="n">dimensions</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="n">modify</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="p">):</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;weak&quot;</span><span class="p">:</span> <span class="n">FacilityLocationWeak</span><span class="p">,</span> <span class="s2">&quot;strong&quot;</span><span class="p">:</span> <span class="n">FacilityLocationStrong</span><span class="p">}</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+        <span class="n">index</span><span class="o">=</span><span class="n">dimensions</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">s</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">it</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="n">solvers</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">keys</span><span class="p">())],</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">):</span>
+        <span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">,</span> <span class="o">*</span><span class="n">_</span> <span class="o">=</span> <span class="n">GenerateFacilityLocationInstance</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
+        <span class="n">instance</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">option</span><span class="p">:</span> <span class="n">model</span><span class="p">[</span><span class="n">option</span><span class="p">](</span><span class="n">installation</span><span class="p">,</span> <span class="n">service</span><span class="p">)</span> <span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+        <span class="p">}</span>
+        <span class="n">values</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">solvers</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+            <span class="n">solver</span> <span class="o">=</span> <span class="n">column</span><span class="p">[:</span> <span class="n">column</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)]</span>
+            <span class="n">option</span> <span class="o">=</span> <span class="n">column</span><span class="p">[</span><span class="n">column</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:]</span>
+            <span class="n">t</span> <span class="o">=</span> <span class="n">pc</span><span class="p">()</span>
+            <span class="n">modify</span><span class="p">(</span><span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver</span><span class="p">))</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">instance</span><span class="p">[</span><span class="n">option</span><span class="p">])</span>
+            <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">),</span> <span class="n">column</span><span class="p">]</span> <span class="o">=</span> <span class="n">pc</span><span class="p">()</span> <span class="o">-</span> <span class="n">t</span>
+            <span class="o">*</span><span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">GetSolution</span><span class="p">(</span><span class="n">instance</span><span class="p">[</span><span class="n">option</span><span class="p">])</span>
+            <span class="n">values</span><span class="p">[</span><span class="n">solver</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">values</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">df</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We solve instances with 10 to 100 facilities and 100 to 1000 customers. We then plot the run-time of the strong and weak formulations as a function of the number of facilities.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
+    <span class="n">solvers</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;cbc&quot;</span><span class="p">]</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">solvers</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;gurobi_direct&quot;</span><span class="p">,</span> <span class="s2">&quot;cplex_direct&quot;</span><span class="p">]</span>
+<span class="n">dimensions</span> <span class="o">=</span> <span class="p">[(</span><span class="n">n</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">51</span><span class="p">,</span> <span class="mi">5</span><span class="p">)]</span>
+
+<span class="n">SolveInstances</span><span class="p">(</span><span class="n">dimensions</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="mi">2023</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "f1df101b86464b33ae67dfb114dc3d9b", "version_major": 2, "version_minor": 0}</script><img alt="../../_images/d565860b1415e9cc063dcaad44f586a2038788b3fd04f16e0d881dd97baae928.png" src="../../_images/d565860b1415e9cc063dcaad44f586a2038788b3fd04f16e0d881dd97baae928.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">modify</span> <span class="o">=</span> <span class="n">compose</span><span class="p">(</span><span class="n">ClosureForMaxTime</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">SwitchCutsOff</span><span class="p">)</span>
+<span class="n">SolveInstances</span><span class="p">(</span><span class="n">dimensions</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="mi">2023</span><span class="p">,</span> <span class="n">modify</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "1ebc3796db2b458fb09228a935f5aed4", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING: Loading a SolverResults object with an &#39;aborted&#39; status, but
+containing a solution
+WARNING: Loading a SolverResults object with an &#39;aborted&#39; status, but
+containing a solution
+WARNING: Loading a SolverResults object with an &#39;aborted&#39; status, but
+containing a solution
+WARNING: Loading a SolverResults object with an &#39;aborted&#39; status, but
+containing a solution
+WARNING: Loading a SolverResults object with an &#39;aborted&#39; status, but
+containing a solution
+WARNING: Loading a SolverResults object with an &#39;aborted&#39; status, but
+containing a solution
+</pre></div>
+</div>
+<img alt="../../_images/fab268c87be4d07cecd237121b47f092b6d0cf186c70fa76c8468d6cdd7559be.png" src="../../_images/fab268c87be4d07cecd237121b47f092b6d0cf186c70fa76c8468d6cdd7559be.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">SolveInstances</span><span class="p">(</span><span class="n">dimensions</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="mi">2023</span><span class="p">,</span> <span class="n">SwitchPresolveOff</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "480558b0b5cb45c0ba4c6f75ce5d51ab", "version_major": 2, "version_minor": 0}</script><img alt="../../_images/b856c5b86cffd874c3342e0e66eaf0b1b89c70ea6d0a85636a61417b2d0a2ae0.png" src="../../_images/b856c5b86cffd874c3342e0e66eaf0b1b89c70ea6d0a85636a61417b2d0a2ae0.png" />
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+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">3.6 Facility location problem</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preamble-install-pyomo-and-solvers">Preamble: Install Pyomo and solvers</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#first-milo-formulation">First MILO formulation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#second-milo-formulation">Second MILO formulation</a></li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#solvers-and-options">Solvers and options</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#the-models">The models</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.00.html">1. Mathematical Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../01/01-production-planning.html">1.1 A first production planning problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../01/02-production-planning-basic.html">1.2 A basic Pyomo model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../01/03-production-planning-advanced.html">1.3 A data-driven Pyomo Model</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.00.html">2. Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../02/01-bim.html">2.1 BIM production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/02-lad-regression.html">2.2 Least Absolute Deviation (LAD) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/03-mad-portfolio-optimization.html">2.3 Mean Absolute Deviation (MAD) portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
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+<li class="toctree-l2"><a class="reference internal" href="02-shift-scheduling.html">3.2 Workforce shift scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">3.7 BIM production revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
+<li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="11-maintenance-planning.html">Extra material: Maintenance planning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
+
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
+
+
+
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
+
+
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../appendix/appendix.html">Appendix: Working with Pyomo</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-11"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../appendix/pyomo-style-guide.html">Pyomo style guide</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../appendix/functional-programming-pyomo.html">Functional Programming with Pyomo</a></li>
+</ul>
+</li>
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+    <h1>3.7 BIM production revisited</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preamble-install-pyomo-and-a-solver">Preamble: Install Pyomo and a solver</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data-import">Data import</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#optimization-model">Optimization model</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyomo-implementation">Pyomo implementation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#alternative-pyomo-implementation-using-blocks">Alternative Pyomo implementation using blocks</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
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+  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><section class="tex2jax_ignore mathjax_ignore" id="bim-production-revisited">
+<span id="index-5"></span><h1>3.7 BIM production revisited<a class="headerlink" href="#bim-production-revisited" title="Permalink to this heading">#</a></h1>
+<section id="preamble-install-pyomo-and-a-solver">
+<h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
+<p>The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it
+assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object <code class="docutils literal notranslate"><span class="pre">SOLVER</span></code> for later use.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
+ 
+<span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
+    <span class="o">%</span><span class="k">pip</span> install pyomo &gt;/dev/null 2&gt;/dev/null
+    <span class="o">%</span><span class="k">pip</span> install highspy &gt;/dev/null 2&gt;/dev/null
+ 
+<span class="n">solver</span> <span class="o">=</span> <span class="s1">&#39;appsi_highs&#39;</span>
+ 
+<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+<span class="n">SOLVER</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span>
+ 
+<span class="k">assert</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">solver</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="problem-description">
+<h2>Problem description<a class="headerlink" href="#problem-description" title="Permalink to this heading">#</a></h2>
+<p>We consider again the <span class="xref myst">BIM raw material planning problem</span> from Chapter 2 but with more sophisticated pricing and acquisition protocols. There are now three suppliers, each of which can deliver the following materials:</p>
+<ul class="simple">
+<li><p>A: <strong>silicon</strong>, <strong>germanium</strong> and <strong>plastic</strong></p></li>
+<li><p>B: <strong>copper</strong></p></li>
+<li><p>C: all the above</p></li>
+</ul>
+<p>For the suppliers, the following conditions apply: copper should be acquired in multiples of 100 gram, since it is delivered in sheets of that weight. Unitary materials such as silicon, germanium and plastic may be acquired in any number, but the price is in batches of 100. Meaning that 30 units of silicon with 10 units of germanium and 50 units of plastic cost as much as 1 unit of silicon but half as much as 30 units of silicon with 30 units of germanium and 50 units of plastic. Furthermore, supplier C sells all materials and offers a discount if purchased together: 100 gram of copper and a batch of unitary material cost only 7. This set price is only applied to pairs, meaning that 100 gram of copper and 2 batches cost 13.</p>
+<p>The summary of the prices in $ is given in the following table:</p>
+<div align="center">
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head text-left"><p>Supplier</p></th>
+<th class="head text-right"><p>Copper per sheet of 100 gram</p></th>
+<th class="head text-right"><p>Batches</p></th>
+<th class="head text-right"><p>Together</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-left"><p>A</p></td>
+<td class="text-right"><p>-</p></td>
+<td class="text-right"><p>5</p></td>
+<td class="text-right"><p>-</p></td>
+</tr>
+<tr class="row-odd"><td class="text-left"><p>B</p></td>
+<td class="text-right"><p>3</p></td>
+<td class="text-right"><p>-</p></td>
+<td class="text-right"><p>-</p></td>
+</tr>
+<tr class="row-even"><td class="text-left"><p>C</p></td>
+<td class="text-right"><p>4</p></td>
+<td class="text-right"><p>6</p></td>
+<td class="text-right"><p>7</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Next, for stocked products inventory costs are incurred, whose summary is given in the following table:</p>
+<div align="center">
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head text-right"><p>Copper per 10 gram</p></th>
+<th class="head text-right"><p>Silicon per unit</p></th>
+<th class="head text-right"><p>Germanium per unit</p></th>
+<th class="head text-right"><p>Plastic per unit</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-right"><p>0.1</p></td>
+<td class="text-right"><p>0.02</p></td>
+<td class="text-right"><p>0.02</p></td>
+<td class="text-right"><p>0.02</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>The holding price of copper is per 10 gram and the copper stocked is rounded up to multiples of 10 grams, meaning that 12 grams pay for 20. The capacity limitations of the warehouse allow for a maximum of <span class="math notranslate nohighlight">\(10\)</span> kilograms of copper in stock at any moment, but there are no practical limitations to the number of units of unitary products in stock. The production is made-to-order, meaning that no inventory of chips is kept.</p>
+<p>Recall that BIM has the following stock at the beginning of the year:</p>
+<div align="center">
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head text-right"><p>Copper</p></th>
+<th class="head text-right"><p>Silicon</p></th>
+<th class="head text-right"><p>Germanium</p></th>
+<th class="head text-right"><p>Plastic</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-right"><p>480</p></td>
+<td class="text-right"><p>1000</p></td>
+<td class="text-right"><p>1500</p></td>
+<td class="text-right"><p>1750</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>The company would like to have at least the following stock at the end of the year:</p>
+<div align="center">
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head text-right"><p>Copper</p></th>
+<th class="head text-right"><p>Silicon</p></th>
+<th class="head text-right"><p>Germanium</p></th>
+<th class="head text-right"><p>Plastic</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-right"><p>200</p></td>
+<td class="text-right"><p>500</p></td>
+<td class="text-right"><p>500</p></td>
+<td class="text-right"><p>1000</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>The goal is to build an optimization model using the data above and solve it to minimize the acquisition and holding costs of the products while meeting the required quantities for production.</p>
+</section>
+<section id="data-import">
+<h2>Data import<a class="headerlink" href="#data-import" title="Permalink to this heading">#</a></h2>
+<p>In the next cell we import and store the data described in dictionaries or Pandas dataframes.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="n">StringIO</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+
+<span class="n">demand_data</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">chip, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec</span>
+<span class="s2">logic, 88, 125, 260, 217, 238, 286, 248, 238, 265, 293, 259, 244</span>
+<span class="s2">memory, 47, 62, 81, 65, 95, 118, 86, 89, 82, 82, 84, 66</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+<span class="n">demand_chips</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">demand_data</span><span class="p">),</span> <span class="n">index_col</span><span class="o">=</span><span class="s2">&quot;chip&quot;</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span><span class="n">demand_chips</span><span class="p">)</span>
+
+<span class="n">use</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+<span class="n">use</span><span class="p">[</span><span class="s2">&quot;logic&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;silicon&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">}</span>
+<span class="n">use</span><span class="p">[</span><span class="s2">&quot;memory&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;germanium&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
+<span class="n">use</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">use</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span><span class="n">use</span><span class="p">)</span>
+
+<span class="n">demand</span> <span class="o">=</span> <span class="n">use</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">demand_chips</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+    <tr>
+      <th>chip</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>logic</th>
+      <td>88</td>
+      <td>125</td>
+      <td>260</td>
+      <td>217</td>
+      <td>238</td>
+      <td>286</td>
+      <td>248</td>
+      <td>238</td>
+      <td>265</td>
+      <td>293</td>
+      <td>259</td>
+      <td>244</td>
+    </tr>
+    <tr>
+      <th>memory</th>
+      <td>47</td>
+      <td>62</td>
+      <td>81</td>
+      <td>65</td>
+      <td>95</td>
+      <td>118</td>
+      <td>86</td>
+      <td>89</td>
+      <td>82</td>
+      <td>82</td>
+      <td>84</td>
+      <td>66</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>logic</th>
+      <th>memory</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>silicon</th>
+      <td>1</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>plastic</th>
+      <td>1</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>copper</th>
+      <td>4</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>germanium</th>
+      <td>0</td>
+      <td>1</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>silicon</th>
+      <td>88</td>
+      <td>125</td>
+      <td>260</td>
+      <td>217</td>
+      <td>238</td>
+      <td>286</td>
+      <td>248</td>
+      <td>238</td>
+      <td>265</td>
+      <td>293</td>
+      <td>259</td>
+      <td>244</td>
+    </tr>
+    <tr>
+      <th>plastic</th>
+      <td>135</td>
+      <td>187</td>
+      <td>341</td>
+      <td>282</td>
+      <td>333</td>
+      <td>404</td>
+      <td>334</td>
+      <td>327</td>
+      <td>347</td>
+      <td>375</td>
+      <td>343</td>
+      <td>310</td>
+    </tr>
+    <tr>
+      <th>copper</th>
+      <td>446</td>
+      <td>624</td>
+      <td>1202</td>
+      <td>998</td>
+      <td>1142</td>
+      <td>1380</td>
+      <td>1164</td>
+      <td>1130</td>
+      <td>1224</td>
+      <td>1336</td>
+      <td>1204</td>
+      <td>1108</td>
+    </tr>
+    <tr>
+      <th>germanium</th>
+      <td>47</td>
+      <td>62</td>
+      <td>81</td>
+      <td>65</td>
+      <td>95</td>
+      <td>118</td>
+      <td>86</td>
+      <td>89</td>
+      <td>82</td>
+      <td>82</td>
+      <td>84</td>
+      <td>66</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="optimization-model">
+<h2>Optimization model<a class="headerlink" href="#optimization-model" title="Permalink to this heading">#</a></h2>
+<p>As usual, we need to start modeling by naming our decisions and identifying the relevant index sets. In the <span class="xref myst">previous BIM notebook</span> we already had a set <span class="math notranslate nohighlight">\(T\)</span> time periods (i.e., the months) and a set <span class="math notranslate nohighlight">\(P\)</span> for the products. Since we want to keep track of how much we buy for which material and from which supplier, we need an additional index set <span class="math notranslate nohighlight">\(S\)</span> for the suppliers.</p>
+<p>In the <span class="xref myst">previous BIM notebook</span> all decision variables were continuous, but now we also need to take care of a number of integer decision variables:</p>
+<ul class="simple">
+<li><p>number of batches of the unitary products;</p></li>
+<li><p>number of copper sheets;</p></li>
+<li><p>number of volume units in stock, since the inventory of copper pays per <span class="math notranslate nohighlight">\(10\)</span> g;</p></li>
+<li><p>number of copper sheet-batch of unitary product pairs acquired in the same month from supplier C, yielding discount.</p></li>
+</ul>
+<p>Given the above, we can clearly reuse the variables <span class="math notranslate nohighlight">\(s_{pt}\)</span> from <span class="xref myst">previous BIM notebook</span> to account for the total number of grams/units of each product. With respect to purchasing variables, however, we now need to keep track of unitary products and suppliers. For this reason, we introduce the integer variables <span class="math notranslate nohighlight">\(y_{ts}\)</span> for the number of copper sheets purchased from supplier <span class="math notranslate nohighlight">\(s\)</span>, and continuous variables <span class="math notranslate nohighlight">\(x_{pts}\)</span> for the number of units of unitary product <span class="math notranslate nohighlight">\(p\)</span> purchased from supplier <span class="math notranslate nohighlight">\(s\)</span>. To translate these variables into `amounts of a product purchased regardless of the supplier’, we introduce another variable <span class="math notranslate nohighlight">\(u_{pt}\)</span>.</p>
+<p>Further, we introduce the variables that we use to account for the tricky integer quantities in our model. To account for the number of batches of unitary products at supplier <span class="math notranslate nohighlight">\(s\)</span>, we introduce integer variables <span class="math notranslate nohighlight">\(b_{ts}\)</span>, and, similarly, we introduce an integer variable <span class="math notranslate nohighlight">\(r_t\)</span> for the number of 10 g copper batches in the inventory. Buying a copper sheet together with a batch of unitary products from supplier C is advantageous as we get a discount of <span class="math notranslate nohighlight">\(\beta = 4 + 6 - 7\)</span>. For this reason, we introduce an extra integer variable <span class="math notranslate nohighlight">\(p_t\)</span> to count the number of such pairs, so that <span class="math notranslate nohighlight">\(\beta p_t\)</span> describes the amount to the total discount acquired in month <span class="math notranslate nohighlight">\(t\)</span>. We need to include additional constraints to make sure that these three variables `take the values they should’ in relation to the purchasing and inventory variables.</p>
+<p>Taking all the above into account, we shall use the following decision variables:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(y_{ts}\)</span>: number of sheets of copper purchased in month <span class="math notranslate nohighlight">\(t\)</span> from supplier <span class="math notranslate nohighlight">\(s\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(x_{pts}\)</span>: number of units of unitary product <span class="math notranslate nohighlight">\(p\)</span> purchased in month <span class="math notranslate nohighlight">\(t\)</span> from supplier <span class="math notranslate nohighlight">\(s\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(u_{pt}\)</span>: total number of units of product <span class="math notranslate nohighlight">\(p\)</span> purchased in month <span class="math notranslate nohighlight">\(t\)</span> (from all suppliers combined)</p></li>
+<li><p><span class="math notranslate nohighlight">\(b_{ts}\)</span>: number of batches of 100 unitary products purchased in month <span class="math notranslate nohighlight">\(t\)</span> from supplier <span class="math notranslate nohighlight">\(s\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(p_{t}\)</span>: number of batch-copper sheet pairs purchased in month <span class="math notranslate nohighlight">\(t\)</span> from supplier C</p></li>
+<li><p><span class="math notranslate nohighlight">\(r_t\)</span>: number of 10-unit amounts of copper (rounded up) in month <span class="math notranslate nohighlight">\(t\)</span>.</p></li>
+</ul>
+<p>We begin by formulating the objective function using $ cents as unit. If we denote by <span class="math notranslate nohighlight">\(\alpha_s\)</span> the per-batch cost of unitary products at supplier <span class="math notranslate nohighlight">\(s\)</span>, then the total material acquisition cost is</p>
+<div class="math notranslate nohighlight">
+\[
+\begin{equation*}
+    \sum_{t \in T} \Big( \sum_{s \in S} \pi_{s} b_{ts} + \alpha_{s} y_{ts} \Big) - \beta p_t \Big),
+\end{equation*}
+\]</div>
+<p>where we count the acquisition costs for supplier <span class="math notranslate nohighlight">\(C\)</span> as the total cost of the sheets of copper plus the batches of unitary materials minus the discount due to purchasing the pairs:</p>
+<div class="math notranslate nohighlight">
+\[
+\begin{equation*}
+    \pi_{\text{C}} b_{t, \text{C}} + \alpha_{\text{C}} y_{t, \text{C}}  - \beta p_t.
+\end{equation*}
+\]</div>
+<p>The total inventory cost is</p>
+<div class="math notranslate nohighlight">
+\[
+\begin{equation*}
+    \sum_{t \in T} \Big(  0.1 r_t + \sum_{p \in P \setminus\{\text{copper}\}} h_p s_{pt}  \Big).
+\end{equation*}
+\]</div>
+<p>Now, we need to construct a system of constraints to ensure that the above costs are computed correctly. To transform the amounts of unitary materials purchased into batches of <span class="math notranslate nohighlight">\(100\)</span>, we construct the following constraints:</p>
+<div class="math notranslate nohighlight">
+\[
+\begin{equation*}
+    \sum_{p \in P \setminus\{\text{copper}\}}  x_{pts} \leq 100 b_{ts}.
+\end{equation*}
+\]</div>
+<p>Because the inventory costs for copper are computed by rounding up to 10 g amounts, we account for this via the following constraint</p>
+<div class="math notranslate nohighlight">
+\[
+\begin{equation*}
+    s_{\text{copper}, t} \leq 10 r_t.
+\end{equation*}
+\]</div>
+<p>In the end, we can account for the number of sheets of copper-unitary material batch pairs purchased at supplier C, we can use the following constraints:</p>
+<div class="math notranslate nohighlight">
+\[
+\begin{equation*}
+    p_t \leq b_{t,\text{C}}, \quad \text{ and } \quad p_t \leq y_{t, \text{C}}.
+\end{equation*}
+\]</div>
+<p>Choosing the maximum possible amount <span class="math notranslate nohighlight">\(p_t\)</span> that satisfies such constraints will naturally lead to maximizing the number of pairs and, thus, the total discount.</p>
+<p>The remaining constraints such as the inventory balance constraints, total inventory capacity etc.~are straightforward, which gives us the following complete model formulation:</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}
+\begin{align*}
+    \min \quad &amp; \sum_{t \in T} \Big( \Big( \sum_{s \in S} \pi_{s} b_{ts} + \alpha_{s} y_{ts} \Big) - \beta p_t \Big) +&amp;
+    \sum_{t \in T} \Big (  \gamma_{\text{copper}} r_t + \sum_{p \in P \setminus \{ \text{copper}\}} \gamma_{p} s_{pt}  \Big) \\
+    \textup{s.t.} \quad 
+    &amp; \sum_{p \in P \setminus \{ \text{copper}\}} x_{pts} \leq 100 \cdot b_{ts} &amp; \forall \, t \in T, \, \forall \, s \in S\\
+    &amp; s_{\text{copper}, t} \leq 10 \cdot r_t &amp; \forall \, t \in T\\
+    &amp; s_{\text{copper}, t} \leq 10000 &amp; \forall \, t \in T\\
+    &amp; u_{\text{copper}, t} = 100 \sum_{s \in S} y_{ts} &amp; \forall \, t \in T\\
+    &amp; p_t \leq b_{t,\text{C}}, \quad p_t \leq y_{t, \text{C}} &amp; \forall \, t \in T \\
+    &amp; u_{pt} = \sum_{s \in S} x_{pts} &amp; \forall \, t \in T, \, \forall P \setminus \{ \text{copper}\}\\
+    &amp; s_{p,t-1} + u_{p,t} = \delta_{pt} + s_{pt} &amp; \forall \, t \in T, \, \forall p \in P\\ 
+    &amp; s_{p \textrm{Dec}} \geq \Omega_p &amp;  \forall \, p \in P \\
+    &amp; b_{ts}, r_t \in \mathbb{Z}_{+} &amp; \forall \, t \in T, \, \forall \, s \in S,
+\end{align*}
+\end{split}\]</div>
+<p>where we assume that <span class="math notranslate nohighlight">\(s_{p,t-1}\)</span> represents the initial inventory state. Note that because <span class="math notranslate nohighlight">\(r_t\)</span>, <span class="math notranslate nohighlight">\(b_{ts}\)</span> appear with a non-negative coefficient and <span class="math notranslate nohighlight">\(p_t\)</span> with a positive coefficient in the objective functions, at the optimal solution these variables will automatically take their minimal (<span class="math notranslate nohighlight">\(r_t\)</span>, <span class="math notranslate nohighlight">\(b_{ts}\)</span>) and maximal (<span class="math notranslate nohighlight">\(p_t\)</span>) values allowed by the corresponding constraints.</p>
+</section>
+<section id="pyomo-implementation">
+<h2>Pyomo implementation<a class="headerlink" href="#pyomo-implementation" title="Permalink to this heading">#</a></h2>
+<p>In the next cell, we implement the model described above in Pyomo using a function that can take the data as input arguments, so we can then easily use it with different data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">BIMproduction_v1</span><span class="p">(</span>
+    <span class="n">demand</span><span class="p">,</span>
+    <span class="n">existing</span><span class="p">,</span>
+    <span class="n">desired</span><span class="p">,</span>
+    <span class="n">stock_limit</span><span class="p">,</span>
+    <span class="n">supplying_copper</span><span class="p">,</span>
+    <span class="n">supplying_batches</span><span class="p">,</span>
+    <span class="n">price_copper_sheet</span><span class="p">,</span>
+    <span class="n">price_batch</span><span class="p">,</span>
+    <span class="n">discounted_price</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">copper_sheet_mass</span><span class="p">,</span>
+    <span class="n">copper_bucket_size</span><span class="p">,</span>
+    <span class="n">unitary_products</span><span class="p">,</span>
+    <span class="n">unitary_holding_costs</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span>
+        <span class="s2">&quot;BIM product acquisition and inventory with sophisticated prices&quot;</span>
+    <span class="p">)</span>
+
+    <span class="n">periods</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span>
+    <span class="n">products</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">index</span>
+    <span class="n">first</span> <span class="o">=</span> <span class="n">periods</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">prev</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">periods</span><span class="p">[</span><span class="mi">1</span><span class="p">:])}</span>
+    <span class="n">last</span> <span class="o">=</span> <span class="n">periods</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">products</span><span class="p">,</span> <span class="n">periods</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">delta</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">demand</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">supplying_batches</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">pi</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">price_batch</span><span class="p">[</span><span class="n">s</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">supplying_copper</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kappa</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">price_copper_sheet</span><span class="p">[</span><span class="n">s</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">()</span>
+    <span class="k">def</span> <span class="nf">beta</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">price_batch</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">price_copper_sheet</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">discounted_price</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">products</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">gamma</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">unitary_holding_costs</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">products</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">Alpha</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">existing</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">products</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">Omega</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">desired</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+
+    <span class="n">m</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">supplying_copper</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span>
+        <span class="n">unitary_products</span><span class="p">,</span> <span class="n">periods</span><span class="p">,</span> <span class="n">supplying_batches</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span>
+    <span class="p">)</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">s</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">products</span><span class="p">,</span> <span class="n">periods</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">u</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">products</span><span class="p">,</span> <span class="n">periods</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">supplying_batches</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">r</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">supplying_batches</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">units_in_batches</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">unitary_products</span><span class="p">)</span>
+            <span class="o">&lt;=</span> <span class="n">batch_size</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">b</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span>
+        <span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">periods</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">copper_in_buckets</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;copper&quot;</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">copper_bucket_size</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">periods</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">inventory_capacity</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;copper&quot;</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">stock_limit</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">periods</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">pairs_in_batches</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">b</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">periods</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">pairs_in_sheets</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">products</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">bought</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">p</span> <span class="o">==</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="n">copper_sheet_mass</span> <span class="o">*</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span>
+                <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_copper</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_batches</span><span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Expression</span><span class="p">()</span>
+    <span class="k">def</span> <span class="nf">acquisition_cost</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span>
+            <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">pi</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">b</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_batches</span><span class="p">)</span>
+            <span class="o">+</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">kappa</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_copper</span><span class="p">)</span>
+            <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">beta</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">periods</span>
+        <span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Expression</span><span class="p">()</span>
+    <span class="k">def</span> <span class="nf">inventory_cost</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span>
+            <span class="n">m</span><span class="o">.</span><span class="n">gamma</span><span class="p">[</span><span class="s2">&quot;copper&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
+            <span class="o">+</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">gamma</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">unitary_products</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">periods</span>
+        <span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">minimize</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">total_cost</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">acquisition_cost</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">inventory_cost</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">products</span><span class="p">,</span> <span class="n">periods</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">balance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">t</span> <span class="o">==</span> <span class="n">first</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">Alpha</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">delta</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">prev</span><span class="p">[</span><span class="n">t</span><span class="p">]]</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">delta</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">products</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">finish</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">last</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">Omega</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+
+    <span class="n">SOLVER</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">m</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We then solve the model using the provided data. We introduce three auxiliary function to more quickly parse the output of the model and use them to report the solution as a set of Pandas dataframes and visualize the monthly stock levels.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">Table1d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">retriever</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="mi">0</span> <span class="o">+</span> <span class="n">retriever</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">J</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">J</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+
+<span class="k">def</span> <span class="nf">Table2d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">I</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">retriever</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</span><span class="p">(</span>
+        <span class="p">[[</span><span class="nb">int</span><span class="p">(</span><span class="mi">0</span> <span class="o">+</span> <span class="n">retriever</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">J</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">I</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">I</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">J</span>
+    <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">Table3d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">I</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">names</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">retriever</span><span class="p">):</span>
+    <span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_product</span><span class="p">([</span><span class="n">I</span><span class="p">,</span> <span class="n">J</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</span><span class="p">(</span>
+        <span class="p">[[</span><span class="nb">int</span><span class="p">(</span><span class="mi">0</span> <span class="o">+</span> <span class="n">retriever</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">K</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">I</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">J</span><span class="p">],</span>
+        <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="n">K</span><span class="p">,</span>
+    <span class="p">)</span>
+
+
+<span class="n">m1</span> <span class="o">=</span> <span class="n">BIMproduction_v1</span><span class="p">(</span>
+    <span class="n">demand</span><span class="o">=</span><span class="n">demand</span><span class="p">,</span>
+    <span class="n">existing</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;silicon&quot;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">:</span> <span class="mi">1500</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">1750</span><span class="p">,</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">4800</span><span class="p">},</span>
+    <span class="n">desired</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;silicon&quot;</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">2000</span><span class="p">},</span>
+    <span class="n">stock_limit</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span>
+    <span class="n">supplying_copper</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span>
+    <span class="n">supplying_batches</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span>
+    <span class="n">price_copper_sheet</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">300</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mi">400</span><span class="p">},</span>
+    <span class="n">price_batch</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mi">600</span><span class="p">},</span>
+    <span class="n">discounted_price</span><span class="o">=</span><span class="mi">700</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">copper_sheet_mass</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">copper_bucket_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="n">unitary_products</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;silicon&quot;</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">],</span>
+    <span class="n">unitary_holding_costs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;silicon&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Optimal cost is </span><span class="si">{</span><span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m1</span><span class="o">.</span><span class="n">total_cost</span><span class="p">)</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> cents, that is $</span><span class="si">{</span><span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m1</span><span class="o">.</span><span class="n">total_cost</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Monthly purchased material&quot;</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span><span class="n">Table2d</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">demand</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">])))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Acquisition plan for copper sheet and batch pairs from supplier C&quot;</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span><span class="n">Table1d</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">J</span><span class="o">=</span><span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">retriever</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">j</span><span class="p">])))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Acquisition plan for copper sheets materials&quot;</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span><span class="n">Table2d</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">])))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Acquisition plan for unitary materials (batches)&quot;</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span><span class="n">Table2d</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">b</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">])))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Acquisition plan for unitary materials (quantities)&quot;</span><span class="p">)</span>
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">Table3d</span><span class="p">(</span>
+        <span class="n">m1</span><span class="p">,</span>
+        <span class="n">I</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span>
+        <span class="n">J</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;silicon&quot;</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">],</span>
+        <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;supplier&quot;</span><span class="p">,</span> <span class="s2">&quot;materials&quot;</span><span class="p">],</span>
+        <span class="n">K</span><span class="o">=</span><span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span>
+        <span class="n">retriever</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">:</span> <span class="mi">0</span> <span class="o">+</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">]),</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;The stock levels at the end of each month resulting from the optimal acquisition and production plan&quot;</span>
+<span class="p">)</span>
+<span class="n">stock</span> <span class="o">=</span> <span class="n">Table2d</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">demand</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]))</span>
+<span class="n">display</span><span class="p">(</span><span class="n">stock</span><span class="p">)</span>
+<span class="n">stock</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">drawstyle</span><span class="o">=</span><span class="s2">&quot;steps-mid&quot;</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stock</span><span class="o">.</span><span class="n">columns</span><span class="p">)),</span> <span class="n">stock</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]
+Copyright (c) 2023 HiGHS under MIT licence terms
+Optimal cost is 110216 cents, that is $1102.16
+
+Monthly purchased material
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>silicon</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>228</td>
+      <td>233</td>
+      <td>274</td>
+      <td>228</td>
+      <td>349</td>
+      <td>257</td>
+      <td>690</td>
+    </tr>
+    <tr>
+      <th>plastic</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>266</td>
+      <td>326</td>
+      <td>371</td>
+      <td>351</td>
+      <td>343</td>
+      <td>1310</td>
+    </tr>
+    <tr>
+      <th>copper</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>1000</td>
+      <td>1200</td>
+      <td>1100</td>
+      <td>1300</td>
+      <td>1300</td>
+      <td>1200</td>
+      <td>3100</td>
+    </tr>
+    <tr>
+      <th>germanium</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Acquisition plan for copper sheet and batch pairs from supplier C
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>3</td>
+      <td>5</td>
+      <td>6</td>
+      <td>6</td>
+      <td>7</td>
+      <td>6</td>
+      <td>20</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Acquisition plan for copper sheets materials
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>B</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>7</td>
+      <td>7</td>
+      <td>4</td>
+      <td>7</td>
+      <td>6</td>
+      <td>6</td>
+      <td>11</td>
+    </tr>
+    <tr>
+      <th>C</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>3</td>
+      <td>5</td>
+      <td>6</td>
+      <td>6</td>
+      <td>7</td>
+      <td>6</td>
+      <td>20</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Acquisition plan for unitary materials (batches)
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>A</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>C</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>3</td>
+      <td>5</td>
+      <td>6</td>
+      <td>6</td>
+      <td>7</td>
+      <td>6</td>
+      <td>20</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Acquisition plan for unitary materials (quantities)
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+    <tr>
+      <th>supplier</th>
+      <th>materials</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th rowspan="3" valign="top">A</th>
+      <th>silicon</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>germanium</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>plastic</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">C</th>
+      <th>silicon</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>228</td>
+      <td>233</td>
+      <td>274</td>
+      <td>228</td>
+      <td>349</td>
+      <td>257</td>
+      <td>690</td>
+    </tr>
+    <tr>
+      <th>germanium</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>plastic</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>266</td>
+      <td>326</td>
+      <td>371</td>
+      <td>351</td>
+      <td>343</td>
+      <td>1310</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The stock levels at the end of each month resulting from the optimal acquisition and production plan
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>silicon</th>
+      <td>912</td>
+      <td>787</td>
+      <td>527</td>
+      <td>310</td>
+      <td>72</td>
+      <td>14</td>
+      <td>0</td>
+      <td>36</td>
+      <td>0</td>
+      <td>56</td>
+      <td>54</td>
+      <td>500</td>
+    </tr>
+    <tr>
+      <th>plastic</th>
+      <td>1615</td>
+      <td>1428</td>
+      <td>1087</td>
+      <td>805</td>
+      <td>472</td>
+      <td>68</td>
+      <td>0</td>
+      <td>0</td>
+      <td>24</td>
+      <td>0</td>
+      <td>0</td>
+      <td>1000</td>
+    </tr>
+    <tr>
+      <th>copper</th>
+      <td>4354</td>
+      <td>3730</td>
+      <td>2528</td>
+      <td>1530</td>
+      <td>388</td>
+      <td>8</td>
+      <td>44</td>
+      <td>14</td>
+      <td>90</td>
+      <td>54</td>
+      <td>50</td>
+      <td>2042</td>
+    </tr>
+    <tr>
+      <th>germanium</th>
+      <td>1453</td>
+      <td>1391</td>
+      <td>1310</td>
+      <td>1245</td>
+      <td>1150</td>
+      <td>1032</td>
+      <td>946</td>
+      <td>857</td>
+      <td>775</td>
+      <td>693</td>
+      <td>609</td>
+      <td>543</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><img alt="../../_images/6c183a43527ba8eeb8f935c343c37fe2b34c63be871a38835736191f0a92e84c.png" src="../../_images/6c183a43527ba8eeb8f935c343c37fe2b34c63be871a38835736191f0a92e84c.png" />
+</div>
+</div>
+</section>
+<section id="alternative-pyomo-implementation-using-blocks">
+<h2>Alternative Pyomo implementation using blocks<a class="headerlink" href="#alternative-pyomo-implementation-using-blocks" title="Permalink to this heading">#</a></h2>
+<p>In the next cell, we implement a second version of the same model, which is equivalent to the previous one, but uses Pyomo sets and block components to iterate over all the time indices.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">BIMproduction_v2</span><span class="p">(</span>
+    <span class="n">demand</span><span class="p">,</span>
+    <span class="n">existing</span><span class="p">,</span>
+    <span class="n">desired</span><span class="p">,</span>
+    <span class="n">stock_limit</span><span class="p">,</span>
+    <span class="n">supplying_copper</span><span class="p">,</span>
+    <span class="n">supplying_batches</span><span class="p">,</span>
+    <span class="n">price_copper_sheet</span><span class="p">,</span>
+    <span class="n">price_batch</span><span class="p">,</span>
+    <span class="n">discounted_price</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">copper_sheet_mass</span><span class="p">,</span>
+    <span class="n">copper_bucket_size</span><span class="p">,</span>
+    <span class="n">unitary_products</span><span class="p">,</span>
+    <span class="n">unitary_holding_costs</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span>
+        <span class="s2">&quot;BIM product acquisition and inventory with sophisticated prices (using blocks)&quot;</span>
+    <span class="p">)</span>
+
+    <span class="n">periods</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span>
+    <span class="n">products</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">index</span>
+    <span class="n">first</span> <span class="o">=</span> <span class="n">periods</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">prev</span> <span class="o">=</span> <span class="p">{</span><span class="n">j</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">periods</span><span class="p">,</span> <span class="n">periods</span><span class="p">[</span><span class="mi">1</span><span class="p">:])}</span>
+    <span class="n">last</span> <span class="o">=</span> <span class="n">periods</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">m</span><span class="o">.</span><span class="n">T</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">periods</span><span class="p">)</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">P</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">products</span><span class="p">)</span>
+
+    <span class="n">m</span><span class="o">.</span><span class="n">PT</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">P</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">T</span>  <span class="c1"># to avoid internal set bloat</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Block</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">A</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">supplying_batches</span><span class="p">,</span> <span class="n">products</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">supplying_batches</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">supplying_copper</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">supplying_batches</span><span class="p">)</span>
+        <span class="k">def</span> <span class="nf">in_batches</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">products</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">batch_size</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">b</span><span class="p">[</span><span class="n">s</span><span class="p">]</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
+        <span class="k">def</span> <span class="nf">pairs_in_batches</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">b</span><span class="o">.</span><span class="n">p</span> <span class="o">&lt;=</span> <span class="n">b</span><span class="o">.</span><span class="n">b</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
+        <span class="k">def</span> <span class="nf">pairs_in_sheets</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">b</span><span class="o">.</span><span class="n">p</span> <span class="o">&lt;=</span> <span class="n">b</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Expression</span><span class="p">(</span><span class="n">products</span><span class="p">)</span>
+        <span class="k">def</span> <span class="nf">u</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">p</span> <span class="o">==</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">copper_sheet_mass</span> <span class="o">*</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span>
+                    <span class="n">b</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_copper</span>
+                <span class="p">)</span>
+            <span class="k">return</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_batches</span><span class="p">)</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Expression</span><span class="p">()</span>
+        <span class="k">def</span> <span class="nf">cost</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="n">discount</span> <span class="o">=</span> <span class="n">price_batch</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">price_copper_sheet</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">discounted_price</span>
+            <span class="k">return</span> <span class="p">(</span>
+                <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">price_copper_sheet</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_copper</span><span class="p">)</span>
+                <span class="o">+</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">price_batch</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">b</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">supplying_batches</span><span class="p">)</span>
+                <span class="o">-</span> <span class="n">discount</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">p</span>
+            <span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Block</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">I</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">s</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">products</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">r</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
+        <span class="k">def</span> <span class="nf">copper_in_buckets</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">b</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;copper&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">copper_bucket_size</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">r</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
+        <span class="k">def</span> <span class="nf">capacity</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">b</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;copper&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">stock_limit</span>
+
+        <span class="nd">@b</span><span class="o">.</span><span class="n">Expression</span><span class="p">()</span>
+        <span class="k">def</span> <span class="nf">cost</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">unitary_holding_costs</span><span class="p">[</span><span class="s2">&quot;copper&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">r</span> <span class="o">+</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span>
+                <span class="n">unitary_holding_costs</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">unitary_products</span>
+            <span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">PT</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">delta</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">demand</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">p</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Expression</span><span class="p">()</span>
+    <span class="k">def</span> <span class="nf">acquisition_cost</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">A</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">cost</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Expression</span><span class="p">()</span>
+    <span class="k">def</span> <span class="nf">inventory_cost</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">pyo</span><span class="o">.</span><span class="n">quicksum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">I</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">cost</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">minimize</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">total_cost</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">acquisition_cost</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">inventory_cost</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">PT</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">balance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">t</span> <span class="o">==</span> <span class="n">first</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">existing</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">A</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">delta</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">I</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">A</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">I</span><span class="p">[</span><span class="n">prev</span><span class="p">[</span><span class="n">t</span><span class="p">]]</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">delta</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">I</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+
+    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">P</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">finish</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">I</span><span class="p">[</span><span class="n">last</span><span class="p">]</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">desired</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+
+    <span class="n">SOLVER</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">m</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can then pass the parameters and data to this Pyomo model and solve it, as illustrated in the following code cell. We report the solution as a set of Pandas dataframes and visualize the monthly stock levels.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m2</span> <span class="o">=</span> <span class="n">BIMproduction_v2</span><span class="p">(</span>
+    <span class="n">demand</span><span class="o">=</span><span class="n">demand</span><span class="p">,</span>
+    <span class="n">existing</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;silicon&quot;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">:</span> <span class="mi">1500</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">1750</span><span class="p">,</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">4800</span><span class="p">},</span>
+    <span class="n">desired</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;silicon&quot;</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">2000</span><span class="p">},</span>
+    <span class="n">stock_limit</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span>
+    <span class="n">supplying_copper</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span>
+    <span class="n">supplying_batches</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span>
+    <span class="n">price_copper_sheet</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">300</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mi">400</span><span class="p">},</span>
+    <span class="n">price_batch</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mi">600</span><span class="p">},</span>
+    <span class="n">discounted_price</span><span class="o">=</span><span class="mi">700</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">copper_sheet_mass</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">copper_bucket_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="n">unitary_products</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;silicon&quot;</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">],</span>
+    <span class="n">unitary_holding_costs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;copper&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;silicon&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Optimal cost is </span><span class="si">{</span><span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m2</span><span class="o">.</span><span class="n">total_cost</span><span class="p">)</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> cents, that is $</span><span class="si">{</span><span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m2</span><span class="o">.</span><span class="n">total_cost</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+
+<span class="n">Table3d</span><span class="p">(</span>
+    <span class="n">m2</span><span class="p">,</span>
+    <span class="n">I</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">],</span>
+    <span class="n">J</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;silicon&quot;</span><span class="p">,</span> <span class="s2">&quot;germanium&quot;</span><span class="p">,</span> <span class="s2">&quot;plastic&quot;</span><span class="p">],</span>
+    <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;supplier&quot;</span><span class="p">,</span> <span class="s2">&quot;materials&quot;</span><span class="p">],</span>
+    <span class="n">K</span><span class="o">=</span><span class="n">m2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+    <span class="n">retriever</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">:</span> <span class="mi">0</span> <span class="o">+</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">A</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]),</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;The stock levels at the end of each month resulting from the optimal acquisition and production plan&quot;</span>
+<span class="p">)</span>
+<span class="n">stock</span> <span class="o">=</span> <span class="n">Table2d</span><span class="p">(</span><span class="n">m2</span><span class="p">,</span> <span class="n">I</span><span class="o">=</span><span class="n">m2</span><span class="o">.</span><span class="n">P</span><span class="p">,</span> <span class="n">J</span><span class="o">=</span><span class="n">m2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">retriever</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="n">pyo</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">I</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+<span class="n">display</span><span class="p">(</span><span class="n">stock</span><span class="p">)</span>
+<span class="n">stock</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">drawstyle</span><span class="o">=</span><span class="s2">&quot;steps-mid&quot;</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stock</span><span class="o">.</span><span class="n">columns</span><span class="p">)),</span> <span class="n">stock</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]
+Copyright (c) 2023 HiGHS under MIT licence terms
+Optimal cost is 110216 cents, that is $1102.16
+
+The stock levels at the end of each month resulting from the optimal acquisition and production plan
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Jan</th>
+      <th>Feb</th>
+      <th>Mar</th>
+      <th>Apr</th>
+      <th>May</th>
+      <th>Jun</th>
+      <th>Jul</th>
+      <th>Aug</th>
+      <th>Sep</th>
+      <th>Oct</th>
+      <th>Nov</th>
+      <th>Dec</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>silicon</th>
+      <td>912</td>
+      <td>787</td>
+      <td>527</td>
+      <td>310</td>
+      <td>72</td>
+      <td>15</td>
+      <td>0</td>
+      <td>35</td>
+      <td>24</td>
+      <td>0</td>
+      <td>54</td>
+      <td>500</td>
+    </tr>
+    <tr>
+      <th>plastic</th>
+      <td>1615</td>
+      <td>1427</td>
+      <td>1086</td>
+      <td>804</td>
+      <td>471</td>
+      <td>67</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>56</td>
+      <td>0</td>
+      <td>1000</td>
+    </tr>
+    <tr>
+      <th>copper</th>
+      <td>4354</td>
+      <td>3730</td>
+      <td>2528</td>
+      <td>1530</td>
+      <td>388</td>
+      <td>8</td>
+      <td>44</td>
+      <td>14</td>
+      <td>90</td>
+      <td>54</td>
+      <td>50</td>
+      <td>2042</td>
+    </tr>
+    <tr>
+      <th>germanium</th>
+      <td>1453</td>
+      <td>1391</td>
+      <td>1310</td>
+      <td>1245</td>
+      <td>1150</td>
+      <td>1032</td>
+      <td>946</td>
+      <td>857</td>
+      <td>775</td>
+      <td>693</td>
+      <td>609</td>
+      <td>543</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><img alt="../../_images/448a994bd8731d95a6a94d81dc9b4c4e18ae2413d5be05b7351d244da16c67f2.png" src="../../_images/448a994bd8731d95a6a94d81dc9b4c4e18ae2413d5be05b7351d244da16c67f2.png" />
+</div>
+</div>
+</section>
+</section>
+
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+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">3.6 Facility location problem</p>
+      </div>
+    </a>
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+        <p class="prev-next-title">Extra material: Cryptarithms puzzle</p>
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+    <i class="fa-solid fa-list"></i> Contents
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preamble-install-pyomo-and-a-solver">Preamble: Install Pyomo and a solver</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data-import">Data import</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#optimization-model">Optimization model</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyomo-implementation">Pyomo implementation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#alternative-pyomo-implementation-using-blocks">Alternative Pyomo implementation using blocks</a></li>
+</ul>
+  </nav></div>
+
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diff --git a/notebooks/03/08-cryptarithms.html b/notebooks/03/08-cryptarithms.html
index 1d73e574..8a866ff0 100644
--- a/notebooks/03/08-cryptarithms.html
+++ b/notebooks/03/08-cryptarithms.html
@@ -75,7 +75,7 @@
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="Extra material: Strip packing" href="09-strip-packing.html" />
-    <link rel="prev" title="3.5 Machine Scheduling" href="05-machine-scheduling.html" />
+    <link rel="prev" title="3.7 BIM production revisited" href="07-bim-production-revisited.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -694,12 +697,12 @@ <h2>Suggested exercises<a class="headerlink" href="#suggested-exercises" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="05-machine-scheduling.html"
+       href="07-bim-production-revisited.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">3.5 Machine Scheduling</p>
+        <p class="prev-next-title">3.7 BIM production revisited</p>
       </div>
     </a>
     <a class="right-next"
diff --git a/notebooks/03/09-strip-packing.html b/notebooks/03/09-strip-packing.html
index 28c6613e..ef31def0 100644
--- a/notebooks/03/09-strip-packing.html
+++ b/notebooks/03/09-strip-packing.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/03/10-job-shop-scheduling.html b/notebooks/03/10-job-shop-scheduling.html
index ddc2bae6..90abec50 100644
--- a/notebooks/03/10-job-shop-scheduling.html
+++ b/notebooks/03/10-job-shop-scheduling.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/03/11-maintenance-planning.html b/notebooks/03/11-maintenance-planning.html
index cafed78f..9849be15 100644
--- a/notebooks/03/11-maintenance-planning.html
+++ b/notebooks/03/11-maintenance-planning.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="03.00.html">3. Mixed Integer Linear Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/04/dinner-seat-allocation.html b/notebooks/04/01-dinner-seat-allocation.html
similarity index 96%
rename from notebooks/04/dinner-seat-allocation.html
rename to notebooks/04/01-dinner-seat-allocation.html
index 538c0869..71c691fb 100644
--- a/notebooks/04/dinner-seat-allocation.html
+++ b/notebooks/04/01-dinner-seat-allocation.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Dinner seating arrangement &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>4.1 Dinner seating arrangement &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,10 +71,10 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/dinner-seat-allocation';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/01-dinner-seat-allocation';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Minimum-Cost Flow Problem" href="mincost-flow.html" />
+    <link rel="next" title="4.2 Minimum-Cost Flow Problem" href="02-mincost-flow.html" />
     <link rel="prev" title="4. Network Optimization" href="04.00.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="04.00.html">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Dinner seating arrangement</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/dinner-seat-allocation.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/01-dinner-seat-allocation.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/dinner-seat-allocation.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/01-dinner-seat-allocation.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/04/dinner-seat-allocation.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/04/01-dinner-seat-allocation.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Dinner seating arrangement</h1>
+    <h1>4.1 Dinner seating arrangement</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -497,7 +500,7 @@ <h2> Contents </h2>
             </div>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
-<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Dinner seating arrangement</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">4.1 Dinner seating arrangement</a><ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preamble-install-pyomo-and-a-solver">Preamble: Install Pyomo and a solver</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#implementation">Implementation</a></li>
@@ -522,7 +525,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><span class="target" id="index-6"></span><section class="tex2jax_ignore mathjax_ignore" id="dinner-seating-arrangement">
-<span id="index-7"></span><h1>Dinner seating arrangement<a class="headerlink" href="#dinner-seating-arrangement" title="Permalink to this heading">#</a></h1>
+<span id="index-7"></span><h1>4.1 Dinner seating arrangement<a class="headerlink" href="#dinner-seating-arrangement" title="Permalink to this heading">#</a></h1>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object <code class="docutils literal notranslate"><span class="pre">SOLVER</span></code> for later use.</p>
@@ -1563,11 +1566,11 @@ <h2>A more systematic comparison between LO solvers and network algorithms<a cla
       </div>
     </a>
     <a class="right-next"
-       href="mincost-flow.html"
+       href="02-mincost-flow.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Minimum-Cost Flow Problem</p>
+        <p class="prev-next-title">4.2 Minimum-Cost Flow Problem</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1586,7 +1589,7 @@ <h2>A more systematic comparison between LO solvers and network algorithms<a cla
   </div>
   <nav class="bd-toc-nav page-toc">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Dinner seating arrangement</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">4.1 Dinner seating arrangement</a><ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preamble-install-pyomo-and-a-solver">Preamble: Install Pyomo and a solver</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#implementation">Implementation</a></li>
diff --git a/notebooks/04/mincost-flow.html b/notebooks/04/02-mincost-flow.html
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rename to notebooks/04/02-mincost-flow.html
index 54ee2cff..a48ea43a 100644
--- a/notebooks/04/mincost-flow.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Minimum-Cost Flow Problem &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>4.2 Minimum-Cost Flow Problem &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/mincost-flow';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/02-mincost-flow';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Gasoline distribution" href="gasoline-distribution.html" />
-    <link rel="prev" title="Dinner seating arrangement" href="dinner-seat-allocation.html" />
+    <link rel="next" title="4.3 Gasoline distribution" href="03-gasoline-distribution.html" />
+    <link rel="prev" title="4.1 Dinner seating arrangement" href="01-dinner-seat-allocation.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="04.00.html">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/mincost-flow.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/02-mincost-flow.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/mincost-flow.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/02-mincost-flow.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/04/mincost-flow.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/04/02-mincost-flow.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Minimum-Cost Flow Problem</h1>
+    <h1>4.2 Minimum-Cost Flow Problem</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -512,7 +515,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><section class="tex2jax_ignore mathjax_ignore" id="minimum-cost-flow-problem">
-<span id="index-4"></span><h1>Minimum-Cost Flow Problem<a class="headerlink" href="#minimum-cost-flow-problem" title="Permalink to this heading">#</a></h1>
+<span id="index-4"></span><h1>4.2 Minimum-Cost Flow Problem<a class="headerlink" href="#minimum-cost-flow-problem" title="Permalink to this heading">#</a></h1>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object <code class="docutils literal notranslate"><span class="pre">SOLVER</span></code> for later use.</p>
@@ -767,20 +770,20 @@ <h2>Mathematical formulation<a class="headerlink" href="#mathematical-formulatio
                   <!-- Previous / next buttons -->
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+       href="01-dinner-seat-allocation.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Dinner seating arrangement</p>
+        <p class="prev-next-title">4.1 Dinner seating arrangement</p>
       </div>
     </a>
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-       href="gasoline-distribution.html"
+       href="03-gasoline-distribution.html"
        title="next page">
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-        <p class="prev-next-title">Gasoline distribution</p>
+        <p class="prev-next-title">4.3 Gasoline distribution</p>
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     </a>
diff --git a/notebooks/04/gasoline-distribution.html b/notebooks/04/03-gasoline-distribution.html
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index 827a4b1a..c6233ebf 100644
--- a/notebooks/04/gasoline-distribution.html
+++ b/notebooks/04/03-gasoline-distribution.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Gasoline distribution &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>4.3 Gasoline distribution &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/gasoline-distribution';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/03-gasoline-distribution';</script>
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-    <link rel="next" title="Exam room scheduling" href="graph-coloring.html" />
-    <link rel="prev" title="Minimum-Cost Flow Problem" href="mincost-flow.html" />
+    <link rel="next" title="4.4 Exam room scheduling" href="04-exam-room-scheduling.html" />
+    <link rel="prev" title="4.2 Minimum-Cost Flow Problem" href="02-mincost-flow.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="04.00.html">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/gasoline-distribution.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/03-gasoline-distribution.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/gasoline-distribution.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/03-gasoline-distribution.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/04/gasoline-distribution.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/04/03-gasoline-distribution.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Gasoline distribution</h1>
+    <h1>4.3 Gasoline distribution</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -523,7 +526,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><section class="tex2jax_ignore mathjax_ignore" id="gasoline-distribution">
-<span id="index-4"></span><h1>Gasoline distribution<a class="headerlink" href="#gasoline-distribution" title="Permalink to this heading">#</a></h1>
+<span id="index-4"></span><h1>4.3 Gasoline distribution<a class="headerlink" href="#gasoline-distribution" title="Permalink to this heading">#</a></h1>
 <p>This notebook presents a transportation model to optimally allocate the delivery of a commodity from multiple sources to multiple destinations. The model invites a discussion of the pitfalls in optimizing a global objective for customers who may have an uneven share of the resulting benefits, then through model refinement arrives at a group cost-sharing plan to delivery costs.</p>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
@@ -2159,20 +2162,20 @@ <h3>Graphviz<a class="headerlink" href="#graphviz" title="Permalink to this head
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="mincost-flow.html"
+       href="02-mincost-flow.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Minimum-Cost Flow Problem</p>
+        <p class="prev-next-title">4.2 Minimum-Cost Flow Problem</p>
       </div>
     </a>
     <a class="right-next"
-       href="graph-coloring.html"
+       href="04-exam-room-scheduling.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Exam room scheduling</p>
+        <p class="prev-next-title">4.4 Exam room scheduling</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/notebooks/04/graph-coloring.html b/notebooks/04/04-exam-room-scheduling.html
similarity index 93%
rename from notebooks/04/graph-coloring.html
rename to notebooks/04/04-exam-room-scheduling.html
index ac8ce42f..2741bfd7 100644
--- a/notebooks/04/graph-coloring.html
+++ b/notebooks/04/04-exam-room-scheduling.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Exam room scheduling &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>4.4 Exam room scheduling &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/graph-coloring';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/04-exam-room-scheduling';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Cryptocurrency arbitrage search" href="cryptocurrency-arbitrage.html" />
-    <link rel="prev" title="Gasoline distribution" href="gasoline-distribution.html" />
+    <link rel="next" title="4.5 Cryptocurrency arbitrage search" href="05-cryptocurrency-arbitrage.html" />
+    <link rel="prev" title="4.3 Gasoline distribution" href="03-gasoline-distribution.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="04.00.html">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/graph-coloring.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/04-exam-room-scheduling.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/graph-coloring.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/04-exam-room-scheduling.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/04/graph-coloring.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/04/04-exam-room-scheduling.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Exam room scheduling</h1>
+    <h1>4.4 Exam room scheduling</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -512,7 +515,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><section class="tex2jax_ignore mathjax_ignore" id="exam-room-scheduling">
-<span id="index-4"></span><h1>Exam room scheduling<a class="headerlink" href="#exam-room-scheduling" title="Permalink to this heading">#</a></h1>
+<span id="index-4"></span><h1>4.4 Exam room scheduling<a class="headerlink" href="#exam-room-scheduling" title="Permalink to this heading">#</a></h1>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object <code class="docutils literal notranslate"><span class="pre">SOLVER</span></code> for later use.</p>
@@ -795,20 +798,20 @@ <h2>Mathematical formulation<a class="headerlink" href="#mathematical-formulatio
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="03-gasoline-distribution.html"
        title="previous page">
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       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Gasoline distribution</p>
+        <p class="prev-next-title">4.3 Gasoline distribution</p>
       </div>
     </a>
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-       href="cryptocurrency-arbitrage.html"
+       href="05-cryptocurrency-arbitrage.html"
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-        <p class="prev-next-title">Cryptocurrency arbitrage search</p>
+        <p class="prev-next-title">4.5 Cryptocurrency arbitrage search</p>
       </div>
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diff --git a/notebooks/04/04.00.html b/notebooks/04/04.00.html
index 371747c0..407c570f 100644
--- a/notebooks/04/04.00.html
+++ b/notebooks/04/04.00.html
@@ -72,7 +72,7 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/04.00';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Dinner seating arrangement" href="dinner-seat-allocation.html" />
+    <link rel="next" title="4.1 Dinner seating arrangement" href="01-dinner-seat-allocation.html" />
     <link rel="prev" title="Extra material: Maintenance planning" href="../03/11-maintenance-planning.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -471,13 +474,13 @@ <h1>4. Network Optimization<a class="headerlink" href="#network-optimization" ti
 <p>In the previous chapters, we dealt with general problems by first formulating all necessary constraints and then passing the problem to an LO or MILO solver, but in a way we have been oblivious to the problem’s structure. However, it is often advantageous to analyze this structure, as it can enable us to find better solution methods. In this chapter, we consider a very general class of problems with such a special structure – the <strong>network flow</strong> problems.</p>
 <p>This chapter includes several examples with companion Pyomo implementation that explore various modeling and implementation aspects of network optimization:</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="dinner-seat-allocation.html"><span class="doc std std-doc">A dinner seating arrangement problem</span></a></p></li>
-<li><p><a class="reference internal" href="mincost-flow.html"><span class="doc std std-doc">A transportation problem</span></a>, using the minimum-cost flow formulation.</p></li>
-<li><p><a class="reference internal" href="gasoline-distribution.html"><span class="doc std std-doc">A franchise gasoline distribution problem</span></a></p></li>
-<li><p><a class="reference internal" href="graph-coloring.html"><span class="doc std std-doc">A scheduling problem</span></a> formulated and solved as a graph coloring problem.</p></li>
-<li><p><a class="reference internal" href="cryptocurrency-arbitrage.html"><span class="doc std std-doc">A cryptocurrency arbitrage problem</span></a></p></li>
-<li><p><a class="reference internal" href="power-network.html"><span class="doc std std-doc">Extra material: Energy dispatch problem</span></a></p></li>
-<li><p><a class="reference internal" href="forex-arbitrage.html"><span class="doc std std-doc">Extra material: Forex arbitrage</span></a></p></li>
+<li><p><a class="reference internal" href="01-dinner-seat-allocation.html"><span class="doc std std-doc">A dinner seating arrangement problem</span></a></p></li>
+<li><p><a class="reference internal" href="02-mincost-flow.html"><span class="doc std std-doc">A transportation problem</span></a>, using the minimum-cost flow formulation.</p></li>
+<li><p><a class="reference internal" href="03-gasoline-distribution.html"><span class="doc std std-doc">A franchise gasoline distribution problem</span></a></p></li>
+<li><p><a class="reference internal" href="04-exam-room-scheduling.html"><span class="doc std std-doc">A scheduling problem</span></a> formulated and solved as a graph coloring problem.</p></li>
+<li><p><a class="reference internal" href="05-cryptocurrency-arbitrage.html"><span class="doc std std-doc">A cryptocurrency arbitrage problem</span></a></p></li>
+<li><p><a class="reference internal" href="06-power-network.html"><span class="doc std std-doc">Extra material: Energy dispatch problem</span></a></p></li>
+<li><p><a class="reference internal" href="07-forex-arbitrage.html"><span class="doc std std-doc">Extra material: Forex arbitrage</span></a></p></li>
 </ul>
 <p>Go to the <a class="reference internal" href="../05/05.00.html"><span class="doc std std-doc">next chapter</span></a> about convex optimization.</p>
 <div class="toctree-wrapper compound">
@@ -524,11 +527,11 @@ <h1>4. Network Optimization<a class="headerlink" href="#network-optimization" ti
       </div>
     </a>
     <a class="right-next"
-       href="dinner-seat-allocation.html"
+       href="01-dinner-seat-allocation.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Dinner seating arrangement</p>
+        <p class="prev-next-title">4.1 Dinner seating arrangement</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/notebooks/04/cryptocurrency-arbitrage.html b/notebooks/04/05-cryptocurrency-arbitrage.html
similarity index 97%
rename from notebooks/04/cryptocurrency-arbitrage.html
rename to notebooks/04/05-cryptocurrency-arbitrage.html
index 5713779f..c0347b1f 100644
--- a/notebooks/04/cryptocurrency-arbitrage.html
+++ b/notebooks/04/05-cryptocurrency-arbitrage.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Cryptocurrency arbitrage search &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>4.5 Cryptocurrency arbitrage search &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/cryptocurrency-arbitrage';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/05-cryptocurrency-arbitrage';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra material: Energy dispatch problem" href="power-network.html" />
-    <link rel="prev" title="Exam room scheduling" href="graph-coloring.html" />
+    <link rel="next" title="Extra material: Energy dispatch problem" href="06-power-network.html" />
+    <link rel="prev" title="4.4 Exam room scheduling" href="04-exam-room-scheduling.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="04.00.html">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/cryptocurrency-arbitrage.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/05-cryptocurrency-arbitrage.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/cryptocurrency-arbitrage.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/05-cryptocurrency-arbitrage.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/04/cryptocurrency-arbitrage.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/04/05-cryptocurrency-arbitrage.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Cryptocurrency arbitrage search</h1>
+    <h1>4.5 Cryptocurrency arbitrage search</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -526,7 +529,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><section class="tex2jax_ignore mathjax_ignore" id="cryptocurrency-arbitrage-search">
-<span id="index-5"></span><h1>Cryptocurrency arbitrage search<a class="headerlink" href="#cryptocurrency-arbitrage-search" title="Permalink to this heading">#</a></h1>
+<span id="index-5"></span><h1>4.5 Cryptocurrency arbitrage search<a class="headerlink" href="#cryptocurrency-arbitrage-search" title="Permalink to this heading">#</a></h1>
 <section id="installations-and-imports">
 <h2>Installations and Imports<a class="headerlink" href="#installations-and-imports" title="Permalink to this heading">#</a></h2>
 </section>
@@ -2358,16 +2361,16 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="graph-coloring.html"
+       href="04-exam-room-scheduling.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Exam room scheduling</p>
+        <p class="prev-next-title">4.4 Exam room scheduling</p>
       </div>
     </a>
     <a class="right-next"
-       href="power-network.html"
+       href="06-power-network.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/04/power-network.html b/notebooks/04/06-power-network.html
similarity index 97%
rename from notebooks/04/power-network.html
rename to notebooks/04/06-power-network.html
index 95557acc..44e93941 100644
--- a/notebooks/04/power-network.html
+++ b/notebooks/04/06-power-network.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/power-network';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/06-power-network';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra material: Forex Arbitrage" href="forex-arbitrage.html" />
-    <link rel="prev" title="Cryptocurrency arbitrage search" href="cryptocurrency-arbitrage.html" />
+    <link rel="next" title="Extra material: Forex Arbitrage" href="07-forex-arbitrage.html" />
+    <link rel="prev" title="4.5 Cryptocurrency arbitrage search" href="05-cryptocurrency-arbitrage.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="04.00.html">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/power-network.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/06-power-network.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/power-network.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/06-power-network.html&body=Your%20issue%20content%20here." target="_blank"
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    title="Open an issue"
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@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/04/power-network.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/04/06-power-network.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1760,16 +1763,16 @@ <h1>Comparing the three models<a class="headerlink" href="#comparing-the-three-m
                   <!-- Previous / next buttons -->
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+        <p class="prev-next-title">4.5 Cryptocurrency arbitrage search</p>
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diff --git a/notebooks/04/forex-arbitrage.html b/notebooks/04/07-forex-arbitrage.html
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,13 +212,13 @@
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="04.00.html">4. Network Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
@@ -222,48 +226,47 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/forex-arbitrage.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/04/07-forex-arbitrage.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
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    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
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+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/04/07-forex-arbitrage.html&body=Your%20issue%20content%20here." target="_blank"
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@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/04/forex-arbitrage.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/04/07-forex-arbitrage.ipynb" target="_blank"
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    title="Download source file"
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@@ -1168,7 +1171,7 @@ <h2>FOREX data<a class="headerlink" href="#forex-data" title="Permalink to this
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diff --git a/notebooks/05/milk-pooling.html b/notebooks/05/01-milk-pooling.html
similarity index 97%
rename from notebooks/05/milk-pooling.html
rename to notebooks/05/01-milk-pooling.html
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--- a/notebooks/05/milk-pooling.html
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@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Milk pooling and blending &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>5.1 Milk pooling and blending &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,10 +71,10 @@
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/milk-pooling.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/01-milk-pooling.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/milk-pooling.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/01-milk-pooling.html&body=Your%20issue%20content%20here." target="_blank"
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@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/05/milk-pooling.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/05/01-milk-pooling.ipynb" target="_blank"
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@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Milk pooling and blending</h1>
+    <h1>5.1 Milk pooling and blending</h1>
     <!-- Table of contents -->
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@@ -522,7 +525,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><section class="tex2jax_ignore mathjax_ignore" id="milk-pooling-and-blending">
-<span id="index-6"></span><h1>Milk pooling and blending<a class="headerlink" href="#milk-pooling-and-blending" title="Permalink to this heading">#</a></h1>
+<span id="index-6"></span><h1>5.1 Milk pooling and blending<a class="headerlink" href="#milk-pooling-and-blending" title="Permalink to this heading">#</a></h1>
 <p>Pooling and blending operations involve the “pooling” of various streams to create intermediate mixtures that are subsequently blended with other streams to meet final product specifications. These operations are common to the chemical processing and petroleum sectors where limited tankage may be available, or when it is necessary to transport materials by train, truck, or pipeline to remote blending terminals. Similar applications arise in agriculture, food, mining, wastewater treatment, and other industries.</p>
 <p>This notebook considers a simple example of a wholesale milk distributor to show how <strong>non-convexity</strong> arises in the optimization of pooling and blending operations. Non-convexity is due to presence of <strong>bilinear</strong> terms that are the product of two decision variables where one is a scale-dependent <strong>extensive</strong> quantity measuring the amount or flow of a product, and the other is scale-independent <strong>intensive</strong> quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of <code class="docutils literal notranslate"><span class="pre">ipopt</span></code>, a solver specifically designed to find global solutions to nonlinear optimization (NLO) problems.</p>
 <section id="preamble-install-pyomo-and-a-solver">
@@ -2278,11 +2281,11 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
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diff --git a/notebooks/05/ols-regression.html b/notebooks/05/02-ols-regression.html
similarity index 92%
rename from notebooks/05/ols-regression.html
rename to notebooks/05/02-ols-regression.html
index 29abbaf6..065815a0 100644
--- a/notebooks/05/ols-regression.html
+++ b/notebooks/05/02-ols-regression.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Ordinary Least Squares (OLS) Regression &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>5.2 Ordinary Least Squares (OLS) Regression &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/ols-regression';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/02-ols-regression';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Markowitz portfolio optimization" href="markowitz_portfolio.html" />
-    <link rel="prev" title="Milk pooling and blending" href="milk-pooling.html" />
+    <link rel="next" title="5.3 Markowitz portfolio optimization" href="03-markowitz-portfolio.html" />
+    <link rel="prev" title="5.1 Milk pooling and blending" href="01-milk-pooling.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/ols-regression.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/02-ols-regression.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/ols-regression.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/02-ols-regression.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/05/ols-regression.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/05/02-ols-regression.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Ordinary Least Squares (OLS) Regression</h1>
+    <h1>5.2 Ordinary Least Squares (OLS) Regression</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -515,8 +518,8 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><section class="tex2jax_ignore mathjax_ignore" id="ordinary-least-squares-ols-regression">
-<span id="index-1"></span><h1>Ordinary Least Squares (OLS) Regression<a class="headerlink" href="#ordinary-least-squares-ols-regression" title="Permalink to this heading">#</a></h1>
-<p>In Chapter 2 we introduced linear regression with least absolute deviations (LAD), see <span class="xref myst">this notebook</span>. Here we consider the same problem setting, but slightly change the underlying optimization problem, in particular its objective function, obtaining the classical ordinary least squares (OLS) regression.</p>
+<span id="index-1"></span><h1>5.2 Ordinary Least Squares (OLS) Regression<a class="headerlink" href="#ordinary-least-squares-ols-regression" title="Permalink to this heading">#</a></h1>
+<p>In Chapter 2 we introduced linear regression with least absolute deviations (LAD), see <a class="reference internal" href="../02/02-lad-regression.html"><span class="doc std std-doc">this notebook</span></a>. Here we consider the same problem setting, but slightly change the underlying optimization problem, in particular its objective function, obtaining the classical ordinary least squares (OLS) regression.</p>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>This cell selects and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and ipopt, then sets SOLVER to use the ipopt solver. If run elsewhere, it assumes Pyomo and the Mosek solver have been previously installed and sets SOLVER to use the Mosek solver via the Pyomo SolverFactory. It then verifies that SOLVER is available. For linear problems, the solver HiGHS is imported and used.</p>
@@ -808,20 +811,20 @@ <h2>Visualizing the results and comparison with LAD regression<a class="headerli
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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-       href="milk-pooling.html"
+       href="01-milk-pooling.html"
        title="previous page">
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       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Milk pooling and blending</p>
+        <p class="prev-next-title">5.1 Milk pooling and blending</p>
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+       href="03-markowitz-portfolio.html"
        title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Markowitz portfolio optimization</p>
+        <p class="prev-next-title">5.3 Markowitz portfolio optimization</p>
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diff --git a/notebooks/05/markowitz_portfolio.html b/notebooks/05/03-markowitz-portfolio.html
similarity index 90%
rename from notebooks/05/markowitz_portfolio.html
rename to notebooks/05/03-markowitz-portfolio.html
index 8ba00dad..a1bd004e 100644
--- a/notebooks/05/markowitz_portfolio.html
+++ b/notebooks/05/03-markowitz-portfolio.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Markowitz portfolio optimization &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>5.3 Markowitz portfolio optimization &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/markowitz_portfolio';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/03-markowitz-portfolio';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Support Vector Machines for Binary Classification" href="svm.html" />
-    <link rel="prev" title="Ordinary Least Squares (OLS) Regression" href="ols-regression.html" />
+    <link rel="next" title="5.4 Support Vector Machines for Binary Classification" href="04-svm-binary-classification.html" />
+    <link rel="prev" title="5.2 Ordinary Least Squares (OLS) Regression" href="02-ols-regression.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/markowitz_portfolio.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/03-markowitz-portfolio.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/markowitz_portfolio.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/03-markowitz-portfolio.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/05/markowitz_portfolio.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/05/03-markowitz-portfolio.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Markowitz portfolio optimization</h1>
+    <h1>5.3 Markowitz portfolio optimization</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -511,7 +514,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><section class="tex2jax_ignore mathjax_ignore" id="markowitz-portfolio-optimization">
-<span id="index-2"></span><h1>Markowitz portfolio optimization<a class="headerlink" href="#markowitz-portfolio-optimization" title="Permalink to this heading">#</a></h1>
+<span id="index-2"></span><h1>5.3 Markowitz portfolio optimization<a class="headerlink" href="#markowitz-portfolio-optimization" title="Permalink to this heading">#</a></h1>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>This cell selects and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and ipopt, then sets SOLVER to
@@ -701,20 +704,20 @@ <h2>Problem description and model formulation<a class="headerlink" href="#proble
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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-       href="ols-regression.html"
+       href="02-ols-regression.html"
        title="previous page">
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       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Ordinary Least Squares (OLS) Regression</p>
+        <p class="prev-next-title">5.2 Ordinary Least Squares (OLS) Regression</p>
       </div>
     </a>
     <a class="right-next"
-       href="svm.html"
+       href="04-svm-binary-classification.html"
        title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Support Vector Machines for Binary Classification</p>
+        <p class="prev-next-title">5.4 Support Vector Machines for Binary Classification</p>
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     </a>
diff --git a/notebooks/05/svm.html b/notebooks/05/04-svm-binary-classification.html
similarity index 97%
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rename to notebooks/05/04-svm-binary-classification.html
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--- a/notebooks/05/svm.html
+++ b/notebooks/05/04-svm-binary-classification.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Support Vector Machines for Binary Classification &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>5.4 Support Vector Machines for Binary Classification &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/svm';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/04-svm-binary-classification';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra material: Refinery production and shadow pricing with CVXPY" href="refinery-production.html" />
-    <link rel="prev" title="Markowitz portfolio optimization" href="markowitz_portfolio.html" />
+    <link rel="next" title="Extra material: Refinery production and shadow pricing with CVXPY" href="05-refinery-production.html" />
+    <link rel="prev" title="5.3 Markowitz portfolio optimization" href="03-markowitz-portfolio.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/svm.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/04-svm-binary-classification.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/svm.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/04-svm-binary-classification.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/05/svm.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/05/04-svm-binary-classification.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Support Vector Machines for Binary Classification</h1>
+    <h1>5.4 Support Vector Machines for Binary Classification</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -538,7 +541,7 @@ <h2> Contents </h2>
                 <article class="bd-article" role="main">
                   
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-for-binary-classification">
-<span id="index-4"></span><h1>Support Vector Machines for Binary Classification<a class="headerlink" href="#support-vector-machines-for-binary-classification" title="Permalink to this heading">#</a></h1>
+<span id="index-4"></span><h1>5.4 Support Vector Machines for Binary Classification<a class="headerlink" href="#support-vector-machines-for-binary-classification" title="Permalink to this heading">#</a></h1>
 <p>Support Vector Machines (SVM) are a type of supervised machine learning model. Similar to other machine learning techniques based on regression, training an SVM classifier uses examples with known outcomes, and involves optimization some measure of performance. The resulting classifier can then be applied to classify data with unknown outcomes.</p>
 <p>In this notebook, we will demonstrate the process of training an SVM for binary classification using linear and quadratic programming. Our implementation will initially focus on linear support vector machines which separate the feature space by means of a hyperplane. We will explore both primal and dual formulations. Then, using kernels, the dual formulation is extended to binary classification in higher-order and nonlinear feature spaces. Several different formulations of the optimization problem are given in Pyomo and applied to a banknote classification application.</p>
 <section id="preamble-install-pyomo-and-a-solver">
@@ -1998,16 +2001,16 @@ <h3>Polynomial kernels<a class="headerlink" href="#polynomial-kernels" title="Pe
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="03-markowitz-portfolio.html"
        title="previous page">
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         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Markowitz portfolio optimization</p>
+        <p class="prev-next-title">5.3 Markowitz portfolio optimization</p>
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+       href="05-refinery-production.html"
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diff --git a/notebooks/05/refinery-production.html b/notebooks/05/05-refinery-production.html
similarity index 94%
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rename to notebooks/05/05-refinery-production.html
index 9b16e98d..6819bb26 100644
--- a/notebooks/05/refinery-production.html
+++ b/notebooks/05/05-refinery-production.html
@@ -69,11 +69,11 @@
 const thebe_selector_output = ".output, .cell_output"
 </script>
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/refinery-production';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/05-refinery-production';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra Material: Cutting Stock" href="cutting-stock.html" />
-    <link rel="prev" title="Support Vector Machines for Binary Classification" href="svm.html" />
+    <link rel="next" title="Extra Material: Cutting Stock" href="06-cutting-stock.html" />
+    <link rel="prev" title="5.4 Support Vector Machines for Binary Classification" href="04-svm-binary-classification.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm.html">Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -325,7 +328,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/refinery-production.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/05-refinery-production.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -373,7 +376,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/refinery-production.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/05-refinery-production.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -403,7 +406,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/05/refinery-production.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/05/05-refinery-production.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1034,16 +1037,16 @@ <h2>Suggested Exercises<a class="headerlink" href="#suggested-exercises" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="svm.html"
+       href="04-svm-binary-classification.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Support Vector Machines for Binary Classification</p>
+        <p class="prev-next-title">5.4 Support Vector Machines for Binary Classification</p>
       </div>
     </a>
     <a class="right-next"
-       href="cutting-stock.html"
+       href="06-cutting-stock.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/05/05.00.html b/notebooks/05/05.00.html
index 97a21519..d67f6295 100644
--- a/notebooks/05/05.00.html
+++ b/notebooks/05/05.00.html
@@ -72,8 +72,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/05.00';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Milk pooling and blending" href="milk-pooling.html" />
-    <link rel="prev" title="Extra material: Forex Arbitrage" href="../04/forex-arbitrage.html" />
+    <link rel="next" title="5.1 Milk pooling and blending" href="01-milk-pooling.html" />
+    <link rel="prev" title="Extra material: Forex Arbitrage" href="../04/07-forex-arbitrage.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -472,12 +475,12 @@ <h1>5. Convex Optimization<a class="headerlink" href="#convex-optimization" titl
 <p>Luckily, it will turn out that the real separation between simple and difficult problems is not whether they are linear vs. nonlinear, but instead, whether they are <strong>convex</strong> or not. Convexity is a very desirable property of an optimization problem that makes the optimization problem solvable to optimality in a reasonable time. For that reason, it plays a central role in mathematical optimization.</p>
 <p>This chapter includes several examples with companion Pyomo implementation that explore various modeling and implementation aspects of convex optimization:</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="milk-pooling.html"><span class="doc std std-doc">Milk pooling and blending</span></a></p></li>
-<li><p><a class="reference internal" href="ols-regression.html"><span class="doc std std-doc">Ordinary Least Squares (OLS) regression</span></a></p></li>
-<li><p><a class="reference internal" href="markowitz_portfolio.html"><span class="doc std std-doc">Markowitz portfolio optimization problem</span></a></p></li>
-<li><p><a class="reference internal" href="svm.html"><span class="doc std std-doc">Support Vector Machines for binary classification</span></a></p></li>
-<li><p><a class="reference internal" href="refinery-production.html"><span class="doc std std-doc">Extra material: Refinery production</span></a></p></li>
-<li><p><a class="reference internal" href="cutting-stock.html"><span class="doc std std-doc">Extra material: Cutting stock</span></a></p></li>
+<li><p><a class="reference internal" href="01-milk-pooling.html"><span class="doc std std-doc">Milk pooling and blending</span></a></p></li>
+<li><p><a class="reference internal" href="02-ols-regression.html"><span class="doc std std-doc">Ordinary Least Squares (OLS) regression</span></a></p></li>
+<li><p><a class="reference internal" href="03-markowitz-portfolio.html"><span class="doc std std-doc">Markowitz portfolio optimization problem</span></a></p></li>
+<li><p><a class="reference internal" href="04-svm-binary-classification.html"><span class="doc std std-doc">Support Vector Machines for binary classification</span></a></p></li>
+<li><p><a class="reference internal" href="05-refinery-production.html"><span class="doc std std-doc">Extra material: Refinery production</span></a></p></li>
+<li><p><a class="reference internal" href="06-cutting-stock.html"><span class="doc std std-doc">Extra material: Cutting stock</span></a></p></li>
 </ul>
 <p>Go to the <a class="reference internal" href="../06/06.00.html"><span class="doc std std-doc">next chapter</span></a> about conic optimization.</p>
 <div class="toctree-wrapper compound">
@@ -515,7 +518,7 @@ <h1>5. Convex Optimization<a class="headerlink" href="#convex-optimization" titl
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="../04/forex-arbitrage.html"
+       href="../04/07-forex-arbitrage.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
@@ -524,11 +527,11 @@ <h1>5. Convex Optimization<a class="headerlink" href="#convex-optimization" titl
       </div>
     </a>
     <a class="right-next"
-       href="milk-pooling.html"
+       href="01-milk-pooling.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Milk pooling and blending</p>
+        <p class="prev-next-title">5.1 Milk pooling and blending</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/notebooks/05/cutting-stock.html b/notebooks/05/06-cutting-stock.html
similarity index 98%
rename from notebooks/05/cutting-stock.html
rename to notebooks/05/06-cutting-stock.html
index f354c0bc..7f0fb44f 100644
--- a/notebooks/05/cutting-stock.html
+++ b/notebooks/05/06-cutting-stock.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/cutting-stock';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/05/06-cutting-stock';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="6. Conic Optimization" href="../06/06.00.html" />
-    <link rel="prev" title="Extra material: Refinery production and shadow pricing with CVXPY" href="refinery-production.html" />
+    <link rel="prev" title="Extra material: Refinery production and shadow pricing with CVXPY" href="05-refinery-production.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/cutting-stock.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/05/06-cutting-stock.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/cutting-stock.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/05/06-cutting-stock.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/05/cutting-stock.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/05/06-cutting-stock.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1787,7 +1790,7 @@ <h2>References<a class="headerlink" href="#references" title="Permalink to this
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="refinery-production.html"
+       href="05-refinery-production.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
diff --git a/notebooks/06/economic-order-quantity.html b/notebooks/06/01-economic-order-quantity.html
similarity index 97%
rename from notebooks/06/economic-order-quantity.html
rename to notebooks/06/01-economic-order-quantity.html
index 1a72fab1..8b4263dc 100644
--- a/notebooks/06/economic-order-quantity.html
+++ b/notebooks/06/01-economic-order-quantity.html
@@ -71,10 +71,10 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/economic-order-quantity';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/01-economic-order-quantity';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="The Kelly Criterion" href="kelly-criterion.html" />
+    <link rel="next" title="The Kelly Criterion" href="02-kelly-criterion.html" />
     <link rel="prev" title="6. Conic Optimization" href="06.00.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="06.00.html">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/economic-order-quantity.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/01-economic-order-quantity.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/economic-order-quantity.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/01-economic-order-quantity.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/06/economic-order-quantity.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/06/01-economic-order-quantity.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1792,7 +1795,7 @@ <h2>Appendix: Formulation with SOCO constraints<a class="headerlink" href="#appe
       </div>
     </a>
     <a class="right-next"
-       href="kelly-criterion.html"
+       href="02-kelly-criterion.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/06/kelly-criterion.html b/notebooks/06/02-kelly-criterion.html
similarity index 94%
rename from notebooks/06/kelly-criterion.html
rename to notebooks/06/02-kelly-criterion.html
index 3c0b9ac3..caecf28f 100644
--- a/notebooks/06/kelly-criterion.html
+++ b/notebooks/06/02-kelly-criterion.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/kelly-criterion';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/02-kelly-criterion';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Markowitz portfolio optimization revisited" href="markowitz_portfolio_revisited.html" />
-    <link rel="prev" title="Economic Order Quantity" href="economic-order-quantity.html" />
+    <link rel="next" title="Markowitz portfolio optimization revisited" href="03-markowitz-portfolio-revisited.html" />
+    <link rel="prev" title="Economic Order Quantity" href="01-economic-order-quantity.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="06.00.html">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-economic-order-quantity.html">Economic Order Quantity</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/kelly-criterion.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/02-kelly-criterion.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/kelly-criterion.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/02-kelly-criterion.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/06/kelly-criterion.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/06/02-kelly-criterion.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -872,7 +875,7 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="01-economic-order-quantity.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
@@ -881,7 +884,7 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
       </div>
     </a>
     <a class="right-next"
-       href="markowitz_portfolio_revisited.html"
+       href="03-markowitz-portfolio-revisited.html"
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diff --git a/notebooks/06/markowitz_portfolio_revisited.html b/notebooks/06/03-markowitz-portfolio-revisited.html
similarity index 89%
rename from notebooks/06/markowitz_portfolio_revisited.html
rename to notebooks/06/03-markowitz-portfolio-revisited.html
index 0eec3e7d..174e37b6 100644
--- a/notebooks/06/markowitz_portfolio_revisited.html
+++ b/notebooks/06/03-markowitz-portfolio-revisited.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/markowitz_portfolio_revisited';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/03-markowitz-portfolio-revisited';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Optimal Design of Multilayered Building Insulation" href="building-insulation.html" />
-    <link rel="prev" title="The Kelly Criterion" href="kelly-criterion.html" />
+    <link rel="next" title="Optimal Design of Multilayered Building Insulation" href="04-building-insulation.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="06.00.html">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-kelly-criterion.html">The Kelly Criterion</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/markowitz_portfolio_revisited.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/03-markowitz-portfolio-revisited.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/markowitz_portfolio_revisited.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/03-markowitz-portfolio-revisited.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/06/markowitz_portfolio_revisited.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/06/03-markowitz-portfolio-revisited.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -550,7 +553,7 @@ <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-in
 </section>
 <section id="problem-description-and-model-formulation">
 <h2>Problem description and model formulation<a class="headerlink" href="#problem-description-and-model-formulation" title="Permalink to this heading">#</a></h2>
-<p>Consider again the <a class="reference internal" href="../05/markowitz_portfolio.html"><span class="doc std std-doc">Markowitz portfolio optimization</span></a> we presented earlier in Chapter 5. Recall that the matrix <span class="math notranslate nohighlight">\(\Sigma\)</span> describes the covariance among the uncertain return rates <span class="math notranslate nohighlight">\(r_i\)</span>, <span class="math notranslate nohighlight">\(i=1,\dots, n\)</span>. Since <span class="math notranslate nohighlight">\(\Sigma\)</span> is positive semidefinite by definition, it allows for a Cholesky factorization, namely <span class="math notranslate nohighlight">\(\Sigma = B B^\top\)</span>. We can then rewrite the quadratic constraint as <span class="math notranslate nohighlight">\(\|B^\top x \|_2 \leq \gamma\)</span> and thus as <span class="math notranslate nohighlight">\((\gamma, B^\top x) \in \mathcal{L}^{n+1}\)</span> using the Lorentz cone. In this way, we realize that the original portfolio problem we formulated <a class="reference internal" href="../05/markowitz_portfolio.html"><span class="doc std std-doc">earlier</span></a> is in fact a conic quadratic optimization problem, which can thus be solved faster and more reliably. The optimal solution of that problem was the one with the maximum expected return while allowing for a specific level <span class="math notranslate nohighlight">\(\gamma\)</span> of risk.</p>
+<p>Consider again the <span class="xref myst">Markowitz portfolio optimization</span> we presented earlier in Chapter 5. Recall that the matrix <span class="math notranslate nohighlight">\(\Sigma\)</span> describes the covariance among the uncertain return rates <span class="math notranslate nohighlight">\(r_i\)</span>, <span class="math notranslate nohighlight">\(i=1,\dots, n\)</span>. Since <span class="math notranslate nohighlight">\(\Sigma\)</span> is positive semidefinite by definition, it allows for a Cholesky factorization, namely <span class="math notranslate nohighlight">\(\Sigma = B B^\top\)</span>. We can then rewrite the quadratic constraint as <span class="math notranslate nohighlight">\(\|B^\top x \|_2 \leq \gamma\)</span> and thus as <span class="math notranslate nohighlight">\((\gamma, B^\top x) \in \mathcal{L}^{n+1}\)</span> using the Lorentz cone. In this way, we realize that the original portfolio problem we formulated <span class="xref myst">earlier</span> is in fact a conic quadratic optimization problem, which can thus be solved faster and more reliably. The optimal solution of that problem was the one with the maximum expected return while allowing for a specific level <span class="math notranslate nohighlight">\(\gamma\)</span> of risk.</p>
 <p>However, an investor could aim for a different trade-off between return and risk and formulate a slightly different optimization problem, namely</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}
@@ -733,7 +736,7 @@ <h2>Problem description and model formulation<a class="headerlink" href="#proble
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@@ -742,7 +745,7 @@ <h2>Problem description and model formulation<a class="headerlink" href="#proble
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diff --git a/notebooks/06/building-insulation.html b/notebooks/06/04-building-insulation.html
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="06.00.html">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/building-insulation.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/04-building-insulation.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
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@@ -375,7 +378,7 @@
       
       
       
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+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/04-building-insulation.html&body=Your%20issue%20content%20here." target="_blank"
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@@ -405,7 +408,7 @@
       
       
       
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diff --git a/notebooks/06/svm-conic.html b/notebooks/06/05-svm-conic.html
similarity index 96%
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+    <title>Support Vector Machines with Conic Optimization &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="06.00.html">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/svm-conic.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/05-svm-conic.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/svm-conic.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/05-svm-conic.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
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@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/06/svm-conic.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/06/05-svm-conic.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Training Support Vector Machines with Conic Optimization</h1>
+    <h1>Support Vector Machines with Conic Optimization</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -530,9 +533,9 @@ <h2> Contents </h2>
 <div id="searchbox"></div>
                 <article class="bd-article" role="main">
                   
-  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><section class="tex2jax_ignore mathjax_ignore" id="training-support-vector-machines-with-conic-optimization">
-<span id="index-5"></span><h1>Training Support Vector Machines with Conic Optimization<a class="headerlink" href="#training-support-vector-machines-with-conic-optimization" title="Permalink to this heading">#</a></h1>
-<p>In this notebook we come back to the concept of training support vector machines as we did in <a class="reference internal" href="../05/svm.html"><span class="doc std std-doc">the first support vector machine notebook</span></a></p>
+  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-with-conic-optimization">
+<span id="index-5"></span><h1>Support Vector Machines with Conic Optimization<a class="headerlink" href="#support-vector-machines-with-conic-optimization" title="Permalink to this heading">#</a></h1>
+<p>In this notebook we come back to the concept of training support vector machines as we did in <span class="xref myst">the first support vector machine notebook</span></p>
 <p>The difference is that we shall now be solving the dual problems related to training the SVM’s using the conic quadratic optimization by explicitly calling the Mosek solver, which should yield more stable numerical results than general convex optimization.</p>
 <p>The first part of this notebook shall therefore consist of data imports and other things that need no further explanation. Please move directly to the cell entitled “Conic optimization model” if you already have the data loaded from there.</p>
 <p>Point of attention: An important difference with the first notebook will be the fact that we will eliminate the ‘intercept’ <span class="math notranslate nohighlight">\(b\)</span> of the SVM to keep our equations simple.</p>
@@ -748,7 +751,7 @@ <h3>Performance metrics<a class="headerlink" href="#performance-metrics" title="
 <h2>Conic optimization model<a class="headerlink" href="#conic-optimization-model" title="Permalink to this heading">#</a></h2>
 <section id="primal-formulation">
 <h3>Primal formulation<a class="headerlink" href="#primal-formulation" title="Permalink to this heading">#</a></h3>
-<p>As already explained in in <a class="reference internal" href="../05/svm.html"><span class="doc std std-doc">the first support vector machine notebook</span></a>, the standard formulation of a linear support vector machine uses training sets with <span class="math notranslate nohighlight">\(p\)</span>-element feature vectors <span class="math notranslate nohighlight">\(x_i\in\mathbb{R}^p\)</span> along with classification labels for those vectors, <span class="math notranslate nohighlight">\(y_i = \pm 1\)</span>. A classifier is defined by two parameters: a weight vector <span class="math notranslate nohighlight">\(w\in\mathbb{R}^p\)</span> and a bias term <span class="math notranslate nohighlight">\(b\in\mathbb{R}\)</span></p>
+<p>As already explained in in <span class="xref myst">the first support vector machine notebook</span>, the standard formulation of a linear support vector machine uses training sets with <span class="math notranslate nohighlight">\(p\)</span>-element feature vectors <span class="math notranslate nohighlight">\(x_i\in\mathbb{R}^p\)</span> along with classification labels for those vectors, <span class="math notranslate nohighlight">\(y_i = \pm 1\)</span>. A classifier is defined by two parameters: a weight vector <span class="math notranslate nohighlight">\(w\in\mathbb{R}^p\)</span> and a bias term <span class="math notranslate nohighlight">\(b\in\mathbb{R}\)</span></p>
 <div class="math notranslate nohighlight">
 \[
 \begin{align*}
@@ -1223,7 +1226,7 @@ <h3>Nonlinear feature spaces<a class="headerlink" href="#nonlinear-feature-space
 \[
 y^{pred} = \text{sgn} \left( w^\top \phi(\bar{x}) \right).
 \]</div>
-<p>From now onwards, the derivation of the dual problem goes exactly the same way as in the linear SVM case (please refer to <a class="reference internal" href="../05/svm.html"><span class="doc std std-doc">the first support vector machine notebook</span></a> ), and we arrive at:</p>
+<p>From now onwards, the derivation of the dual problem goes exactly the same way as in the linear SVM case (please refer to <span class="xref myst">the first support vector machine notebook</span> ), and we arrive at:</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}
 \begin{align*}
@@ -1596,7 +1599,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
                   <!-- Previous / next buttons -->
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@@ -1605,7 +1608,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
       </div>
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     <a class="right-next"
-       href="investment-wheel.html"
+       href="06-investment-wheel.html"
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diff --git a/notebooks/06/investment-wheel.html b/notebooks/06/06-investment-wheel.html
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index 2ac7a947..599fa45c 100644
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+++ b/notebooks/06/06-investment-wheel.html
@@ -71,11 +71,11 @@
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+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/06-investment-wheel';</script>
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-    <link rel="prev" title="Training Support Vector Machines with Conic Optimization" href="svm-conic.html" />
+    <link rel="next" title="Extra material: Optimal Growth Portfolios with Risk Aversion" href="07-optimal-growth-portfolios.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="06.00.html">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/investment-wheel.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/06-investment-wheel.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/investment-wheel.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/06-investment-wheel.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/06/investment-wheel.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/06/06-investment-wheel.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1018,16 +1021,16 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="05-svm-conic.html"
        title="previous page">
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       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Training Support Vector Machines with Conic Optimization</p>
+        <p class="prev-next-title">Support Vector Machines with Conic Optimization</p>
       </div>
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+       href="07-optimal-growth-portfolios.html"
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diff --git a/notebooks/06/06.00.html b/notebooks/06/06.00.html
index 7e06ba80..f04dfdc9 100644
--- a/notebooks/06/06.00.html
+++ b/notebooks/06/06.00.html
@@ -72,8 +72,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/06.00';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Economic Order Quantity" href="economic-order-quantity.html" />
-    <link rel="prev" title="Extra Material: Cutting Stock" href="../05/cutting-stock.html" />
+    <link rel="next" title="Economic Order Quantity" href="01-economic-order-quantity.html" />
+    <link rel="prev" title="Extra Material: Cutting Stock" href="../05/06-cutting-stock.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -470,13 +473,13 @@ <h1>6. Conic Optimization</h1>
 <h1>6. Conic Optimization<a class="headerlink" href="#conic-optimization" title="Permalink to this heading">#</a></h1>
 <p>In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of conic problems:</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="economic-order-quantity.html"><span class="doc std std-doc">Economic order quantity</span></a></p></li>
-<li><p><a class="reference internal" href="kelly-criterion.html"><span class="doc std std-doc">The Kelly Criterion</span></a></p></li>
-<li><p><a class="reference internal" href="markowitz_portfolio_revisited.html"><span class="doc std std-doc">Markowitz portfolio optimization problem revisited</span></a></p></li>
-<li><p><a class="reference internal" href="building-insulation.html"><span class="doc std std-doc">Optimal design of multi-layered building insulation</span></a></p></li>
-<li><p><a class="reference internal" href="svm-conic.html"><span class="doc std std-doc">Training Support Vector Machines with Conic Programming</span></a></p></li>
-<li><p><a class="reference internal" href="investment-wheel.html"><span class="doc std std-doc">Extra material: Luenberger’s Investment Wheel</span></a></p></li>
-<li><p><a class="reference internal" href="optimal-growth-portfolios.html"><span class="doc std std-doc">Extra material: Optimal Growth Portfolio</span></a></p></li>
+<li><p><a class="reference internal" href="01-economic-order-quantity.html"><span class="doc std std-doc">Economic order quantity</span></a></p></li>
+<li><p><a class="reference internal" href="02-kelly-criterion.html"><span class="doc std std-doc">The Kelly criterion</span></a></p></li>
+<li><p><a class="reference internal" href="03-markowitz-portfolio-revisited.html"><span class="doc std std-doc">Markowitz portfolio optimization problem revisited</span></a></p></li>
+<li><p><a class="reference internal" href="04-building-insulation.html"><span class="doc std std-doc">Optimal design of multi-layered building insulation</span></a></p></li>
+<li><p><a class="reference internal" href="05-svm-conic.html"><span class="doc std std-doc">Support Vector Machines with conic optimization</span></a></p></li>
+<li><p><a class="reference internal" href="06-investment-wheel.html"><span class="doc std std-doc">Extra material: Luenberger’s investment wheel</span></a></p></li>
+<li><p><a class="reference internal" href="07-optimal-growth-portfolios.html"><span class="doc std std-doc">Extra material: Optimal growth portfolio</span></a></p></li>
 </ul>
 <p>Go to the <a class="reference internal" href="../07/07.00.html"><span class="doc std std-doc">next chapter</span></a> about what happens when optimization meets reality and needs to account for uncertainty.</p>
 <div class="toctree-wrapper compound">
@@ -514,7 +517,7 @@ <h1>6. Conic Optimization<a class="headerlink" href="#conic-optimization" title=
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="../05/06-cutting-stock.html"
        title="previous page">
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       <div class="prev-next-info">
@@ -523,7 +526,7 @@ <h1>6. Conic Optimization<a class="headerlink" href="#conic-optimization" title=
       </div>
     </a>
     <a class="right-next"
-       href="economic-order-quantity.html"
+       href="01-economic-order-quantity.html"
        title="next page">
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diff --git a/notebooks/06/optimal-growth-portfolios.html b/notebooks/06/07-optimal-growth-portfolios.html
similarity index 96%
rename from notebooks/06/optimal-growth-portfolios.html
rename to notebooks/06/07-optimal-growth-portfolios.html
index a14566d4..10524664 100644
--- a/notebooks/06/optimal-growth-portfolios.html
+++ b/notebooks/06/07-optimal-growth-portfolios.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/optimal-growth-portfolios';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/06/07-optimal-growth-portfolios';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
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     <link rel="next" title="7. Accounting for Uncertainty: Optimization Meets Reality" href="../07/07.00.html" />
-    <link rel="prev" title="Extra material: Luenberger’s Investment Wheel" href="investment-wheel.html" />
+    <link rel="prev" title="Extra material: Luenberger’s Investment Wheel" href="06-investment-wheel.html" />
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   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="06.00.html">6. Conic Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/optimal-growth-portfolios.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/06/07-optimal-growth-portfolios.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/optimal-growth-portfolios.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/06/07-optimal-growth-portfolios.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/06/optimal-growth-portfolios.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/06/07-optimal-growth-portfolios.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1183,7 +1186,7 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="06-investment-wheel.html"
        title="previous page">
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diff --git a/notebooks/07/fleet-assignment.html b/notebooks/07/01-fleet-assignment.html
similarity index 97%
rename from notebooks/07/fleet-assignment.html
rename to notebooks/07/01-fleet-assignment.html
index 3cedaf76..3e8568f1 100644
--- a/notebooks/07/fleet-assignment.html
+++ b/notebooks/07/01-fleet-assignment.html
@@ -71,10 +71,10 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/07/fleet-assignment';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/07/01-fleet-assignment';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="Robustness analysis of BIM production plan via simulations" href="bim-robustness-analysis.html" />
+    <link rel="next" title="Robustness analysis of BIM production plan via simulations" href="02-bim-robustness-analysis.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/07/fleet-assignment.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/07/01-fleet-assignment.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/07/fleet-assignment.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/07/01-fleet-assignment.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/07/fleet-assignment.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/07/01-fleet-assignment.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1518,7 +1521,7 @@ <h2>Reducing riskiness of the schedule<a class="headerlink" href="#reducing-risk
       </div>
     </a>
     <a class="right-next"
-       href="bim-robustness-analysis.html"
+       href="02-bim-robustness-analysis.html"
        title="next page">
       <div class="prev-next-info">
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diff --git a/notebooks/07/bim-robustness-analysis.html b/notebooks/07/02-bim-robustness-analysis.html
similarity index 96%
rename from notebooks/07/bim-robustness-analysis.html
rename to notebooks/07/02-bim-robustness-analysis.html
index 63cc0564..b2f5cd5e 100644
--- a/notebooks/07/bim-robustness-analysis.html
+++ b/notebooks/07/02-bim-robustness-analysis.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/07/bim-robustness-analysis';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/07/02-bim-robustness-analysis';</script>
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+    <link rel="prev" title="Fleet assignment problem" href="01-fleet-assignment.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-fleet-assignment.html">Fleet assignment problem</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/07/bim-robustness-analysis.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/07/02-bim-robustness-analysis.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/07/bim-robustness-analysis.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/07/02-bim-robustness-analysis.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/07/bim-robustness-analysis.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/07/02-bim-robustness-analysis.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1502,7 +1505,7 @@ <h2>Actual performance of the robust solution<a class="headerlink" href="#actual
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="01-fleet-assignment.html"
        title="previous page">
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diff --git a/notebooks/07/07.00.html b/notebooks/07/07.00.html
index c10dd02c..851dfbcb 100644
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+++ b/notebooks/07/07.00.html
@@ -72,8 +72,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/07/07.00';</script>
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     <link rel="search" title="Search" href="../../search.html" />
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-    <link rel="prev" title="Extra material: Optimal Growth Portfolios with Risk Aversion" href="../06/optimal-growth-portfolios.html" />
+    <link rel="next" title="Fleet assignment problem" href="01-fleet-assignment.html" />
+    <link rel="prev" title="Extra material: Optimal Growth Portfolios with Risk Aversion" href="../06/07-optimal-growth-portfolios.html" />
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@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">7. Accounting for Uncertainty: Optimization Meets Reality</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -497,8 +500,8 @@ <h1>7. Accounting for Uncertainty: Optimization Meets Reality<a class="headerlin
 </ul>
 <p>We will show three examples of how one can inspect solutions for their sensitivity to data changes.</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="fleet-assignment.html"><span class="doc std std-doc">Fleet assignment problem</span></a></p></li>
-<li><p><a class="reference internal" href="bim-robustness-analysis.html"><span class="doc std std-doc">Robustness analysis of BIM production plan via simulations</span></a></p></li>
+<li><p><a class="reference internal" href="01-fleet-assignment.html"><span class="doc std std-doc">Fleet assignment problem</span></a></p></li>
+<li><p><a class="reference internal" href="02-bim-robustness-analysis.html"><span class="doc std std-doc">Robustness analysis of BIM production plan via simulations</span></a></p></li>
 </ul>
 <p>Go to the <a class="reference internal" href="../08/08.00.html"><span class="doc std std-doc">next chapter</span></a> about robust optimization and to <a class="reference internal" href="../09/09.00.html"><span class="doc std std-doc">Chapter 9</span></a> about stochastic optimization.</p>
 <div class="toctree-wrapper compound">
@@ -536,7 +539,7 @@ <h1>7. Accounting for Uncertainty: Optimization Meets Reality<a class="headerlin
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="../06/optimal-growth-portfolios.html"
+       href="../06/07-optimal-growth-portfolios.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
@@ -545,7 +548,7 @@ <h1>7. Accounting for Uncertainty: Optimization Meets Reality<a class="headerlin
       </div>
     </a>
     <a class="right-next"
-       href="fleet-assignment.html"
+       href="01-fleet-assignment.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/08/bim-robust-optimization.html b/notebooks/08/01-bim-robust-optimization.html
similarity index 97%
rename from notebooks/08/bim-robust-optimization.html
rename to notebooks/08/01-bim-robust-optimization.html
index 29f3bc97..ccfaf768 100644
--- a/notebooks/08/bim-robust-optimization.html
+++ b/notebooks/08/01-bim-robust-optimization.html
@@ -71,7 +71,7 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/08/bim-robust-optimization';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/08/01-bim-robust-optimization';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="9. Stochastic Optimization - Single Stage Problems" href="../09/09.00.html" />
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,41 +212,41 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="08.00.html">8. Robust Optimization - Single Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -250,20 +254,19 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/08/bim-robust-optimization.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/08/01-bim-robust-optimization.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/08/bim-robust-optimization.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/08/01-bim-robust-optimization.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/08/bim-robust-optimization.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/08/01-bim-robust-optimization.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
diff --git a/notebooks/08/08.00.html b/notebooks/08/08.00.html
index acf73946..45059b97 100644
--- a/notebooks/08/08.00.html
+++ b/notebooks/08/08.00.html
@@ -72,8 +72,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/08/08.00';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Robust BIM microchip production problem" href="bim-robust-optimization.html" />
-    <link rel="prev" title="Robustness analysis of BIM production plan via simulations" href="../07/bim-robustness-analysis.html" />
+    <link rel="next" title="Robust BIM microchip production problem" href="01-bim-robust-optimization.html" />
+    <link rel="prev" title="Robustness analysis of BIM production plan via simulations" href="../07/02-bim-robustness-analysis.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">8. Robust Optimization - Single Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -470,7 +473,7 @@ <h1>8. Robust Optimization - Single Stage Problems</h1>
 <h1>8. Robust Optimization - Single Stage Problems<a class="headerlink" href="#robust-optimization-single-stage-problems" title="Permalink to this heading">#</a></h1>
 <p>In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of robust optimization:</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="bim-robust-optimization.html"><span class="doc std std-doc">Robust version of BIM production problem</span></a></p></li>
+<li><p><a class="reference internal" href="01-bim-robust-optimization.html"><span class="doc std std-doc">Robust version of BIM production problem</span></a></p></li>
 </ul>
 <p>Go to the <a class="reference internal" href="../09/09.00.html"><span class="doc std std-doc">next chapter</span></a> about stochastic optimization.</p>
 <div class="toctree-wrapper compound">
@@ -508,7 +511,7 @@ <h1>8. Robust Optimization - Single Stage Problems<a class="headerlink" href="#r
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="../07/bim-robustness-analysis.html"
+       href="../07/02-bim-robustness-analysis.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
@@ -517,7 +520,7 @@ <h1>8. Robust Optimization - Single Stage Problems<a class="headerlink" href="#r
       </div>
     </a>
     <a class="right-next"
-       href="bim-robust-optimization.html"
+       href="01-bim-robust-optimization.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/09/pop-up_shop.html b/notebooks/09/01-pop-up-shop.html
similarity index 95%
rename from notebooks/09/pop-up_shop.html
rename to notebooks/09/01-pop-up-shop.html
index 1a4ec29f..1bdfa6f5 100644
--- a/notebooks/09/pop-up_shop.html
+++ b/notebooks/09/01-pop-up-shop.html
@@ -71,10 +71,10 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/pop-up_shop';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/01-pop-up-shop';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Markowitz portfolio optimization with chance constraints" href="markowitz_portfolio_with_chance_constraint.html" />
+    <link rel="next" title="Markowitz portfolio optimization with chance constraints" href="02-markowitz-portfolio-with-chance-constraint.html" />
     <link rel="prev" title="9. Stochastic Optimization - Single Stage Problems" href="09.00.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/pop-up_shop.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/01-pop-up-shop.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/pop-up_shop.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/01-pop-up-shop.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/09/pop-up_shop.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/09/01-pop-up-shop.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -1173,7 +1176,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
       </div>
     </a>
     <a class="right-next"
-       href="markowitz_portfolio_with_chance_constraint.html"
+       href="02-markowitz-portfolio-with-chance-constraint.html"
        title="next page">
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         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/09/markowitz_portfolio_with_chance_constraint.html b/notebooks/09/02-markowitz-portfolio-with-chance-constraint.html
similarity index 91%
rename from notebooks/09/markowitz_portfolio_with_chance_constraint.html
rename to notebooks/09/02-markowitz-portfolio-with-chance-constraint.html
index 6b6c025c..d4e22a5f 100644
--- a/notebooks/09/markowitz_portfolio_with_chance_constraint.html
+++ b/notebooks/09/02-markowitz-portfolio-with-chance-constraint.html
@@ -71,11 +71,11 @@
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     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/markowitz_portfolio_with_chance_constraint';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/02-markowitz-portfolio-with-chance-constraint';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Stock optimization for seafood distribution center" href="seafood.html" />
-    <link rel="prev" title="Pop-up shop" href="pop-up_shop.html" />
+    <link rel="next" title="Stock optimization for seafood distribution center" href="03-seafood-distribution-center.html" />
+    <link rel="prev" title="Pop-up shop" href="01-pop-up-shop.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="pop-up_shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-pop-up-shop.html">Pop-up shop</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/markowitz_portfolio_with_chance_constraint.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/markowitz_portfolio_with_chance_constraint.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/02-markowitz-portfolio-with-chance-constraint.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/09/markowitz_portfolio_with_chance_constraint.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -546,7 +549,7 @@ <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-in
 </section>
 <section id="problem-description">
 <h2>Problem description<a class="headerlink" href="#problem-description" title="Permalink to this heading">#</a></h2>
-<p>We consider here another variant of the Markowitz portfolio optimization problem, which we already encountered in the context of convex optimization <a class="reference internal" href="../05/markowitz_portfolio.html"><span class="doc std std-doc">here</span></a> and in the context of conic optimization <a class="reference internal" href="../06/markowitz_portfolio_revisited.html"><span class="doc std std-doc">here</span></a>.</p>
+<p>We consider here another variant of the Markowitz portfolio optimization problem, which we already encountered in the context of convex optimization <span class="xref myst">here</span> and in the context of conic optimization <span class="xref myst">here</span>.</p>
 <p>Assuming there is an initial unit capital <span class="math notranslate nohighlight">\(C\)</span> that needs to be invested in a selection of <span class="math notranslate nohighlight">\(n\)</span> possible assets, each of them with a unknown return rate <span class="math notranslate nohighlight">\(r_i\)</span>, <span class="math notranslate nohighlight">\(i=1,\dots,n\)</span>. Let <span class="math notranslate nohighlight">\(x\)</span> be the vector whose <span class="math notranslate nohighlight">\(i\)</span>-th component <span class="math notranslate nohighlight">\(x_i\)</span> describes the fraction of the capital invested in asset <span class="math notranslate nohighlight">\(i\)</span>. The return rate vector <span class="math notranslate nohighlight">\(r\)</span> can be modelled by a multivariate Gaussian distribution with mean <span class="math notranslate nohighlight">\(\mu\)</span> and covariance <span class="math notranslate nohighlight">\(\Sigma\)</span>. Assume there is also a risk-free asset with guaranteed return rate <span class="math notranslate nohighlight">\(R\)</span> and let <span class="math notranslate nohighlight">\(\tilde{x}\)</span> the amount invested in that asset. We want to determine the portfolio that maximizes the <em>expected</em> return <span class="math notranslate nohighlight">\(\mathbb{E} ( R \tilde{x} + r^\top x )\)</span>, which in view of our assumptions rewrites as <span class="math notranslate nohighlight">\( \mathbb{E} ( R \tilde{x} + r^\top x ) = R \tilde{x} + \mu^\top x\)</span>.</p>
 <p>Additionally, we includ a <em>loss risk chance constraint</em> of the form</p>
 <div class="math notranslate nohighlight">
@@ -677,7 +680,7 @@ <h2>Problem description<a class="headerlink" href="#problem-description" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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+       href="01-pop-up-shop.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
@@ -686,7 +689,7 @@ <h2>Problem description<a class="headerlink" href="#problem-description" title="
       </div>
     </a>
     <a class="right-next"
-       href="seafood.html"
+       href="03-seafood-distribution-center.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/09/seafood.html b/notebooks/09/03-seafood-distribution-center.html
similarity index 96%
rename from notebooks/09/seafood.html
rename to notebooks/09/03-seafood-distribution-center.html
index 74dbe662..3271b7e0 100644
--- a/notebooks/09/seafood.html
+++ b/notebooks/09/03-seafood-distribution-center.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/seafood';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/03-seafood-distribution-center';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Economic dispatch in energy systems" href="economicdispatch.html" />
-    <link rel="prev" title="Markowitz portfolio optimization with chance constraints" href="markowitz_portfolio_with_chance_constraint.html" />
+    <link rel="next" title="Economic dispatch in energy systems" href="04-economic-dispatch.html" />
+    <link rel="prev" title="Markowitz portfolio optimization with chance constraints" href="02-markowitz-portfolio-with-chance-constraint.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/seafood.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/03-seafood-distribution-center.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/seafood.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/03-seafood-distribution-center.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/09/seafood.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/09/03-seafood-distribution-center.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -985,7 +988,7 @@ <h2>Convergence of the SAA method<a class="headerlink" href="#convergence-of-the
                   <!-- Previous / next buttons -->
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@@ -994,7 +997,7 @@ <h2>Convergence of the SAA method<a class="headerlink" href="#convergence-of-the
       </div>
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-       href="economicdispatch.html"
+       href="04-economic-dispatch.html"
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diff --git a/notebooks/09/economicdispatch.html b/notebooks/09/04-economic-dispatch.html
similarity index 95%
rename from notebooks/09/economicdispatch.html
rename to notebooks/09/04-economic-dispatch.html
index 465d1830..410d22c2 100644
--- a/notebooks/09/economicdispatch.html
+++ b/notebooks/09/04-economic-dispatch.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/economicdispatch';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/04-economic-dispatch';</script>
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     <link rel="next" title="10. Two-Stage Problems" href="../10/10.00.html" />
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+    <link rel="prev" title="Stock optimization for seafood distribution center" href="03-seafood-distribution-center.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="seafood.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/economicdispatch.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/09/04-economic-dispatch.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/economicdispatch.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/09/04-economic-dispatch.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/09/economicdispatch.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/09/04-economic-dispatch.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -630,7 +633,9 @@ <h2>Data import<a class="headerlink" href="#data-import" title="Permalink to thi
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">read_economic_dispatch_data</span><span class="p">():</span>
-    <span class="n">base_url</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/09/&quot;</span>
+    <span class="n">base_url</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="s2">&quot;https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/09/data/&quot;</span>
+    <span class="p">)</span>
     <span class="n">nodes_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">base_url</span> <span class="o">+</span> <span class="s2">&quot;nodes.csv&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span>
         <span class="p">[</span><span class="s2">&quot;node_id&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;p_min&quot;</span><span class="p">,</span> <span class="s2">&quot;p_max&quot;</span><span class="p">,</span> <span class="s2">&quot;c_var&quot;</span><span class="p">]</span>
     <span class="p">]</span>
@@ -978,7 +983,7 @@ <h2>Sensitivity analysis<a class="headerlink" href="#sensitivity-analysis" title
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="seafood.html"
+       href="03-seafood-distribution-center.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
diff --git a/notebooks/09/09.00.html b/notebooks/09/09.00.html
index 9e280cad..ded1cce5 100644
--- a/notebooks/09/09.00.html
+++ b/notebooks/09/09.00.html
@@ -72,8 +72,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/09/09.00';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Pop-up shop" href="pop-up_shop.html" />
-    <link rel="prev" title="Robust BIM microchip production problem" href="../08/bim-robust-optimization.html" />
+    <link rel="next" title="Pop-up shop" href="01-pop-up-shop.html" />
+    <link rel="prev" title="Robust BIM microchip production problem" href="../08/01-bim-robust-optimization.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">9. Stochastic Optimization - Single Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -470,10 +473,10 @@ <h1>9. Stochastic Optimization - Single Stage Problems</h1>
 <h1>9. Stochastic Optimization - Single Stage Problems<a class="headerlink" href="#stochastic-optimization-single-stage-problems" title="Permalink to this heading">#</a></h1>
 <p>In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of stochastic optimization:</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="pop-up_shop.html"><span class="doc std std-doc">Optimal management of a pop-up shop</span></a></p></li>
-<li><p><a class="reference internal" href="markowitz_portfolio_with_chance_constraint.html"><span class="doc std std-doc">Markowitz portfolio with chance constraints</span></a></p></li>
-<li><p><a class="reference internal" href="seafood.html"><span class="doc std std-doc">Stock optimization for seafood distribution center</span></a></p></li>
-<li><p><a class="reference internal" href="economicdispatch.html"><span class="doc std std-doc">Economic dispatch in energy systems</span></a></p></li>
+<li><p><a class="reference internal" href="01-pop-up-shop.html"><span class="doc std std-doc">Optimal management of a pop-up shop</span></a></p></li>
+<li><p><a class="reference internal" href="02-markowitz-portfolio-with-chance-constraint.html"><span class="doc std std-doc">Markowitz portfolio with chance constraints</span></a></p></li>
+<li><p><a class="reference internal" href="03-seafood-distribution-center.html"><span class="doc std std-doc">Stock optimization for seafood distribution center</span></a></p></li>
+<li><p><a class="reference internal" href="04-economic-dispatch.html"><span class="doc std std-doc">Economic dispatch in energy systems</span></a></p></li>
 </ul>
 <p>Go to the <a class="reference internal" href="../10/10.00.html"><span class="doc std std-doc">next chapter</span></a> about two-stage (robust and stochastic) optimization.</p>
 <div class="toctree-wrapper compound">
@@ -511,7 +514,7 @@ <h1>9. Stochastic Optimization - Single Stage Problems<a class="headerlink" href
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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-       href="../08/bim-robust-optimization.html"
+       href="../08/01-bim-robust-optimization.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
@@ -520,7 +523,7 @@ <h1>9. Stochastic Optimization - Single Stage Problems<a class="headerlink" href
       </div>
     </a>
     <a class="right-next"
-       href="pop-up_shop.html"
+       href="01-pop-up-shop.html"
        title="next page">
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         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/10/opf-ldr.html b/notebooks/10/02-opf-linear-decision-rule.html
similarity index 91%
rename from notebooks/10/opf-ldr.html
rename to notebooks/10/02-opf-linear-decision-rule.html
index 26d65754..fbc2f8fa 100644
--- a/notebooks/10/opf-ldr.html
+++ b/notebooks/10/02-opf-linear-decision-rule.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/opf-ldr';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/02-opf-linear-decision-rule';</script>
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Two-stage Production Planning" href="ccg.html" />
-    <link rel="prev" title="Airline seat allocation problem" href="airline-seating.html" />
+    <link rel="next" title="Two-stage production planning using constraint and column generation" href="03-two-stage-production-planning.html" />
+    <link rel="prev" title="10. Two-Stage Problems" href="10.00.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="10.00.html">10. Two-Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="airline-seating.html">Airline seat allocation problem</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/opf-ldr.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/02-opf-linear-decision-rule.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/opf-ldr.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/02-opf-linear-decision-rule.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/10/opf-ldr.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/10/02-opf-linear-decision-rule.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -520,7 +523,7 @@ <h2> Contents </h2>
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><section class="tex2jax_ignore mathjax_ignore" id="optimal-power-flow-problem-with-recourse-actions">
 <span id="index-6"></span><h1>Optimal power flow problem with recourse actions<a class="headerlink" href="#optimal-power-flow-problem-with-recourse-actions" title="Permalink to this heading">#</a></h1>
 <p>In this notebook we illustrate an application of the idea of linear decision rules to a two-stage optimal power flow problem in which the power of the conventional generators has to adapt automatically to balance out energy surplus/shortage due to changes in renewable resources input.</p>
-<p>We will be working with a modified version of the <a class="reference internal" href="../04/power-network.html"><span class="doc std std-doc">energy dispatch problem</span></a> and the <a class="reference internal" href="opf-wind-curtailment.html"><span class="doc std std-doc">OPF problem with wind curtailment</span></a>. Please refer to the corresponding notebooks for the basics of power networks.</p>
+<p>We will be working with a modified version of the <span class="xref myst">energy dispatch problem</span> and the <span class="xref myst">OPF problem with wind curtailment</span>. Please refer to the corresponding notebooks for the basics of power networks.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install pyomo and select solver</span>
@@ -577,7 +580,7 @@ <h2>Problem description<a class="headerlink" href="#problem-description" title="
 \end{align*}
 \)</span>$</p>
 <p>where <span class="math notranslate nohighlight">\(c_i(.)\)</span> and <span class="math notranslate nohighlight">\(\hat{c}_i(.)\)</span> are the cost functions related to the pre-committed <span class="math notranslate nohighlight">\(\bar{p}_i\)</span> and changed <span class="math notranslate nohighlight">\(r_i\)</span> amounts of energy, remembering that among <span class="math notranslate nohighlight">\(\bar{p}_i\)</span>’s, only those with <span class="math notranslate nohighlight">\(i \in \mathcal{G}^{\text{coal}} \cup \mathcal{G}^{\text{gas}}\)</span> are actual decision variables while <span class="math notranslate nohighlight">\(\bar{p}_i\)</span>, <span class="math notranslate nohighlight">\(i \in \mathcal{G}^{\text{solar}} \cup \mathcal{G}^{\text{wind}}\)</span> are known parameters (think of them as wind and solar power forecasts).</p>
-<p>In the <a class="reference internal" href="opf-wind-curtailment.html"><span class="doc std std-doc">OPF problem with wind curtailment</span></a> we solved a similar problem using the SAA method. More specifically, we generated a number of scenarios for the unknown parameters and combined the second-stage problems related to each of these scenarios with the single first-stage problem obtaining one large MILO problem.
+<p>In the <span class="xref myst">OPF problem with wind curtailment</span> we solved a similar problem using the SAA method. More specifically, we generated a number of scenarios for the unknown parameters and combined the second-stage problems related to each of these scenarios with the single first-stage problem obtaining one large MILO problem.
 In this notebook, we are going to use a slightly different approach, namely implementing a specific type of linear decision rules. For each conventional generator, we set a recourse action, that is real-time adjustment of its power production, based on the realization of the renewable energy. More specifically, each conventional generator <span class="math notranslate nohighlight">\(i \in \mathcal{G}^{\text{coal}} \cup \mathcal{G}^{\text{gas}}\)</span> has a <em>participation factor</em> <span class="math notranslate nohighlight">\(\alpha_i \geq 0\)</span> which determines to which extent that generator responds to the total imbalance <span class="math notranslate nohighlight">\(\sum_j \Delta_j\)</span>. Specifically, the power production <em>after the recourse action</em> at the conventional generator <span class="math notranslate nohighlight">\(i\)</span> is denoted by <span class="math notranslate nohighlight">\(p_i\)</span> and is given by</p>
 <div class="math notranslate nohighlight">
 \[
@@ -660,7 +663,7 @@ <h2>Problem description<a class="headerlink" href="#problem-description" title="
 <h2>Pyomo solution<a class="headerlink" href="#pyomo-solution" title="Permalink to this heading">#</a></h2>
 <section id="data-imports">
 <h3>Data imports<a class="headerlink" href="#data-imports" title="Permalink to this heading">#</a></h3>
-<p>Importantly for our problem, the variable costs of producing a unit of energy per generator are stored in their corresponding ‘c_var’ attributes. For the rest, we are using the same data elements as in the <a class="reference internal" href="opf-wind-curtailment.html"><span class="doc std std-doc">OPF problem with wind curtailment</span></a>.</p>
+<p>Importantly for our problem, the variable costs of producing a unit of energy per generator are stored in their corresponding ‘c_var’ attributes. For the rest, we are using the same data elements as in the <span class="xref myst">OPF problem with wind curtailment</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load packages</span>
@@ -727,9 +730,7 @@ <h3>SAA implementation<a class="headerlink" href="#saa-implementation" title="Pe
     <span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">NG</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span>
         <span class="n">initialize</span><span class="o">=</span><span class="p">[</span>
-            <span class="n">i</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-            <span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">])</span>
+            <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">])</span>
         <span class="p">]</span>
     <span class="p">)</span>
 
@@ -757,10 +758,7 @@ <h3>SAA implementation<a class="headerlink" href="#saa-implementation" title="Pe
         <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
         <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span>
             <span class="nb">sum</span><span class="p">(</span>
-                <span class="mi">2</span>
-                <span class="o">*</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;c_var&quot;</span><span class="p">]</span>
-                <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">abs_total_imbalance</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
+                <span class="mi">2</span> <span class="o">*</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;c_var&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">abs_total_imbalance</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
                 <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
                 <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;coal&quot;</span><span class="p">,</span> <span class="s2">&quot;gas&quot;</span><span class="p">]</span>
             <span class="p">)</span>
@@ -789,9 +787,7 @@ <h3>SAA implementation<a class="headerlink" href="#saa-implementation" title="Pe
     <span class="p">)</span>
 
     <span class="c1"># Participation factors must sum to one</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">sum_one</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
-        <span class="n">rule</span><span class="o">=</span><span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
-    <span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">sum_one</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">rule</span><span class="o">=</span><span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="n">uniformparticipationfactors</span><span class="p">:</span>
         <span class="c1"># Participation factors must be equal</span>
@@ -803,8 +799,7 @@ <h3>SAA implementation<a class="headerlink" href="#saa-implementation" title="Pe
     <span class="n">model</span><span class="o">.</span><span class="n">power_withrecourse</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
         <span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span>
         <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span>
-        <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">total_imbalance</span><span class="p">[</span><span class="n">t</span><span class="p">],</span>
+        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">total_imbalance</span><span class="p">[</span><span class="n">t</span><span class="p">],</span>
     <span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">generation_upper_bound_withrecourse</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
         <span class="n">model</span><span class="o">.</span><span class="n">CG</span><span class="p">,</span>
@@ -821,16 +816,12 @@ <h3>SAA implementation<a class="headerlink" href="#saa-implementation" title="Pe
     <span class="n">model</span><span class="o">.</span><span class="n">outgoing_flow</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Expression</span><span class="p">(</span>
         <span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span>
         <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span>
-            <span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span> <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">E</span>
-        <span class="p">),</span>
+        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span> <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">),</span>
     <span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">incoming_flow</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Expression</span><span class="p">(</span>
         <span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span>
         <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span>
-            <span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span> <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">E</span>
-        <span class="p">),</span>
+        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span> <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">),</span>
     <span class="p">)</span>
 
     <span class="c1"># Net power production at each node after recourse actions</span>
@@ -851,14 +842,12 @@ <h3>SAA implementation<a class="headerlink" href="#saa-implementation" title="Pe
     <span class="n">model</span><span class="o">.</span><span class="n">flows_upper_bound</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
         <span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">,</span>
         <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">t</span><span class="p">]</span>
-        <span class="o">&lt;=</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">],</span>
+        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">t</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">],</span>
     <span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">flows_lower_bound</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
         <span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">,</span>
         <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="o">-</span><span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">t</span><span class="p">]</span>
-        <span class="o">&lt;=</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">],</span>
+        <span class="n">rule</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="o">-</span><span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">t</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">],</span>
     <span class="p">)</span>
 
     <span class="c1"># Solve the model</span>
@@ -897,12 +886,8 @@ <h3>Scenario generation<a class="headerlink" href="#scenario-generation" title="
     <span class="p">}</span>
     <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
 <span class="p">]</span>
-<span class="n">totalimbalances</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">t</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">imbalances</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imbalances</span><span class="p">))</span>
-<span class="p">}</span>
-<span class="n">abstotalimbalances</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">t</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span><span class="n">totalimbalances</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">totalimbalances</span><span class="p">))</span>
-<span class="p">}</span>
+<span class="n">totalimbalances</span> <span class="o">=</span> <span class="p">{</span><span class="n">t</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">imbalances</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imbalances</span><span class="p">))}</span>
+<span class="n">abstotalimbalances</span> <span class="o">=</span> <span class="p">{</span><span class="n">t</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span><span class="n">totalimbalances</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">totalimbalances</span><span class="p">))}</span>
 </pre></div>
 </div>
 </div>
@@ -946,7 +931,7 @@ <h3>Perfect forecast case (no imbalances)<a class="headerlink" href="#perfect-fo
 </div>
 </div>
 <p>It is key to understand how this solution would perform if there were perturbations in renewable energy production.</p>
-<p>First of all, it is not guaranteed that it is possible to find a feasible solution in such a case. Following an approach similar to that of the <a class="reference internal" href="opf-wind-curtailment.html"><span class="doc std std-doc">OPF problem with wind curtailment</span></a>, one can solve for the remaining variables when keeping the initial solution <span class="math notranslate nohighlight">\(\bar{p}\)</span> fixed and check that with uniform participation factors, it is not possible to find a feasible flow in any of the scenarios we consider. If instead we allow for non-uniform participation factors, then this is not possible in 13 out of 100 scenarios.</p>
+<p>First of all, it is not guaranteed that it is possible to find a feasible solution in such a case. Following an approach similar to that of the <span class="xref myst">OPF problem with wind curtailment</span>, one can solve for the remaining variables when keeping the initial solution <span class="math notranslate nohighlight">\(\bar{p}\)</span> fixed and check that with uniform participation factors, it is not possible to find a feasible flow in any of the scenarios we consider. If instead we allow for non-uniform participation factors, then this is not possible in 13 out of 100 scenarios.</p>
 <p>Putting the feasibility issues aside for a moment, let us check how much extra cost would there be with uniform participation factors would there be on average across our scenarios. We can calculate this by taking the total imbalance and computing the cost of the recourse action to cover it, assuming that every coal and gas generator adjusts its production proportionally to the optimal participation factor previously obtained.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1134,20 +1119,20 @@ <h3>Stochastic case (nonzero imbalances)<a class="headerlink" href="#stochastic-
                   <!-- Previous / next buttons -->
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        title="previous page">
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         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Airline seat allocation problem</p>
+        <p class="prev-next-title">10. Two-Stage Problems</p>
       </div>
     </a>
     <a class="right-next"
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+       href="03-two-stage-production-planning.html"
        title="next page">
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-        <p class="prev-next-title">Two-stage Production Planning</p>
+        <p class="prev-next-title">Two-stage production planning using constraint and column generation</p>
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diff --git a/notebooks/10/ccg.html b/notebooks/10/03-two-stage-production-planning.html
similarity index 95%
rename from notebooks/10/ccg.html
rename to notebooks/10/03-two-stage-production-planning.html
index 1e83bedd..f53cc26a 100644
--- a/notebooks/10/ccg.html
+++ b/notebooks/10/03-two-stage-production-planning.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Two-stage Production Planning &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
+    <title>Two-stage production planning using constraint and column generation &#8212; Companion code for the book &#34;Hands-On Mathematical Optimization with Python&#34;</title>
   
   
   
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/ccg';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/03-two-stage-production-planning';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra: The farmer’s problem and its variants" href="farmer.html" />
-    <link rel="prev" title="Optimal power flow problem with recourse actions" href="opf-ldr.html" />
+    <link rel="next" title="Extra: The farmer’s problem and its variants" href="04-farmer-problem.html" />
+    <link rel="prev" title="Optimal power flow problem with recourse actions" href="02-opf-linear-decision-rule.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="10.00.html">10. Two-Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/ccg.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/03-two-stage-production-planning.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/ccg.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/03-two-stage-production-planning.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/10/ccg.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/10/03-two-stage-production-planning.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -487,7 +490,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Two-stage Production Planning</h1>
+    <h1>Two-stage production planning using constraint and column generation</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -515,8 +518,8 @@ <h2> Contents </h2>
 <div id="searchbox"></div>
                 <article class="bd-article" role="main">
                   
-  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><section class="tex2jax_ignore mathjax_ignore" id="two-stage-production-planning">
-<span id="index-6"></span><h1>Two-stage Production Planning<a class="headerlink" href="#two-stage-production-planning" title="Permalink to this heading">#</a></h1>
+  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><section class="tex2jax_ignore mathjax_ignore" id="two-stage-production-planning-using-constraint-and-column-generation">
+<span id="index-6"></span><h1>Two-stage production planning using constraint and column generation<a class="headerlink" href="#two-stage-production-planning-using-constraint-and-column-generation" title="Permalink to this heading">#</a></h1>
 <p>The purpose of this notebook is to demonstrate a range of techniques for two-stage optimization (robust and stochastic) using a range of techniques:</p>
 <ul class="simple">
 <li><p>Robust Optimization (including Constraint and Column Generation)</p></li>
@@ -988,9 +991,7 @@ <h2>The average-case optimization using SAA<a class="headerlink" href="#the-aver
 <span class="n">worst_case_ps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
 <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Z</span><span class="p">)):</span>
     <span class="n">c</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">R</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">model_params</span><span class="p">(</span><span class="o">**</span><span class="n">Z</span><span class="p">[</span><span class="n">s</span><span class="p">])</span>
-    <span class="n">worst_case_ps</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="nb">sum</span><span class="p">(</span><span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">c</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="s2">&quot;y3&quot;</span><span class="p">]()</span>
-    <span class="p">)</span>
+    <span class="n">worst_case_ps</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">c</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="s2">&quot;y3&quot;</span><span class="p">]()</span>
 
 <span class="n">xopt_avg</span> <span class="o">=</span> <span class="mf">637.08</span>
 <span class="nb">print</span><span class="p">(</span>
@@ -1172,10 +1173,7 @@ <h2>Column and constraint generation for the robust solution<a class="headerlink
         <span class="k">def</span> <span class="nf">at_least_one_violated</span><span class="p">(</span>
             <span class="n">b</span><span class="p">,</span>
         <span class="p">):</span>  <span class="c1"># only care for violations related to the `actual&#39; constraints, not the profit constraint</span>
-            <span class="k">return</span> <span class="p">(</span>
-                <span class="nb">sum</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">L</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="s2">&quot;profit&quot;</span><span class="p">)</span>
-                <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">L</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="mi">2</span>
-            <span class="p">)</span>
+            <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">u</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">L</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="s2">&quot;profit&quot;</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">L</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="mi">2</span>
 
         <span class="nd">@b</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">L</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
         <span class="k">def</span> <span class="nf">model_constraints</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
@@ -1313,7 +1311,7 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="opf-ldr.html"
+       href="02-opf-linear-decision-rule.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
@@ -1322,7 +1320,7 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
       </div>
     </a>
     <a class="right-next"
-       href="farmer.html"
+       href="04-farmer-problem.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
diff --git a/notebooks/10/farmer.html b/notebooks/10/04-farmer-problem.html
similarity index 93%
rename from notebooks/10/farmer.html
rename to notebooks/10/04-farmer-problem.html
index e2302967..d9ff6f03 100644
--- a/notebooks/10/farmer.html
+++ b/notebooks/10/04-farmer-problem.html
@@ -71,11 +71,11 @@
     <script async="async" src="../../_static/sphinx-thebe.js"></script>
     <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/farmer';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/04-farmer-problem';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Extra: Two-stage energy dispatch optimization with wind curtailment" href="opf-wind-curtailment.html" />
-    <link rel="prev" title="Two-stage Production Planning" href="ccg.html" />
+    <link rel="next" title="Extra: Two-stage energy dispatch optimization with wind curtailment" href="05-opf-wind-curtailment.html" />
+    <link rel="prev" title="Two-stage production planning using constraint and column generation" href="03-two-stage-production-planning.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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   </head>
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="10.00.html">10. Two-Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ccg.html">Two-stage Production Planning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/farmer.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/04-farmer-problem.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/farmer.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/04-farmer-problem.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/10/farmer.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/10/04-farmer-problem.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -883,20 +886,12 @@ <h2>Model Building<a class="headerlink" href="#model-building" title="Permalink
         <span class="n">df</span><span class="p">[</span><span class="s2">&quot;plant [acres]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">acres</span><span class="p">[</span><span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
         <span class="n">df</span><span class="p">[</span><span class="s2">&quot;grow [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">grow</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
         <span class="n">df</span><span class="p">[</span><span class="s2">&quot;buy [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">buy</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
-        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;feed [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-            <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">crops</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;required&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">}</span>
-        <span class="p">)</span>
-        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;quota [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-            <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">crops</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;quota&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">}</span>
-        <span class="p">)</span>
+        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;feed [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">crops</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;required&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
+        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;quota [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">crops</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;quota&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
         <span class="n">df</span><span class="p">[</span><span class="s2">&quot;sell [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">sell</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
         <span class="n">df</span><span class="p">[</span><span class="s2">&quot;excess [tons]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">excess</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
-        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;revenue [euro]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-            <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">revenue</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">}</span>
-        <span class="p">)</span>
-        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;expense [euro]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-            <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">expense</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">}</span>
-        <span class="p">)</span>
+        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;revenue [euro]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">revenue</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
+        <span class="n">df</span><span class="p">[</span><span class="s2">&quot;expense [euro]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">expense</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">})</span>
         <span class="n">df</span><span class="p">[</span><span class="s2">&quot;profit [euro]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
             <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">revenue</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">expense</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CROPS</span><span class="p">}</span>
         <span class="p">)</span>
@@ -912,10 +907,12 @@ <h2>1. Mean Solution<a class="headerlink" href="#mean-solution" title="Permalink
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">farmer</span><span class="p">(</span><span class="n">crops</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">yields</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
 
+
 <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">pyo</span><span class="o">.</span><span class="n">summation</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">scenario_profit</span><span class="p">)</span>
 
+
 <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
 <span class="n">farm_report</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
 </pre></div>
@@ -1795,9 +1792,7 @@ <h2>4. Risk versus Return<a class="headerlink" href="#risk-versus-return" title=
     <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">wc</span><span class="p">,</span> <span class="s2">&quot;return&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">objective</span><span class="p">()</span>
     <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">wc</span><span class="p">,</span> <span class="s2">&quot;wc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">wc</span>
 
-<span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;wc&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;return&quot;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;worst case&quot;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;mean return&quot;</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span>
-<span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;wc&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;return&quot;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;worst case&quot;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;mean return&quot;</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1832,9 +1827,7 @@ <h2>5. Recommended Planting Strategy<a class="headerlink" href="#recommended-pla
     <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">wc</span><span class="p">,</span> <span class="s2">&quot;return&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">objective</span><span class="p">()</span>
     <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">wc</span><span class="p">,</span> <span class="s2">&quot;wc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">wc</span>
 
-<span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;wc&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;return&quot;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;worst case&quot;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;mean return&quot;</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span>
-<span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;wc&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;return&quot;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;worst case&quot;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;mean return&quot;</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -2145,16 +2138,16 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="ccg.html"
+       href="03-two-stage-production-planning.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Two-stage Production Planning</p>
+        <p class="prev-next-title">Two-stage production planning using constraint and column generation</p>
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     <a class="right-next"
-       href="opf-wind-curtailment.html"
+       href="05-opf-wind-curtailment.html"
        title="next page">
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diff --git a/notebooks/10/opf-wind-curtailment.html b/notebooks/10/05-opf-wind-curtailment.html
similarity index 81%
rename from notebooks/10/opf-wind-curtailment.html
rename to notebooks/10/05-opf-wind-curtailment.html
index acb8d91d..80131a50 100644
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+++ b/notebooks/10/05-opf-wind-curtailment.html
@@ -71,11 +71,11 @@
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-    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/opf-wind-curtailment';</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/05-opf-wind-curtailment';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
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     <link rel="next" title="Appendix: Working with Pyomo" href="../appendix/appendix.html" />
-    <link rel="prev" title="Extra: The farmer’s problem and its variants" href="farmer.html" />
+    <link rel="prev" title="Extra: The farmer’s problem and its variants" href="04-farmer-problem.html" />
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@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,61 +212,60 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="10.00.html">10. Two-Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="farmer.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
@@ -327,7 +330,7 @@
       
       
       
-      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/opf-wind-curtailment.ipynb" target="_blank"
+      <li><a href="https://colab.research.google.com/github/mobook/MO-book/blob/main/notebooks/10/05-opf-wind-curtailment.ipynb" target="_blank"
    class="btn btn-sm dropdown-item"
    title="Launch onColab"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -375,7 +378,7 @@
       
       
       
-      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/opf-wind-curtailment.html&body=Your%20issue%20content%20here." target="_blank"
+      <li><a href="https://github.com/mobook/MO-book/issues/new?title=Issue%20on%20page%20%2Fnotebooks/10/05-opf-wind-curtailment.html&body=Your%20issue%20content%20here." target="_blank"
    class="btn btn-sm btn-source-issues-button dropdown-item"
    title="Open an issue"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -405,7 +408,7 @@
       
       
       
-      <li><a href="../../_sources/notebooks/10/opf-wind-curtailment.ipynb" target="_blank"
+      <li><a href="../../_sources/notebooks/10/05-opf-wind-curtailment.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -526,22 +529,21 @@ <h2> Contents </h2>
 <span id="index-5"></span><h1>Extra: Two-stage energy dispatch optimization with wind curtailment<a class="headerlink" href="#extra-two-stage-energy-dispatch-optimization-with-wind-curtailment" title="Permalink to this heading">#</a></h1>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
-<p>This cell selects and verifies a global SOLVER for the notebook.</p>
-<p>If run on Google Colab, the cell installs Pyomo and HiGHS, then sets SOLVER to
-use the Highs solver via the appsi module. If run elsewhere, it assumes Pyomo and CBC
-have been previously installed and sets SOLVER to use the CBC solver via the Pyomo
-SolverFactory. It then verifies that SOLVER is available.</p>
+<p>The following cell sets and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and the HiGHS solver, while, if run elsewhere, it assumes Pyomo and HiGHS have been previously installed. It then sets to use HiGHS as solver via the appsi module and a test is performed to verify that it is available. The solver interface is stored in a global object <code class="docutils literal notranslate"><span class="pre">SOLVER</span></code> for later use.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">os</span>
-
-<span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
-    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pyomo<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
-    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>highspy<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
-    <span class="n">SOLVER</span> <span class="o">=</span> <span class="s2">&quot;appsi_highs&quot;</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">SOLVER</span> <span class="o">=</span> <span class="s2">&quot;cbc&quot;</span>
+ 
+<span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
+    <span class="o">%</span><span class="k">pip</span> install pyomo &gt;/dev/null 2&gt;/dev/null
+    <span class="o">%</span><span class="k">pip</span> install highspy &gt;/dev/null 2&gt;/dev/null
+ 
+<span class="n">solver</span> <span class="o">=</span> <span class="s1">&#39;appsi_highs&#39;</span>
+ 
+<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+<span class="n">SOLVER</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span>
+ 
+<span class="k">assert</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">solver</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
 </pre></div>
 </div>
 </div>
@@ -555,7 +557,7 @@ <h1>Preliminaries<a class="headerlink" href="#preliminaries" title="Permalink to
 <p>But what happens if power supply does not meet demand? When <em>supply exceeds demand</em>, then the electrical frequency increases. If <em>demand exceeds supply</em>, then the frequency drops. Since power plants are designed to operate within a certain frequency range, there is a risk that they will disconnect from the grid after a period of time. If the frequency deviates too much from the 50 Hertz, then there is a risk that the power plants switch off one after another, potentially leading to a complete power blackout.</p>
 <p>Using conventional power generators such as coal and gas, power supply and demand are often matched by increasing/decreasing the production rate of their power plants and taking generating units online or offline. With the advent of renewable energy sources, it has become increasingly more difficult to match supply and demand. Besides not being controllable power sources, network operators rely on forecasting models to predict the power generated by renewable sources. In practice, this prediction is fairly accurate for solar energy, but wind energy is particularly difficult to predict correctly.</p>
 <img src="https://www.mdpi.com/energies/energies-13-05595/article_deploy/html/images/energies-13-05595-g018.png" width="600"/>
-<p>The goal of this notebook is to ensure that power demand meets supply while taking into account wind fluctuations. We will introduce two optimization problems and solve them as stochastic optimization problems. <strong>Read first the <a class="reference internal" href="../04/power-network.html"><span class="doc std std-doc">energy dispatch problem</span></a> from the Chapter 4 for the preliminaries on power networks and the Optimal Power Flow (OPF) problem</strong>. An important difference from the setting there, is that here we <em>will not assume that the wind generation is a decision variable</em>. Instead, wind generation is a random variable, as will be explained later on. We do assume that solar and hydropower are decision variables, since the former is accurately predicated whereas the latter is controllable.</p>
+<p>The goal of this notebook is to ensure that power demand meets supply while taking into account wind fluctuations. We will introduce two optimization problems and solve them as stochastic optimization problems. <strong>Read first the <span class="xref myst">energy dispatch problem</span> from the Chapter 4 for the preliminaries on power networks and the Optimal Power Flow (OPF) problem</strong>. An important difference from the setting there, is that here we <em>will not assume that the wind generation is a decision variable</em>. Instead, wind generation is a random variable, as will be explained later on. We do assume that solar and hydropower are decision variables, since the former is accurately predicated whereas the latter is controllable.</p>
 <p><a name="uc"></a></p>
 <section id="unit-commitment">
 <h2>Unit Commitment<a class="headerlink" href="#unit-commitment" title="Permalink to this heading">#</a></h2>
@@ -621,8 +623,7 @@ <h2>Unit Commitment<a class="headerlink" href="#unit-commitment" title="Permalin
 <h2>Package and data import<a class="headerlink" href="#package-and-data-import" title="Permalink to this heading">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load packages</span>
-<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -631,7 +632,7 @@ <h2>Package and data import<a class="headerlink" href="#package-and-data-import"
 <span class="kn">import</span> <span class="nn">time</span>
 
 <span class="c1"># Download the data</span>
-<span class="n">base_url</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/10/&quot;</span>
+<span class="n">base_url</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/mobook/MO-book/main/notebooks/10/data/&quot;</span>
 <span class="n">nodes_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">base_url</span> <span class="o">+</span> <span class="s2">&quot;nodes.csv&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">edges_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">base_url</span> <span class="o">+</span> <span class="s2">&quot;edges.csv&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
@@ -645,7 +646,7 @@ <h2>Package and data import<a class="headerlink" href="#package-and-data-import"
 </div>
 <section id="network-data">
 <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to this heading">#</a></h3>
-<p>This notebook uses the same network as in <a class="reference internal" href="../04/power-network.html"><span class="doc std std-doc">energy dispatch problem</span></a> and hence the same data structure. In particular, the nodes and edges of the network are stored in the <code class="docutils literal notranslate"><span class="pre">nodes</span></code> and <code class="docutils literal notranslate"><span class="pre">edges</span></code> dataframes, respectively. The <code class="docutils literal notranslate"><span class="pre">edges</span></code> dataframe contains the following columns:</p>
+<p>This notebook uses the same network as in <span class="xref myst">energy dispatch problem</span> and hence the same data structure. In particular, the nodes and edges of the network are stored in the <code class="docutils literal notranslate"><span class="pre">nodes</span></code> and <code class="docutils literal notranslate"><span class="pre">edges</span></code> dataframes, respectively. The <code class="docutils literal notranslate"><span class="pre">edges</span></code> dataframe contains the following columns:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">edge</span> <span class="pre">id</span></code>: the unique identifier of the edge, describing the node pair <code class="docutils literal notranslate"><span class="pre">(i,j)</span></code> of the edge;</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">f_max</span></code>: describing the maximum power flow capacity of the edge; and</p></li>
@@ -1169,10 +1170,16 @@ <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to t
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">g_offshore</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">piecewise</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="p">[</span><span class="n">v</span> <span class="o">&lt;=</span> <span class="mf">3.5</span><span class="p">,</span> <span class="n">v</span> <span class="o">&gt;</span> <span class="mi">15</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">600</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="mf">0.18007</span> <span class="o">*</span> <span class="n">v</span><span class="o">**</span><span class="mi">3</span> <span class="o">-</span> <span class="mf">7.72049</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">piecewise</span><span class="p">(</span>
+        <span class="n">v</span><span class="p">,</span> <span class="p">[</span><span class="n">v</span> <span class="o">&lt;=</span> <span class="mf">3.5</span><span class="p">,</span> <span class="n">v</span> <span class="o">&gt;</span> <span class="mi">15</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">600</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="mf">0.18007</span> <span class="o">*</span> <span class="n">v</span><span class="o">**</span><span class="mi">3</span> <span class="o">-</span> <span class="mf">7.72049</span><span class="p">]</span>
+    <span class="p">)</span>
+
 
 <span class="k">def</span> <span class="nf">g_onshore</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">piecewise</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="p">[</span><span class="n">v</span> <span class="o">&lt;=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">v</span> <span class="o">&gt;</span> <span class="mi">14</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">450</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="mf">0.16563</span> <span class="o">*</span> <span class="n">v</span><span class="o">**</span><span class="mi">3</span> <span class="o">-</span> <span class="mf">4.4718</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">piecewise</span><span class="p">(</span>
+        <span class="n">v</span><span class="p">,</span> <span class="p">[</span><span class="n">v</span> <span class="o">&lt;=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">v</span> <span class="o">&gt;</span> <span class="mi">14</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">450</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="mf">0.16563</span> <span class="o">*</span> <span class="n">v</span><span class="o">**</span><span class="mi">3</span> <span class="o">-</span> <span class="mf">4.4718</span><span class="p">]</span>
+    <span class="p">)</span>
+
 
 <span class="c1"># assign wind generators</span>
 <span class="n">g</span> <span class="o">=</span> <span class="p">{</span><span class="mi">64</span><span class="p">:</span> <span class="n">g_onshore</span><span class="p">,</span> <span class="mi">65</span><span class="p">:</span> <span class="n">g_offshore</span><span class="p">}</span>
@@ -1184,7 +1191,7 @@ <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to t
 
 <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Wind Speed&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Power Output&quot;</span><span class="p">)</span> 
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Power Output&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1208,38 +1215,61 @@ <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to t
     <span class="n">model</span><span class="o">.</span><span class="n">T</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)))</span>
     <span class="n">model</span><span class="o">.</span><span class="n">V</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
     <span class="n">model</span><span class="o">.</span><span class="n">E</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">W</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;wind&quot;</span><span class="p">])</span>
-    
+    <span class="n">model</span><span class="o">.</span><span class="n">W</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span>
+        <span class="n">initialize</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;wind&quot;</span>
+        <span class="p">]</span>
+    <span class="p">)</span>
+
     <span class="c1"># Set including all generator nodes except wind generators</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">NW</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;wind&quot;</span><span class="p">])</span>  
-    
+    <span class="n">model</span><span class="o">.</span><span class="n">NW</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span>
+        <span class="n">initialize</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;wind&quot;</span>
+        <span class="p">]</span>
+    <span class="p">)</span>
+
     <span class="n">model</span><span class="o">.</span><span class="n">M</span> <span class="o">=</span> <span class="mi">1000</span>
 
     <span class="c1"># Declare decision variables</span>
-    
+
     <span class="c1"># Binary variable indicating whether a generator is on or off</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>  
-    
+    <span class="n">model</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>
+
     <span class="c1"># Power generation of each generator</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span> 
-    
+    <span class="n">model</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
+
     <span class="c1"># Voltage angle of each node</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> 
-    
+    <span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">V</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
     <span class="c1"># Power flow on each edge</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">],</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">]))</span>
-    
+    <span class="n">model</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">,</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+        <span class="n">bounds</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="p">(</span>
+            <span class="o">-</span><span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">],</span>
+            <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;f_max&quot;</span><span class="p">],</span>
+        <span class="p">),</span>
+    <span class="p">)</span>
+
     <span class="c1"># Binary variable indicating whether a wind turbine is curtailed (0) or not (1)</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>  
+    <span class="n">model</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>
 
     <span class="c1"># Objective function, with the goal of minimizing the total cost of generation</span>
     <span class="nd">@model</span><span class="o">.</span><span class="n">Expression</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">term2</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;c_var&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;is_generator&quot;</span><span class="p">])</span>
-    
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">data</span><span class="p">[</span><span class="s2">&quot;c_var&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;is_generator&quot;</span><span class="p">]</span>
+        <span class="p">)</span>
+
     <span class="nd">@model</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">minimize</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
-        <span class="n">term1</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="mi">1000</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;coal&quot;</span><span class="p">,</span> <span class="s2">&quot;gas&quot;</span><span class="p">])</span>
+        <span class="n">term1</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="mi">1000</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;coal&quot;</span><span class="p">,</span> <span class="s2">&quot;gas&quot;</span><span class="p">]</span>
+        <span class="p">)</span>
         <span class="k">return</span> <span class="n">term1</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">term2</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
 
     <span class="c1"># Declare constraints</span>
@@ -1248,7 +1278,7 @@ <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to t
     <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">wind_speed_to_power</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">])</span> <span class="o">*</span> <span class="n">g</span><span class="p">[</span><span class="n">i</span><span class="p">](</span><span class="n">samples</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
-    
+
     <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">wind_curtailment</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">samples</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">vmax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">M</span>
@@ -1256,7 +1286,7 @@ <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to t
     <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">NW</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">generation_upper_bound</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;p_max&quot;</span><span class="p">]</span>
-    
+
     <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">NW</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">generation_lower_bound</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;p_min&quot;</span><span class="p">]</span>
@@ -1267,10 +1297,12 @@ <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to t
         <span class="n">incoming_flow</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span> <span class="k">if</span> <span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">)</span>
         <span class="n">outgoing_flow</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">V</span> <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">incoming_flow</span> <span class="o">-</span> <span class="n">outgoing_flow</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">nodes</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;d&quot;</span><span class="p">]</span>
-    
+
     <span class="nd">@model</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">E</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">susceptance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;b&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">theta</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">theta</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">f</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">t</span><span class="p">]</span> <span class="o">==</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;edges&quot;</span><span class="p">][(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)][</span><span class="s2">&quot;b&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="n">m</span><span class="o">.</span><span class="n">theta</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">theta</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span>
+        <span class="p">)</span>
 
     <span class="k">return</span> <span class="n">model</span>
 </pre></div>
@@ -1288,15 +1320,24 @@ <h3>Network data<a class="headerlink" href="#network-data" title="Permalink to t
 <span class="n">scale65</span> <span class="o">=</span> <span class="mi">18</span>
 <span class="n">shape65</span> <span class="o">=</span> <span class="mi">3</span>
 
-<span class="n">samples</span> <span class="o">=</span> <span class="p">[{</span><span class="mi">64</span><span class="p">:</span> <span class="n">scale64</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">weibull</span><span class="p">(</span><span class="n">shape64</span><span class="p">),</span> <span class="mi">65</span><span class="p">:</span> <span class="n">scale65</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">weibull</span><span class="p">(</span><span class="n">shape65</span><span class="p">)}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)]</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">{</span><span class="mi">64</span><span class="p">:</span> <span class="n">scale64</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">weibull</span><span class="p">(</span><span class="n">shape64</span><span class="p">),</span> <span class="mi">65</span><span class="p">:</span> <span class="n">scale65</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">weibull</span><span class="p">(</span><span class="n">shape65</span><span class="p">)}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
+<span class="p">]</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">UC_windcurtailment</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">samples</span><span class="p">)</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Objective value: </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">objective</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="n">turbineactivity</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([[</span><span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">value</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">W</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of scenarios wind tubine 64 is active: </span><span class="si">{</span><span class="n">turbineactivity</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">2.0f</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of scenarios wind tubine 65 is active: </span><span class="si">{</span><span class="n">turbineactivity</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">2.0f</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">turbineactivity</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
+    <span class="p">[[</span><span class="n">model</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">value</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">W</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Number of scenarios wind tubine 64 is active: </span><span class="si">{</span><span class="n">turbineactivity</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">2.0f</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Number of scenarios wind tubine 65 is active: </span><span class="si">{</span><span class="n">turbineactivity</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">2.0f</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1317,14 +1358,19 @@ <h2>Assessing the quality of the solution<a class="headerlink" href="#assessing-
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">UC_windcurtailment_fixed_x</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">samples</span><span class="p">,</span> <span class="n">fixed_x</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-
     <span class="n">model</span> <span class="o">=</span> <span class="n">UC_windcurtailment</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">samples</span><span class="p">)</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">G</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;is_generator&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;wind&quot;</span><span class="p">])</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">G</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span>
+        <span class="n">initialize</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">i</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">network</span><span class="p">[</span><span class="s2">&quot;nodes&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;is_generator&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;energy_type&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;wind&quot;</span>
+        <span class="p">]</span>
+    <span class="p">)</span>
 
     <span class="k">if</span> <span class="n">fixed_x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">G</span><span class="p">:</span>
             <span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">fix</span><span class="p">(</span><span class="n">fixed_x</span><span class="p">[</span><span class="n">i</span><span class="p">]())</span>
-        
+
     <span class="k">return</span> <span class="n">model</span>
 </pre></div>
 </div>
@@ -1335,10 +1381,12 @@ <h2>Assessing the quality of the solution<a class="headerlink" href="#assessing-
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">T</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
 
-<span class="n">mean_sample</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">samples</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
+<span class="n">mean_sample</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">i</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">samples</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">)])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+<span class="p">}</span>
 
 <span class="n">m_nominal</span> <span class="o">=</span> <span class="n">UC_windcurtailment_fixed_x</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="p">[</span><span class="n">mean_sample</span><span class="p">])</span>
-<span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m_nominal</span><span class="p">)</span>
+<span class="n">SOLVER</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m_nominal</span><span class="p">)</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Nominal objective = </span><span class="si">{</span><span class="n">m_nominal</span><span class="o">.</span><span class="n">objective</span><span class="p">()</span><span class="si">:</span><span class="s2">0.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="n">fixed_x</span> <span class="o">=</span> <span class="n">m_nominal</span><span class="o">.</span><span class="n">x</span>
@@ -1357,17 +1405,16 @@ <h2>Assessing the quality of the solution<a class="headerlink" href="#assessing-
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
 
 <span class="c1"># don&#39;t log pyomo warning messages</span>
-<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;pyomo.core&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">ERROR</span><span class="p">)</span>
+<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;pyomo.core&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">ERROR</span><span class="p">)</span>
 
 <span class="c1"># Variable to count the number of infeasible second stage problems</span>
 <span class="n">n_infeasible</span> <span class="o">=</span> <span class="mi">0</span>
 
-<span class="n">solver</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
     <span class="n">m_single</span> <span class="o">=</span> <span class="n">UC_windcurtailment_fixed_x</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="p">[</span><span class="n">samples</span><span class="p">[</span><span class="n">t</span><span class="p">]],</span> <span class="n">fixed_x</span><span class="p">)</span>
-    <span class="n">results</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m_single</span><span class="p">)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m_single</span><span class="p">)</span>
     <span class="n">n_infeasible</span> <span class="o">+=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">results</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">termination_condition</span> <span class="o">==</span> <span class="s2">&quot;infeasible&quot;</span> <span class="k">else</span> <span class="mi">0</span>
- 
+
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The second stage problem is infeasible in </span><span class="si">{</span><span class="n">n_infeasible</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="n">T</span><span class="si">}</span><span class="s2"> instances&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -1413,7 +1460,7 @@ <h2>Assessing the quality of the solution<a class="headerlink" href="#assessing-
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="farmer.html"
+       href="04-farmer-problem.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
diff --git a/notebooks/10/10.00.html b/notebooks/10/10.00.html
index dc3978ff..1b6e71d5 100644
--- a/notebooks/10/10.00.html
+++ b/notebooks/10/10.00.html
@@ -72,8 +72,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/10/10.00';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Airline seat allocation problem" href="airline-seating.html" />
-    <link rel="prev" title="Economic dispatch in energy systems" href="../09/economicdispatch.html" />
+    <link rel="next" title="Optimal power flow problem with recourse actions" href="02-opf-linear-decision-rule.html" />
+    <link rel="prev" title="Economic dispatch in energy systems" href="../09/04-economic-dispatch.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">10. Two-Stage Problems</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -470,11 +473,11 @@ <h1>10. Two-Stage Problems</h1>
 <h1>10. Two-Stage Problems<a class="headerlink" href="#two-stage-problems" title="Permalink to this heading">#</a></h1>
 <p>In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of two-stage problems affected by uncertainty:</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="airline-seating.html"><span class="doc std std-doc">Airline seating splitting</span></a></p></li>
-<li><p><a class="reference internal" href="opf-ldr.html"><span class="doc std std-doc">Optimal power flow problem with recourse actions</span></a>, a two-stage revisitation of the problem from <a class="reference internal" href="../04/04.00.html"><span class="doc std std-doc">Chapter 4</span></a> that uses linear decision rules.</p></li>
-<li><p><a class="reference internal" href="ccg.html"><span class="doc std std-doc">Two-stage production planning</span></a>, a multi-stage revisitation of the introductory problem from <a class="reference internal" href="../01/01.00.html"><span class="doc std std-doc">Chapter 1</span></a> solved both with SAA and CCG.</p></li>
-<li><p><a class="reference internal" href="farmer.html"><span class="doc std std-doc">Extra: Farmer land allocation</span></a></p></li>
-<li><p><a class="reference internal" href="opf-wind-curtailment.html"><span class="doc std std-doc">Extra: Two-stage energy dispatch optimization with wind curtailment</span></a>, a two-stage revisitation of the problem from <a class="reference internal" href="../04/04.00.html"><span class="doc std std-doc">Chapter 4</span></a> with binary second-stage variables.</p></li>
+<li><p><span class="xref myst">Airline seating splitting</span></p></li>
+<li><p><a class="reference internal" href="02-opf-linear-decision-rule.html"><span class="doc std std-doc">Optimal power flow problem with recourse actions</span></a>, a two-stage variant of the problem from <a class="reference internal" href="../04/04.00.html"><span class="doc std std-doc">Chapter 4</span></a> that uses linear decision rules.</p></li>
+<li><p><span class="xref myst">Two-stage production planning</span>, a multi-stage variant of the introductory problem from <a class="reference internal" href="../01/01.00.html"><span class="doc std std-doc">Chapter 1</span></a> solved both with SAA and CCG.</p></li>
+<li><p><a class="reference internal" href="04-farmer-problem.html"><span class="doc std std-doc">Extra: Farmer land allocation</span></a></p></li>
+<li><p><a class="reference internal" href="05-opf-wind-curtailment.html"><span class="doc std std-doc">Extra: Two-stage energy dispatch optimization with wind curtailment</span></a>, a two-stage variant of the problem from <a class="reference internal" href="../04/04.00.html"><span class="doc std std-doc">Chapter 4</span></a> with binary second-stage variables.</p></li>
 </ul>
 <div class="toctree-wrapper compound">
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@@ -511,7 +514,7 @@ <h1>10. Two-Stage Problems<a class="headerlink" href="#two-stage-problems" title
                   <!-- Previous / next buttons -->
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+       href="../09/04-economic-dispatch.html"
        title="previous page">
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@@ -520,11 +523,11 @@ <h1>10. Two-Stage Problems<a class="headerlink" href="#two-stage-problems" title
       </div>
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     <a class="right-next"
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        title="next page">
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+        <p class="prev-next-title">Optimal power flow problem with recourse actions</p>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.00.html">1. Mathematical Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../01/02-production-planning-basic.html">1.2 A basic Pyomo model</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../01/03-production-planning-advanced.html">1.3 A data-driven Pyomo Model</a></li>
-</ul>
-</li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.00.html">2. Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../02/01-bim.html">2.1 BIM production</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../02/03-mad-portfolio-optimization.html">2.3 Mean Absolute Deviation (MAD) portfolio optimization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-
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-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
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-    <h1>Airline seat allocation problem</h1>
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-                <h2> Contents </h2>
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-            <nav aria-label="Page">
-                <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#attribution">Attribution</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#installation-and-imports">Installation and imports</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-data">Problem data</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#analytics">Analytics</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-1-deterministic-solution-for-the-average-demand-scenario">Model 1. Deterministic solution for the average demand scenario</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-2-two-stage-stochastic-optimization-and-its-extensive-form">Model 2. Two-stage stochastic optimization and its extensive form</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-3-adding-chance-constraints">Model 3. Adding chance constraints</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-4-solving-the-case-of-continuous-demand-distributions-using-the-saa-method">Model 4. Solving the case of continuous demand distributions using the SAA method</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scenario-generation-uncorrelated-case">Scenario generation (uncorrelated case)</a></li>
-</ul>
-</li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-5-adding-correlations-between-different-demand-types">Model 5. Adding correlations between different demand types</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-6-tackling-chance-constraints-using-saa-in-the-case-of-correlated-demand">Model 6. Tackling chance constraints using SAA in the case of correlated demand</a></li>
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-  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><span class="target" id="index-6"></span><section class="tex2jax_ignore mathjax_ignore" id="airline-seat-allocation-problem">
-<span id="index-7"></span><h1>Airline seat allocation problem<a class="headerlink" href="#airline-seat-allocation-problem" title="Permalink to this heading">#</a></h1>
-<section id="attribution">
-<h2>Attribution<a class="headerlink" href="#attribution" title="Permalink to this heading">#</a></h2>
-<p>The following problem statement is adapted from an exercise and examples presented by Birge and Louveaux (2011).</p>
-<ul class="simple">
-<li><p>Birge, J. R., &amp; Louveaux, F. (2011). Introduction to stochastic programming. Springer Science &amp; Business Media.</p></li>
-</ul>
-<p>The adaptations include a change to parameters for consistency among reformulations of the problem, and additional treatments for sample average approximation (SAA) with chance constraints.</p>
-</section>
-<section id="problem-description">
-<h2>Problem description<a class="headerlink" href="#problem-description" title="Permalink to this heading">#</a></h2>
-<p>An airline is deciding how to partition a new plane for the Amsterdam-Buenos Aires route. This plane can seat 200 economy-class passengers. A section can be created for first-class seats, but each of these seats takes the space of 2 economy-class seats. A business-class section can also be created, but each of these takes the space of 1.5 economy-class seats. The profit for a first-class ticket is three times the profit of an economy ticket, while a business-class ticket has a profit of two times an economy ticket’s profit. Once the plane is partitioned into these seating classes, it cannot be changed.</p>
-<p>The airlines knows that the plane will not always be full in every section. The airline has initially identified three scenarios to consider with about equal frequency:</p>
-<ol class="arabic simple">
-<li><p>Weekday morning and evening traffic;</p></li>
-<li><p>Weekend traffic; and</p></li>
-<li><p>Weekday midday traffic.</p></li>
-</ol>
-<p>Under Scenario 1 the airline thinks they can sell as many as 20 first-class tickets, 50 business-class tickets, and 200 economy tickets. Under Scenario 2 these figures are 10 , 24, and 175, while under Scenario 3, they are 6, 10, and 150, respectively. The following table summarizes the forecast demand for these three scenarios.</p>
-<center>
-<table class="table">
-<thead>
-<tr class="row-odd"><th class="head text-left"><p>Scenario</p></th>
-<th class="head text-center"><p>First-class seats</p></th>
-<th class="head text-center"><p>Business-class seats</p></th>
-<th class="head text-center"><p>Economy-class seats</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td class="text-left"><p>(1) weekday morning and evening</p></td>
-<td class="text-center"><p>20</p></td>
-<td class="text-center"><p>50</p></td>
-<td class="text-center"><p>200</p></td>
-</tr>
-<tr class="row-odd"><td class="text-left"><p>(2) weekend</p></td>
-<td class="text-center"><p>10</p></td>
-<td class="text-center"><p>24</p></td>
-<td class="text-center"><p>175</p></td>
-</tr>
-<tr class="row-even"><td class="text-left"><p>(3) weekday midday</p></td>
-<td class="text-center"><p>6</p></td>
-<td class="text-center"><p>10</p></td>
-<td class="text-center"><p>150</p></td>
-</tr>
-<tr class="row-odd"><td class="text-left"><p><strong>Average Scenario</strong></p></td>
-<td class="text-center"><p><strong>12</strong></p></td>
-<td class="text-center"><p><strong>28</strong></p></td>
-<td class="text-center"><p><strong>175</strong></p></td>
-</tr>
-</tbody>
-</table>
-</center>
-<p>The goal of the airline is to maximize ticket revenue. For marketing purposes, the airline will not sell more tickets than seats in each of the sections (hence no overbooking strategy). We further assume customers seeking a first-class or business-class seat will not downgrade if those seats are unavailable.</p>
-</section>
-<section id="installation-and-imports">
-<h2>Installation and imports<a class="headerlink" href="#installation-and-imports" title="Permalink to this heading">#</a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install pyomo and select solver</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-
-<span class="n">SOLVER</span> <span class="o">=</span> <span class="s2">&quot;cbc&quot;</span>
-
-<span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
-    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>highspy<span class="w"> </span>&gt;/dev/null
-    <span class="n">SOLVER</span> <span class="o">=</span> <span class="s2">&quot;appsi_highs&quot;</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="problem-data">
-<h2>Problem data<a class="headerlink" href="#problem-data" title="Permalink to this heading">#</a></h2>
-<p>Pandas DataFrames and Series are used to encode problem data in the following cell, and to encode problem solutions in subsequent cells.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># scenario data</span>
-<span class="n">demand</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;morning and evening&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">200</span><span class="p">},</span>
-        <span class="s2">&quot;weekend&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">24</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">175</span><span class="p">},</span>
-        <span class="s2">&quot;midday&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">150</span><span class="p">},</span>
-    <span class="p">}</span>
-<span class="p">)</span><span class="o">.</span><span class="n">T</span>
-
-<span class="c1"># global revenue and seat factor data</span>
-<span class="n">capacity</span> <span class="o">=</span> <span class="mi">200</span>
-<span class="n">revenue_factor</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mf">3.0</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
-<span class="n">seat_factor</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mf">1.5</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="analytics">
-<h2>Analytics<a class="headerlink" href="#analytics" title="Permalink to this heading">#</a></h2>
-<p>Prior to optimization, a useful first step is to prepare an analytics function to display performance for any given allocation of seats. The first-stage decision variables are the number of seats allocated for each class <span class="math notranslate nohighlight">\(c\in C\)</span>. We would like to provide a analysis showing the operational consequences for any proposed allocation of seats. For this purpose, we create a function <code class="docutils literal notranslate"><span class="pre">seat_report()</span></code> that show the tickets that can be sold in each scenario, the resulting revenue, and the unsatisfied demand (‘spillage’).</p>
-<p>To establish a basis for analyzing possible solutions to the airline’s problem, this function first is demonstrated for the case where the airplane is configured as entirely economy-class.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># function to report analytics for any given seat allocations</span>
-<span class="k">def</span> <span class="nf">seat_report</span><span class="p">(</span><span class="n">seats</span><span class="p">,</span> <span class="n">demand</span><span class="p">):</span>
-    <span class="n">classes</span> <span class="o">=</span> <span class="n">seats</span><span class="o">.</span><span class="n">index</span>
-
-    <span class="c1"># report seat allocation</span>
-    <span class="n">equivalent_seats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-        <span class="p">{</span>
-            <span class="s2">&quot;seat allocation&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">},</span>
-            <span class="s2">&quot;economy equivalent seat allocation&quot;</span><span class="p">:</span> <span class="p">{</span>
-                <span class="n">c</span><span class="p">:</span> <span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">seat_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span>
-            <span class="p">},</span>
-        <span class="p">}</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">equivalent_seats</span><span class="p">[</span><span class="s2">&quot;TOTAL&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">equivalent_seats</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Seat Allocation&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">equivalent_seats</span><span class="p">)</span>
-
-    <span class="c1"># tickets sold is the minimum of available seats and demand</span>
-    <span class="n">tickets</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
-        <span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="n">demand</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Tickets Sold&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">tickets</span><span class="p">)</span>
-
-    <span class="c1"># seats unsold</span>
-    <span class="n">unsold</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
-        <span class="n">unsold</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Seats not Sold&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">unsold</span><span class="p">)</span>
-
-    <span class="c1"># spillage (unmet demand)</span>
-    <span class="n">spillage</span> <span class="o">=</span> <span class="n">demand</span> <span class="o">-</span> <span class="n">tickets</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Spillage (Unfulfilled Demand)&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">spillage</span><span class="p">)</span>
-
-    <span class="c1"># compute revenue</span>
-    <span class="n">revenue</span> <span class="o">=</span> <span class="n">tickets</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">revenue_factor</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span>
-        <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Expected Revenue (in units of economy ticket price): </span><span class="si">{</span><span class="n">revenue</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
-    <span class="p">)</span>
-
-    <span class="c1"># charts</span>
-    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
-    <span class="n">revenue</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;barh&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Revenue by Scenario&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">revenue</span><span class="o">.</span><span class="n">mean</span><span class="p">()]</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">(),</span> <span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mf">1.4</span><span class="p">)</span>
-    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Revenue&quot;</span><span class="p">)</span>
-
-    <span class="n">tickets</span><span class="p">[</span><span class="n">classes</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-        <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;barh&quot;</span><span class="p">,</span> <span class="n">rot</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stacked</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Demand Scenarios&quot;</span>
-    <span class="p">)</span>
-    <span class="n">demand</span><span class="p">[</span><span class="n">classes</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-        <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-        <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;barh&quot;</span><span class="p">,</span>
-        <span class="n">rot</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="n">stacked</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Demand Scenarios&quot;</span><span class="p">,</span>
-        <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]]</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">(),</span> <span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mf">1.4</span><span class="p">)</span>
-    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Seats&quot;</span><span class="p">)</span>
-    <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-
-    <span class="k">return</span>
-
-
-<span class="c1"># a trial solution</span>
-<span class="n">seats_all_economy</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">200</span><span class="p">})</span>
-<span class="n">seat_report</span><span class="p">(</span><span class="n">seats_all_economy</span><span class="p">,</span> <span class="n">demand</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seat Allocation
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-      <th>TOTAL</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>seat allocation</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>200.0</td>
-      <td>200.0</td>
-    </tr>
-    <tr>
-      <th>economy equivalent seat allocation</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>200.0</td>
-      <td>200.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Tickets Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>0</td>
-      <td>0</td>
-      <td>200</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>0</td>
-      <td>0</td>
-      <td>175</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>0</td>
-      <td>0</td>
-      <td>150</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seats not Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>0</td>
-      <td>0</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>0</td>
-      <td>0</td>
-      <td>25</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>0</td>
-      <td>0</td>
-      <td>50</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Spillage (Unfulfilled Demand)
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>20</td>
-      <td>50</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>10</td>
-      <td>24</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>6</td>
-      <td>10</td>
-      <td>0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Expected Revenue (in units of economy ticket price): 175.00
-</pre></div>
-</div>
-<img alt="../../_images/aec05e4113b7e50cb309d28650d7eb0d56ee41906dc25b4d0a38345e96322db4.png" src="../../_images/aec05e4113b7e50cb309d28650d7eb0d56ee41906dc25b4d0a38345e96322db4.png" />
-</div>
-</div>
-</section>
-<section id="model-1-deterministic-solution-for-the-average-demand-scenario">
-<h2>Model 1. Deterministic solution for the average demand scenario<a class="headerlink" href="#model-1-deterministic-solution-for-the-average-demand-scenario" title="Permalink to this heading">#</a></h2>
-<p>A common starting point in stochastic optimization is to solve the deterministic problem where future demands are fixed at their mean values and compute the corresponding optimal solution. The resulting value of the objective has been called the <em>expectation of the expected value problem (EEV)</em> by Birge, or the <em>expected value of the mean (EVM)</em> solution by others.</p>
-<p>Let us introduce the set <span class="math notranslate nohighlight">\(C\)</span> of the three possible classes, i.e., <span class="math notranslate nohighlight">\(C=\{F,B,E\}\)</span>. The objective function is to maximize ticket revenue.</p>
-<div class="math notranslate nohighlight">
-\[
-\max_{s_c, t_c} \quad \sum_{c\in C} r_c t_c 
-\]</div>
-<p>where <span class="math notranslate nohighlight">\(r_c\)</span> is the revenue from selling a ticket for a seat in class <span class="math notranslate nohighlight">\(c\in C\)</span>.</p>
-<p>Let <span class="math notranslate nohighlight">\(s_c\)</span> denote the number of seats of class <span class="math notranslate nohighlight">\(c \in C\)</span> installed in the new plane. Let <span class="math notranslate nohighlight">\(f_c\)</span> be the scale factor denoting the number of economy seats displaced by one seat in class <span class="math notranslate nohighlight">\(c \in C\)</span>. Then, since there is a total of 200 economy-class seats that could fit on the plane, the capacity constraint reads as</p>
-<div class="math notranslate nohighlight">
-\[
-    \sum_{c\in C} f_c s_c \leq 200,
-\]</div>
-<p>Let <span class="math notranslate nohighlight">\(\mu_c\)</span> be the mean demand for seats of class <span class="math notranslate nohighlight">\(c\in C\)</span>, and let <span class="math notranslate nohighlight">\(t_c\)</span> be the number of tickets of class <span class="math notranslate nohighlight">\(c\in C\)</span> that are sold. To ensure we do not sell more tickets than available seats nor more than demand, we need to add two more constraints:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}
-\begin{align*}
-    t_c &amp; \leq s_c \qquad \forall \, c\in C \\
-    t_c &amp; \leq \mu_c \qquad \forall \, c\in C
-\end{align*}
-\end{split}\]</div>
-<p>Lastly, both ticket and seat variables need to be nonnegative integers, so we add the constraints <span class="math notranslate nohighlight">\(\bm{t}, \bm{s} \in \mathbb{Z}_+\)</span>.</p>
-<p>The following cell presents a Pyomo model implementing this model.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">airline_deterministic</span><span class="p">(</span><span class="n">demand</span><span class="p">):</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Airline seating (deterministic)&quot;</span><span class="p">)</span>
-
-    <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-
-    <span class="c1"># first stage variables and constraints</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">seats</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">plane_seats</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">seat_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">capacity</span>
-
-    <span class="c1"># second stage variable and constraints</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">tickets</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">demand_limits</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">demand</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">seat_limits</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-
-    <span class="c1"># objective</span>
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">revenue</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">revenue_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">m</span>
-
-
-<span class="k">def</span> <span class="nf">airline_solve</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-    <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="n">c</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">})</span>
-
-
-<span class="c1"># Solve deterministic model to obtain the expectation of the expected value problem (EEV)</span>
-<span class="n">model_eev</span> <span class="o">=</span> <span class="n">airline_deterministic</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span>
-<span class="n">seats_eev</span> <span class="o">=</span> <span class="n">airline_solve</span><span class="p">(</span><span class="n">model_eev</span><span class="p">)</span>
-<span class="n">seat_report</span><span class="p">(</span><span class="n">seats_eev</span><span class="p">,</span> <span class="n">demand</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seat Allocation
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-      <th>TOTAL</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>seat allocation</th>
-      <td>12.0</td>
-      <td>28.0</td>
-      <td>134.0</td>
-      <td>174.0</td>
-    </tr>
-    <tr>
-      <th>economy equivalent seat allocation</th>
-      <td>24.0</td>
-      <td>42.0</td>
-      <td>134.0</td>
-      <td>200.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Tickets Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
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-    }
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-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>12.0</td>
-      <td>28.0</td>
-      <td>134.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>10.0</td>
-      <td>24.0</td>
-      <td>134.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>6.0</td>
-      <td>10.0</td>
-      <td>134.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seats not Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>2.0</td>
-      <td>4.0</td>
-      <td>0.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>6.0</td>
-      <td>18.0</td>
-      <td>0.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Spillage (Unfulfilled Demand)
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>8.0</td>
-      <td>22.0</td>
-      <td>66.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>41.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>16.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Expected Revenue (in units of economy ticket price): 203.33
-</pre></div>
-</div>
-<img alt="../../_images/dd9444138203cd364755cc3cf7e85c0058b4a1258ee3a0cd5fb1afdcf95bc9ed.png" src="../../_images/dd9444138203cd364755cc3cf7e85c0058b4a1258ee3a0cd5fb1afdcf95bc9ed.png" />
-</div>
-</div>
-</section>
-<section id="model-2-two-stage-stochastic-optimization-and-its-extensive-form">
-<h2>Model 2. Two-stage stochastic optimization and its extensive form<a class="headerlink" href="#model-2-two-stage-stochastic-optimization-and-its-extensive-form" title="Permalink to this heading">#</a></h2>
-<p>If we assume demand is not certain, we can formulate a two-stage stochastic optimization problem. The first-stage or here-and-now variables are the <span class="math notranslate nohighlight">\(s_c\)</span>’s, those related to the seat allocations. Due to their dependence on the realized demand <span class="math notranslate nohighlight">\(\boldsymbol{z}\)</span>, the variables <span class="math notranslate nohighlight">\(t_c\)</span>’s describing the number of tickets sold, are second-stage or recourse decision variables. The full problem formulation is as follows: the first stage problem is</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}
-\begin{align*}
-    \max \quad &amp; \mathbb E_{z} Q(\boldsymbol{s},\boldsymbol{z}) \\
-    \text{s.t.} \quad &amp; \sum_{c\in C} f_c s_c \leq 200,\\
-    &amp; \boldsymbol{s} \in \mathbb{Z}_+,
-\end{align*}
-\end{split}\]</div>
-<p>where <span class="math notranslate nohighlight">\(Q(\boldsymbol{s},\boldsymbol{z})\)</span> is the value of the second-stage problem, defined as</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}
-\begin{align*}
-    Q(\boldsymbol{s},\boldsymbol{z}) := \max \quad &amp; \sum_{c\in C} r_c t_{c}\\
-    \text{s.t.} \quad 
-    &amp; t_c \leq s_c &amp; \forall \, c\in C \\
-    &amp; t_c \leq z_c &amp; \forall \, c\in C \\
-    &amp; \boldsymbol{t} \in \mathbb{Z}_+.
-\end{align*}
-\end{split}\]</div>
-<p>In view of the assumption that there is only a finite number <span class="math notranslate nohighlight">\(N=|S|\)</span> of scenarios for ticket demand, we can write the extensive form of the two-stage stochastic optimization problem above and solve it exactly. To do so, we modify the second-stage variables <span class="math notranslate nohighlight">\(t_{c,s}\)</span> so that they are indexed by both class <span class="math notranslate nohighlight">\(c\)</span> and scenario <span class="math notranslate nohighlight">\(s\)</span>. The expectation can thus be replaced with the average revenue over the <span class="math notranslate nohighlight">\(N\)</span> scenarios, that is</p>
-<div class="math notranslate nohighlight">
-\[
-\max \quad \sum_{s \in S} \frac{1}{N} \sum_{c\in C}  r_c t_{c, s},
-\]</div>
-<p>where the fraction <span class="math notranslate nohighlight">\(\frac{1}{N}\)</span> appears since we assume that all <span class="math notranslate nohighlight">\(N\)</span> scenarios are equally likely. The first stage constraint remains unchanged, while the second stage constraints are tuplicated for each scenario <span class="math notranslate nohighlight">\(s \in S\)</span>, namely</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}
-\begin{align*}
-t_{c,s} &amp; \leq s_c &amp; \forall \, c\in C \\
-t_{c,s} &amp; \leq z_{c, s} &amp; \forall \, (c, s) \in C \times S
-\end{align*}
-\end{split}\]</div>
-<p>The following cell presents a Pyomo model implementing this model and solving it for the <span class="math notranslate nohighlight">\(N=3\)</span> equiprobable scenarios introduced above.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">airline_stochastic</span><span class="p">(</span><span class="n">demand</span><span class="p">):</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Airline seating (stochastic)&quot;</span><span class="p">)</span>
-
-    <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">demand</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
-
-    <span class="c1"># first stage variables and constraints</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">seats</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">plane_seats</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">seat_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">capacity</span>
-
-    <span class="c1"># second stage variable and constraints</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">tickets</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">demand_limits</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">demand</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">s</span><span class="p">]</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">seat_limits</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-
-    <span class="c1"># objective</span>
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">revenue</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span>
-            <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">revenue_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span>
-            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">m</span>
-
-
-<span class="c1"># create and solve model for the three scenarios defined above</span>
-<span class="n">model_stochastic</span> <span class="o">=</span> <span class="n">airline_stochastic</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span>
-<span class="n">seats_stochastic</span> <span class="o">=</span> <span class="n">airline_solve</span><span class="p">(</span><span class="n">model_stochastic</span><span class="p">)</span>
-<span class="n">seat_report</span><span class="p">(</span><span class="n">seats_stochastic</span><span class="p">,</span> <span class="n">demand</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seat Allocation
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-      <th>TOTAL</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>seat allocation</th>
-      <td>10.0</td>
-      <td>20.0</td>
-      <td>150.0</td>
-      <td>180.0</td>
-    </tr>
-    <tr>
-      <th>economy equivalent seat allocation</th>
-      <td>20.0</td>
-      <td>30.0</td>
-      <td>150.0</td>
-      <td>200.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Tickets Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>10.0</td>
-      <td>20.0</td>
-      <td>150.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>10.0</td>
-      <td>20.0</td>
-      <td>150.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>6.0</td>
-      <td>10.0</td>
-      <td>150.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seats not Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>4.0</td>
-      <td>10.0</td>
-      <td>0.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Spillage (Unfulfilled Demand)
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
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-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>10.0</td>
-      <td>30.0</td>
-      <td>50.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>0.0</td>
-      <td>4.0</td>
-      <td>25.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Expected Revenue (in units of economy ticket price): 209.33
-</pre></div>
-</div>
-<img alt="../../_images/6d717b9c0df99f8d8522c0745e2d99097805c028a954604c6b1efb25fd5a807d.png" src="../../_images/6d717b9c0df99f8d8522c0745e2d99097805c028a954604c6b1efb25fd5a807d.png" />
-</div>
-</div>
-</section>
-<section id="model-3-adding-chance-constraints">
-<h2>Model 3. Adding chance constraints<a class="headerlink" href="#model-3-adding-chance-constraints" title="Permalink to this heading">#</a></h2>
-<p>The airline wishes a special guarantee for its clients enrolled in its loyalty program. In particular, it wants <span class="math notranslate nohighlight">\(98\%\)</span> probability to cover the demand of first-class seats and <span class="math notranslate nohighlight">\(95\%\)</span> probability to cover the demand of business-class seats (by clients of the loyalty program). First-class passengers are covered if they can purchase a first-class seat. Business-class passengers are covered if they purchase a business-class seat or upgrade to a first-class seat.</p>
-<p>Assume the demand of loyalty-program passengers is normally distributed as <span class="math notranslate nohighlight">\(z_F \sim \mathcal N(\mu_F, \sigma_F^2)\)</span> and <span class="math notranslate nohighlight">\(z_B \sim \mathcal N(\mu_B, \sigma_B^2)\)</span> for first-class and business, respectively, where the parameters are given in the table below. For completeness, we also include the parameters for economy-class passengers.</p>
-<center>
-<table class="table">
-<thead>
-<tr class="row-odd"><th class="head text-center"><p></p></th>
-<th class="head text-center"><p><span class="math notranslate nohighlight">\(\mu_{\cdot}\)</span></p></th>
-<th class="head text-center"><p><span class="math notranslate nohighlight">\(\sigma_{\cdot}\)</span></p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td class="text-center"><p>F</p></td>
-<td class="text-center"><p>12</p></td>
-<td class="text-center"><p>4</p></td>
-</tr>
-<tr class="row-odd"><td class="text-center"><p>B</p></td>
-<td class="text-center"><p>28</p></td>
-<td class="text-center"><p>8</p></td>
-</tr>
-<tr class="row-even"><td class="text-center"><p>E</p></td>
-<td class="text-center"><p>175</p></td>
-<td class="text-center"><p>20</p></td>
-</tr>
-</tbody>
-</table>
-</center>
-<p>We further assume that the demands for first-class and business-class seats are independent of each other and of the scenario (time of the week).</p>
-<p>Let <span class="math notranslate nohighlight">\(s_F\)</span> be the number of first-class seats and <span class="math notranslate nohighlight">\(s_B\)</span> the number of business seats. The probabilistic constraints are</p>
-<div class="math notranslate nohighlight">
-\[
-\mathbb P(s_F \geq z_F ) \geq 0.98, \qquad \text{ and } \qquad \mathbb P(s_F + s_B \geq z_F + z_B ) \geq 0.95.
-\]</div>
-<p>These are can be written equivalently as linear constraints, specifically</p>
-<div class="math notranslate nohighlight">
-\[
-\frac{s_F - \mu_F}{\sigma_F} \geq \Phi^{-1}(0.98) \approx 2.054 \qquad \text{ and } \qquad \frac{(s_F + s_B) - (\mu_F + \mu_B)}{\sqrt{\sigma_F^2 + \sigma_B^2}} \geq \Phi^{-1}(0.95) \approx 1.645.
-\]</div>
-<p>For the second constraint, we use the fact that the sum of the two independent random variables <span class="math notranslate nohighlight">\(z_F\)</span> and <span class="math notranslate nohighlight">\(z_B\)</span> is normally distributed with mean <span class="math notranslate nohighlight">\(\mu_F + \mu_B\)</span> and variance <span class="math notranslate nohighlight">\(\sigma_F^2 + \sigma_B^2\)</span>.</p>
-<p>We add these equivalent linear counterparts of the two chance constraints to the stochastic optimization model. Rather than writing a function to create a whole new model, we can use the prior function to create and add the two chance constraints using decorators.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import the scipy.stats.norm object needed to use the quantile function of the standard normal distribution</span>
-<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
-
-<span class="c1"># define the mean and standard deviation of the demand for each class</span>
-<span class="n">mu</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="n">sigma</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">16</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}</span>
-<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;mu&quot;</span><span class="p">:</span> <span class="n">mu</span><span class="p">,</span> <span class="s2">&quot;sigma&quot;</span><span class="p">:</span> <span class="n">sigma</span><span class="p">}))</span>
-
-
-<span class="c1"># create a new model with chance constraints that takes as input also</span>
-<span class="c1"># the target quality of service (QoS) levels for classes F and B</span>
-<span class="k">def</span> <span class="nf">airline_cc</span><span class="p">(</span><span class="n">demand</span><span class="p">,</span> <span class="n">QoSF</span><span class="o">=</span><span class="mf">0.98</span><span class="p">,</span> <span class="n">QoSFB</span><span class="o">=</span><span class="mf">0.95</span><span class="p">):</span>
-    <span class="c1"># create two-stage stochastic model as before</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">airline_stochastic</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span>
-
-    <span class="c1"># add equivalent counterparts of the two chance constraints to the first stage problem</span>
-    <span class="c1"># the two coefficients related the inverse CDF of the standard normal are computed using the norm.ppf function</span>
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
-    <span class="k">def</span> <span class="nf">first_class</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">])</span> <span class="o">&gt;=</span> <span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">QoSF</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
-    <span class="k">def</span> <span class="nf">business_class</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">mu</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">mu</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">])</span> <span class="o">&gt;=</span> <span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span>
-            <span class="n">QoSFB</span>
-        <span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">sigma</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">sigma</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">m</span>
-
-
-<span class="c1"># create and solve model</span>
-<span class="n">model_cc</span> <span class="o">=</span> <span class="n">airline_cc</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span>
-<span class="n">seats_cc</span> <span class="o">=</span> <span class="n">airline_solve</span><span class="p">(</span><span class="n">model_cc</span><span class="p">)</span>
-<span class="n">seat_report</span><span class="p">(</span><span class="n">seats_cc</span><span class="p">,</span> <span class="n">demand</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>mu</th>
-      <th>sigma</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>12.0</td>
-      <td>4</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>28.0</td>
-      <td>16</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>175.0</td>
-      <td>20</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seat Allocation
-</pre></div>
-</div>
-<div class="output text_html"><div>
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-      <th>TOTAL</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>seat allocation</th>
-      <td>21.0</td>
-      <td>47.0</td>
-      <td>87.0</td>
-      <td>155.0</td>
-    </tr>
-    <tr>
-      <th>economy equivalent seat allocation</th>
-      <td>42.0</td>
-      <td>70.5</td>
-      <td>87.0</td>
-      <td>199.5</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Tickets Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>20.0</td>
-      <td>47.0</td>
-      <td>87.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>10.0</td>
-      <td>24.0</td>
-      <td>87.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>6.0</td>
-      <td>10.0</td>
-      <td>87.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seats not Sold
-</pre></div>
-</div>
-<div class="output text_html"><div>
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-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>1.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>11.0</td>
-      <td>23.0</td>
-      <td>0.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>15.0</td>
-      <td>37.0</td>
-      <td>0.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Spillage (Unfulfilled Demand)
-</pre></div>
-</div>
-<div class="output text_html"><div>
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-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>morning and evening</th>
-      <td>0.0</td>
-      <td>3.0</td>
-      <td>113.0</td>
-    </tr>
-    <tr>
-      <th>weekend</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>88.0</td>
-    </tr>
-    <tr>
-      <th>midday</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>63.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Expected Revenue (in units of economy ticket price): 177.00
-</pre></div>
-</div>
-<img alt="../../_images/8e7c42c6f90ca7f835e4e98f0bca53dedff4b75d713ce9700847c711f9a92a46.png" src="../../_images/8e7c42c6f90ca7f835e4e98f0bca53dedff4b75d713ce9700847c711f9a92a46.png" />
-</div>
-</div>
-</section>
-<section id="model-4-solving-the-case-of-continuous-demand-distributions-using-the-saa-method">
-<h2>Model 4. Solving the case of continuous demand distributions using the SAA method<a class="headerlink" href="#model-4-solving-the-case-of-continuous-demand-distributions-using-the-saa-method" title="Permalink to this heading">#</a></h2>
-<p>Let us now move past the simplifying assumption that there are only three equally likely scenarios and consider the case where the demand is described by a random vector <span class="math notranslate nohighlight">\((z_F, z_B, z_E)\)</span>, where <span class="math notranslate nohighlight">\(z_c\)</span> is the demand for seats of class <span class="math notranslate nohighlight">\(c\in C\)</span>. The demand for class <span class="math notranslate nohighlight">\(c\)</span> is assumed to be independent and normally distributed with mean <span class="math notranslate nohighlight">\(\mu_c\)</span> and variance <span class="math notranslate nohighlight">\(\sigma_c^2\)</span> as reported in the following table</p>
-<center>
-<table class="table">
-<thead>
-<tr class="row-odd"><th class="head text-center"><p></p></th>
-<th class="head text-center"><p><span class="math notranslate nohighlight">\(\mu\)</span></p></th>
-<th class="head text-center"><p><span class="math notranslate nohighlight">\(\sigma\)</span></p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td class="text-center"><p>F</p></td>
-<td class="text-center"><p>12</p></td>
-<td class="text-center"><p>4</p></td>
-</tr>
-<tr class="row-odd"><td class="text-center"><p>B</p></td>
-<td class="text-center"><p>28</p></td>
-<td class="text-center"><p>8</p></td>
-</tr>
-<tr class="row-even"><td class="text-center"><p>E</p></td>
-<td class="text-center"><p>175</p></td>
-<td class="text-center"><p>20</p></td>
-</tr>
-</tbody>
-</table>
-</center>
-<p>Note that we model the demand for each class using a continuous random variable, which is a simplification of the real world, where the ticket demand is always a discrete nonnegative number. Therefore, we round down all the obtained random numbers.</p>
-<p>However, now that the number of scenarios is not finite anymore, we cannot solve the problem exactly. Instead, we can use the SAA method to approximate the expected value appearing in the objective function. The first step of the SAA is to generate a collection of <span class="math notranslate nohighlight">\(N\)</span> scenarios <span class="math notranslate nohighlight">\((z_{F,s}, z_{B,s}, z_{E,s})\)</span> for <span class="math notranslate nohighlight">\(s=1,\ldots,N\)</span>. We can do this by sampling from the normal distributions with the given means and variances.</p>
-<p>For sake of generality, we create a script to generate scenarios in which the three demands have a general correlation structure captured by a correlation matrix <span class="math notranslate nohighlight">\(\bm{\rho}\)</span>.</p>
-<section id="scenario-generation-uncorrelated-case">
-<h3>Scenario generation (uncorrelated case)<a class="headerlink" href="#scenario-generation-uncorrelated-case" title="Permalink to this heading">#</a></h3>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># sample size</span>
-<span class="n">N</span> <span class="o">=</span> <span class="mi">1000</span>
-
-<span class="c1"># define the mean mu and standard deviation sigma of the demand for each class</span>
-<span class="n">mu</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="n">sigma</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">16</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}</span>
-<span class="n">classes</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span>
-
-<span class="c1"># build covariance matrix from covariances and correlations</span>
-<span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-<span class="n">S</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Model Covariance&quot;</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">S</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">classes</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">classes</span><span class="p">)</span>
-<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-
-<span class="c1"># generate N samples, round each demand entry to nearest integer, and correct non-negative values</span>
-<span class="n">seed</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-<span class="n">samples</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">mu</span><span class="p">),</span> <span class="n">S</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">()</span>
-<span class="n">demand_saa</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">classes</span><span class="p">)</span>
-<span class="n">demand_saa</span><span class="p">[</span><span class="n">demand_saa</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-
-<span class="c1"># report sample means and standard deviations for each class</span>
-<span class="n">demand_saa_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;mu (mean)&quot;</span><span class="p">:</span> <span class="n">mu</span><span class="p">,</span>
-        <span class="s2">&quot;sample mean&quot;</span><span class="p">:</span> <span class="n">demand_saa</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
-        <span class="s2">&quot;sigma (std)&quot;</span><span class="p">:</span> <span class="n">sigma</span><span class="p">,</span>
-        <span class="s2">&quot;sample std&quot;</span><span class="p">:</span> <span class="n">demand_saa</span><span class="o">.</span><span class="n">std</span><span class="p">(),</span>
-    <span class="p">}</span>
-<span class="p">)</span>
-<span class="n">display</span><span class="p">(</span><span class="n">demand_saa_stats</span><span class="p">)</span>
-
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ci</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">classes</span><span class="p">):</span>
-    <span class="n">demand_saa</span><span class="p">[</span><span class="n">ci</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Histogram demand class </span><span class="si">{</span><span class="n">ci</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Model Covariance
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>16</td>
-      <td>0</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>0</td>
-      <td>256</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>0</td>
-      <td>0</td>
-      <td>400</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>mu (mean)</th>
-      <th>sample mean</th>
-      <th>sigma (std)</th>
-      <th>sample std</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>12.0</td>
-      <td>11.889</td>
-      <td>4</td>
-      <td>4.088936</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>28.0</td>
-      <td>28.626</td>
-      <td>16</td>
-      <td>15.051534</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>175.0</td>
-      <td>172.966</td>
-      <td>20</td>
-      <td>19.839669</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><img alt="../../_images/41168d302d65513a23b33648085b60ecd1e2197c03f5caffca7b217c055890a0.png" src="../../_images/41168d302d65513a23b33648085b60ecd1e2197c03f5caffca7b217c055890a0.png" />
-</div>
-</div>
-<p>We also introduce a new function to report the solution and its features in this more general continuous demand case.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># function to report analytics for SAA case</span>
-<span class="k">def</span> <span class="nf">seat_report_saa</span><span class="p">(</span><span class="n">seats</span><span class="p">,</span> <span class="n">demand</span><span class="p">):</span>
-    <span class="n">classes</span> <span class="o">=</span> <span class="n">seats</span><span class="o">.</span><span class="n">index</span>
-
-    <span class="c1"># report seat allocation</span>
-    <span class="n">equivalent_seats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-        <span class="p">{</span>
-            <span class="s2">&quot;seat allocation&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">},</span>
-            <span class="s2">&quot;economy equivalent seat allocation&quot;</span><span class="p">:</span> <span class="p">{</span>
-                <span class="n">c</span><span class="p">:</span> <span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">seat_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span>
-            <span class="p">},</span>
-        <span class="p">}</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">equivalent_seats</span><span class="p">[</span><span class="s2">&quot;TOTAL&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">equivalent_seats</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Seat Allocation&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">equivalent_seats</span><span class="p">)</span>
-
-    <span class="c1"># tickets sold</span>
-    <span class="n">tickets</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
-        <span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="n">demand</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Mean Tickets Sold&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">tickets</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-
-    <span class="c1"># seats unsold</span>
-    <span class="n">unsold</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
-        <span class="n">unsold</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Mean Seats not Sold&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">unsold</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-
-    <span class="c1"># spillage (unmet demand)</span>
-    <span class="n">spillage</span> <span class="o">=</span> <span class="n">demand</span> <span class="o">-</span> <span class="n">tickets</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Mean Spillage (Unfulfilled Demand)&quot;</span><span class="p">)</span>
-    <span class="n">display</span><span class="p">(</span><span class="n">spillage</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-
-    <span class="c1"># compute revenue</span>
-    <span class="n">revenue</span> <span class="o">=</span> <span class="n">tickets</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">revenue_factor</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span>
-        <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Expected Revenue (in units of economy ticket price): </span><span class="si">{</span><span class="n">revenue</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
-    <span class="p">)</span>
-
-    <span class="c1"># charts</span>
-    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-    <span class="n">revenue</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Revenue Histogram&quot;</span><span class="p">)</span>
-
-    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">classes</span><span class="p">):</span>
-        <span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
-        <span class="n">demand</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Tickets Sold&quot;</span><span class="p">,</span> <span class="s2">&quot;Demand&quot;</span><span class="p">])</span>
-        <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
-
-    <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-    <span class="k">return</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>We can now solve the stochastic optimization problem using the generated <span class="math notranslate nohighlight">\(N=1000\)</span> scenarios. Note that we can use the previously defined function <code class="docutils literal notranslate"><span class="pre">airline_stochastic</span></code> to solve the model by simply calling it with a different dataframe as argument.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_ssa</span> <span class="o">=</span> <span class="n">airline_stochastic</span><span class="p">(</span><span class="n">demand_saa</span><span class="p">)</span>
-<span class="n">seats_saa</span> <span class="o">=</span> <span class="n">airline_solve</span><span class="p">(</span><span class="n">model_ssa</span><span class="p">)</span>
-<span class="n">seat_report_saa</span><span class="p">(</span><span class="n">seats_saa</span><span class="p">,</span> <span class="n">demand_saa</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seat Allocation
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-      <th>TOTAL</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>seat allocation</th>
-      <td>11.0</td>
-      <td>20.0</td>
-      <td>148.0</td>
-      <td>179.0</td>
-    </tr>
-    <tr>
-      <th>economy equivalent seat allocation</th>
-      <td>22.0</td>
-      <td>30.0</td>
-      <td>148.0</td>
-      <td>200.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Tickets Sold
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F      9.764
-B     17.416
-E    147.056
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Seats not Sold
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F    1.236
-B    2.584
-E    0.944
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Spillage (Unfulfilled Demand)
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F     2.125
-B    11.210
-E    25.910
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Expected Revenue (in units of economy ticket price): 211.18
-</pre></div>
-</div>
-<img alt="../../_images/2ea558f42b3d4c8ba2ab1c80d56cc99b1ea2b54ce303557c4c8f64d06065de6f.png" src="../../_images/2ea558f42b3d4c8ba2ab1c80d56cc99b1ea2b54ce303557c4c8f64d06065de6f.png" />
-<img alt="../../_images/69b9adbfe76df9e2076dfe66fede2b5ead8aa814aa83c9f427f28965183015e6.png" src="../../_images/69b9adbfe76df9e2076dfe66fede2b5ead8aa814aa83c9f427f28965183015e6.png" />
-</div>
-</div>
-</section>
-</section>
-<section id="model-5-adding-correlations-between-different-demand-types">
-<h2>Model 5. Adding correlations between different demand types<a class="headerlink" href="#model-5-adding-correlations-between-different-demand-types" title="Permalink to this heading">#</a></h2>
-<p>Now assume the ticket demand for the three categories is captured by a <span class="math notranslate nohighlight">\(3\)</span>-dimensional multivariate normal distribution mean <span class="math notranslate nohighlight">\(\mu=(\mu_F, \mu_B, \mu_E)\)</span>, variances <span class="math notranslate nohighlight">\((\sigma_F^2, \sigma_B^2, \sigma_E^2)\)</span> and a symmetric correlation matrix</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}
-P = \left(
-\begin{array}{ccc}
-1 &amp; \rho_{FB} &amp; \rho_{FE} \\
-\rho_{BF} &amp; 1 &amp; \rho_{BE}\\
-\rho_{EF} &amp; \rho_{EB} &amp; 1 \\
-\end{array}
-\right)
-\end{split}\]</div>
-<p>The covariance matrix is given by <span class="math notranslate nohighlight">\(\Sigma = \text{diag}(\sigma)\ P\ \text{diag}(\sigma)\)</span> or</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}
-\Sigma= \left(
-\begin{array}{ccc}
- \sigma_F^2 &amp; \rho_{FB}\sigma_F\sigma_B &amp; \rho_{FE}\sigma_F\sigma_E \\
-\rho_{BF}\sigma_B\sigma_F &amp; \sigma_B^2 &amp; \rho_{BE}\sigma_B\sigma_E\\
-\rho_{EF}\sigma_E\sigma_F &amp; \rho_{EB}\sigma_E\sigma_B &amp; \sigma_E^2 \\
-\end{array}
-\right)
-\end{split}\]</div>
-<p>We assume <span class="math notranslate nohighlight">\(\rho_{FB} = 0.6\)</span>, <span class="math notranslate nohighlight">\(\rho_{BE} = 0.4\)</span> and <span class="math notranslate nohighlight">\(\rho_{FE} = 0.2\)</span>.</p>
-<p>We now sample <span class="math notranslate nohighlight">\(N=1000\)</span> scenarios from this multivariate correlated normal distribution and use the SAA method to approximate the solution of the two-stage stochastic optimization problem.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># sample size</span>
-<span class="n">N</span> <span class="o">=</span> <span class="mi">1000</span>
-
-<span class="c1"># define the mean mu and standard deviation sigma of the demand for each class</span>
-<span class="n">mu</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="n">sigma</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="mi">16</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}</span>
-<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;mu&quot;</span><span class="p">:</span> <span class="n">mu</span><span class="p">,</span> <span class="s2">&quot;sigma&quot;</span><span class="p">:</span> <span class="n">sigma</span><span class="p">}))</span>
-
-<span class="c1"># correlation matrix</span>
-<span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
-
-<span class="c1"># build covariance matrix from covariances and correlations</span>
-<span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-<span class="n">S</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">@</span> <span class="n">P</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-
-<span class="c1"># create samples</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">mu</span><span class="p">),</span> <span class="n">S</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">()</span>
-
-<span class="c1"># truncate to integers and non-negative values</span>
-<span class="n">classes</span> <span class="o">=</span> <span class="n">demand</span><span class="o">.</span><span class="n">columns</span>
-<span class="n">demand_saa</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">classes</span><span class="p">)</span>
-<span class="n">demand_saa</span><span class="p">[</span><span class="n">demand_saa</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;mu&quot;</span><span class="p">])</span>
-<span class="n">df</span><span class="p">[</span><span class="s2">&quot;sample means&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">demand_saa</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">sigma</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;sigma&quot;</span><span class="p">])</span>
-<span class="n">df</span><span class="p">[</span><span class="s2">&quot;sample std dev&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">demand_saa</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
-<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Model Covariance&quot;</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">S</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">classes</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">classes</span><span class="p">)</span>
-<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Sample Covariance&quot;</span><span class="p">)</span>
-<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">demand_saa</span><span class="o">.</span><span class="n">cov</span><span class="p">()))</span>
-
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ci</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">classes</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">cj</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">classes</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">:</span>
-            <span class="n">demand_saa</span><span class="p">[</span><span class="n">ci</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Histogram </span><span class="si">{</span><span class="n">ci</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">demand_saa</span><span class="p">[</span><span class="n">ci</span><span class="p">],</span> <span class="n">demand_saa</span><span class="p">[</span><span class="n">cj</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">cj</span><span class="p">)</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Convariance: </span><span class="si">{</span><span class="n">ci</span><span class="si">}</span><span class="s2"> vs </span><span class="si">{</span><span class="n">cj</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>mu</th>
-      <th>sigma</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>12.0</td>
-      <td>4</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>28.0</td>
-      <td>16</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>175.0</td>
-      <td>20</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
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-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>mu</th>
-      <th>sample means</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>12.0</td>
-      <td>12.012</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>28.0</td>
-      <td>28.590</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>175.0</td>
-      <td>175.301</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
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-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>sigma</th>
-      <th>sample std dev</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>4</td>
-      <td>3.951388</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>16</td>
-      <td>15.139984</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>20</td>
-      <td>20.147365</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Model Covariance
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
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-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>16.0</td>
-      <td>38.4</td>
-      <td>16.0</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>38.4</td>
-      <td>256.0</td>
-      <td>128.0</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>16.0</td>
-      <td>128.0</td>
-      <td>400.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Sample Covariance
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
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-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>F</th>
-      <td>15.613469</td>
-      <td>36.044965</td>
-      <td>12.434823</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>36.044965</td>
-      <td>229.219119</td>
-      <td>107.216627</td>
-    </tr>
-    <tr>
-      <th>E</th>
-      <td>12.434823</td>
-      <td>107.216627</td>
-      <td>405.916315</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><img alt="../../_images/133ae3d32ff19c43e68cb11078dd7c96753e835aecc3877520692c5b12a62e12.png" src="../../_images/133ae3d32ff19c43e68cb11078dd7c96753e835aecc3877520692c5b12a62e12.png" />
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_ssa</span> <span class="o">=</span> <span class="n">airline_stochastic</span><span class="p">(</span><span class="n">demand_saa</span><span class="p">)</span>
-<span class="n">seats_saa</span> <span class="o">=</span> <span class="n">airline_solve</span><span class="p">(</span><span class="n">model_ssa</span><span class="p">)</span>
-<span class="n">seat_report_saa</span><span class="p">(</span><span class="n">seats_saa</span><span class="p">,</span> <span class="n">demand_saa</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seat Allocation
-</pre></div>
-</div>
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-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-      <th>TOTAL</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>seat allocation</th>
-      <td>11.0</td>
-      <td>20.0</td>
-      <td>148.0</td>
-      <td>179.0</td>
-    </tr>
-    <tr>
-      <th>economy equivalent seat allocation</th>
-      <td>22.0</td>
-      <td>30.0</td>
-      <td>148.0</td>
-      <td>200.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Tickets Sold
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F      9.863
-B     17.303
-E    147.184
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Seats not Sold
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F    1.137
-B    2.697
-E    0.816
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Spillage (Unfulfilled Demand)
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F     2.149
-B    11.287
-E    28.117
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Expected Revenue (in units of economy ticket price): 211.38
-</pre></div>
-</div>
-<img alt="../../_images/ef992eaadb2a2e6a9e2ff1a45bfc363a8537ac01c74b81c6d0cee8f668e7369f.png" src="../../_images/ef992eaadb2a2e6a9e2ff1a45bfc363a8537ac01c74b81c6d0cee8f668e7369f.png" />
-<img alt="../../_images/2ba8784408ac99124215a4d8aab3c24e66745d422b3ac6b5c9a2b6b8a1792825.png" src="../../_images/2ba8784408ac99124215a4d8aab3c24e66745d422b3ac6b5c9a2b6b8a1792825.png" />
-</div>
-</div>
-</section>
-<section id="model-6-tackling-chance-constraints-using-saa-in-the-case-of-correlated-demand">
-<h2>Model 6. Tackling chance constraints using SAA in the case of correlated demand<a class="headerlink" href="#model-6-tackling-chance-constraints-using-saa-in-the-case-of-correlated-demand" title="Permalink to this heading">#</a></h2>
-<p>The linear counterparts of the chance constraints used above in Model 3 were derived under the assumption of independent normal distributions of demand for first-class and business travel. That assumption no longer holds for the case where demand scenarios are sampled from correlated distributions.</p>
-<p>This final model replaces the chance constraints by approximating them using two linear constraints that explicitly track unsatisfied demand. In doing so, we introduce two new sets of integer variables <span class="math notranslate nohighlight">\(y_s\)</span> and <span class="math notranslate nohighlight">\(w_s\)</span> and a big-M constant and approximate the true multivariate distribution with the empirical one obtained from the sample.</p>
-<p>The first stage remains unchanged and so does the objective value of the second stage. The adjusted second-stage constraints are:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}
-\begin{align*}
-    t_c &amp; \leq s_c &amp; \forall c\in C \\
-    t_c &amp; \leq z_{c, s} &amp; \forall (c, s) \in C \times S \\
-    s_F + M y_s &amp; \geq z_{F, s} &amp; \forall s \in  S\\
-    s_F + s_B + M w_s &amp; \geq z_{F, s} + z_{B,s} &amp; \forall s \in  S \\
-    \frac{1}{N} \sum_{s\in S} y_s &amp; \leq 1 - 0.98 \\
-    \frac{1}{N} \sum_{s\in S} z_s &amp; \leq 1 - 0.95
-\end{align*}
-\end{split}\]</div>
-<p>where <span class="math notranslate nohighlight">\(y_s\)</span> and <span class="math notranslate nohighlight">\(w_s\)</span> are binary variables indicating those scenarios which do not satisfy the requirements of the airline’s loyalty programs for first-class and business-class passengers.</p>
-<p>The following cell implements this new model. Note that the running time for the cell can be up to a few minutes for a large number of scenarios.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># define big M constant for chance constraints counterparts</span>
-<span class="n">bigM</span> <span class="o">=</span> <span class="mi">100</span>
-
-
-<span class="k">def</span> <span class="nf">airline_final</span><span class="p">(</span><span class="n">demand</span><span class="p">):</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">()</span>
-
-    <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">demand</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">demand</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
-
-    <span class="c1"># first stage variables and constraints</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">seats</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">plane_seats</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">seat_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">capacity</span>
-
-    <span class="c1"># second stage variable and constraints</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">tickets</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeIntegers</span><span class="p">)</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">first_class</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">business_class</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">Binary</span><span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">demand_limits</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">demand</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">s</span><span class="p">]</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span><span class="p">,</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">seat_limits</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">first_class_loyality</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">bigM</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">first_class</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">demand</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">][</span><span class="n">s</span><span class="p">]</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
-    <span class="k">def</span> <span class="nf">first_class_loyality_rate</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">first_class</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mf">0.02</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span>
-            <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span>
-        <span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">business_class_loyality</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
-        <span class="k">return</span> <span class="p">(</span>
-            <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">seats</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">bigM</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">business_class</span><span class="p">[</span><span class="n">s</span><span class="p">]</span>
-            <span class="o">&gt;=</span> <span class="n">demand</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">][</span><span class="n">s</span><span class="p">]</span> <span class="o">+</span> <span class="n">demand</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">][</span><span class="n">s</span><span class="p">]</span>
-        <span class="p">)</span>
-
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
-    <span class="k">def</span> <span class="nf">business_class_loyality_rate</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">business_class</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mf">0.05</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span>
-            <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span>
-        <span class="p">)</span>
-
-    <span class="c1"># objective</span>
-    <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">revenue</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span>
-            <span class="n">m</span><span class="o">.</span><span class="n">tickets</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">revenue_factor</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">CLASSES</span>
-            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SCENARIOS</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">m</span>
-
-
-<span class="c1"># create model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">airline_final</span><span class="p">(</span><span class="n">demand_saa</span><span class="p">)</span>
-<span class="n">seats</span> <span class="o">=</span> <span class="n">airline_solve</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
-<span class="n">seat_report_saa</span><span class="p">(</span><span class="n">seats</span><span class="p">,</span> <span class="n">demand_saa</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Seat Allocation
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>F</th>
-      <th>B</th>
-      <th>E</th>
-      <th>TOTAL</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>seat allocation</th>
-      <td>20.0</td>
-      <td>50.0</td>
-      <td>85.0</td>
-      <td>155.0</td>
-    </tr>
-    <tr>
-      <th>economy equivalent seat allocation</th>
-      <td>40.0</td>
-      <td>75.0</td>
-      <td>85.0</td>
-      <td>200.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Tickets Sold
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F    11.995
-B    28.031
-E    85.000
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Seats not Sold
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F     8.005
-B    21.969
-E     0.000
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Spillage (Unfulfilled Demand)
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>F     0.017
-B     0.559
-E    90.301
-dtype: float64
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Expected Revenue (in units of economy ticket price): 177.05
-</pre></div>
-</div>
-<img alt="../../_images/72ede3091c5ccf0068d1e972481950c647421ec90304ad32a9e55558da186dd8.png" src="../../_images/72ede3091c5ccf0068d1e972481950c647421ec90304ad32a9e55558da186dd8.png" />
-<img alt="../../_images/1813465705cf13f818ee5ff8163f581473ce9828629f186e89c43cc0171c96fb.png" src="../../_images/1813465705cf13f818ee5ff8163f581473ce9828629f186e89c43cc0171c96fb.png" />
-</div>
-</div>
-<p><strong>Exercise</strong></p>
-<p>Compared the results of using positive correlation in demand for first and business-class tickets results in lower expected revenue. What happens if there is no correlation, or there is negative correlation? Verify your predictions by simulation.</p>
-</section>
-</section>
-
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-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#attribution">Attribution</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description">Problem description</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#installation-and-imports">Installation and imports</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-data">Problem data</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#analytics">Analytics</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-1-deterministic-solution-for-the-average-demand-scenario">Model 1. Deterministic solution for the average demand scenario</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-2-two-stage-stochastic-optimization-and-its-extensive-form">Model 2. Two-stage stochastic optimization and its extensive form</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-3-adding-chance-constraints">Model 3. Adding chance constraints</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-4-solving-the-case-of-continuous-demand-distributions-using-the-saa-method">Model 4. Solving the case of continuous demand distributions using the SAA method</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scenario-generation-uncorrelated-case">Scenario generation (uncorrelated case)</a></li>
-</ul>
-</li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-5-adding-correlations-between-different-demand-types">Model 5. Adding correlations between different demand types</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-6-tackling-chance-constraints-using-saa-in-the-case-of-correlated-demand">Model 6. Tackling chance constraints using SAA in the case of correlated demand</a></li>
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diff --git a/notebooks/appendix/appendix.html b/notebooks/appendix/appendix.html
index d486b1ef..bf92d64f 100644
--- a/notebooks/appendix/appendix.html
+++ b/notebooks/appendix/appendix.html
@@ -73,7 +73,7 @@
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="Pyomo style guide" href="pyomo-style-guide.html" />
-    <link rel="prev" title="Extra: Two-stage energy dispatch optimization with wind curtailment" href="../10/opf-wind-curtailment.html" />
+    <link rel="prev" title="Extra: Two-stage energy dispatch optimization with wind curtailment" href="../10/05-opf-wind-curtailment.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -188,9 +188,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -199,6 +199,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -206,62 +210,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
@@ -508,7 +511,7 @@ <h1>Appendix: Working with Pyomo<a class="headerlink" href="#appendix-working-wi
                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
     <a class="left-prev"
-       href="../10/opf-wind-curtailment.html"
+       href="../10/05-opf-wind-curtailment.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
diff --git a/notebooks/appendix/functional-programming-pyomo.html b/notebooks/appendix/functional-programming-pyomo.html
index 5a69d479..ccb07323 100644
--- a/notebooks/appendix/functional-programming-pyomo.html
+++ b/notebooks/appendix/functional-programming-pyomo.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/notebooks/appendix/pyomo-style-guide.html b/notebooks/appendix/pyomo-style-guide.html
index a4f0a8f1..42461cf9 100644
--- a/notebooks/appendix/pyomo-style-guide.html
+++ b/notebooks/appendix/pyomo-style-guide.html
@@ -190,9 +190,9 @@
 <li class="toctree-l2"><a class="reference internal" href="../02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -201,6 +201,10 @@
 <li class="toctree-l2"><a class="reference internal" href="../03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="../03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -208,62 +212,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
diff --git a/objects.inv b/objects.inv
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GIT binary patch
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diff --git a/search.html b/search.html
index a94485a2..8f81e47f 100644
--- a/search.html
+++ b/search.html
@@ -187,9 +187,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/04-bim-dual.html">2.4 Dual of the BIM production problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/05-bim-maxmin.html">2.5 BIM production for worst case</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/06-bim-fractional.html">2.6 BIM production variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/02/07-bim-rawmaterialplanning.html">2.7 BIM production using demand forecasts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/02/07-bim-demand-forecast.html">2.7 BIM production using demand forecasts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/02/08-L1-regression-wine-quality.html">Extra material: Wine quality prediction with <span class="math notranslate nohighlight">\(L_1\)</span> regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/02/09-multiproductionfaciliity_worstcase.html">Extra material: Multi-product facility production</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/02/09-production-faciliity-worst-case.html">Extra material: Multi-product facility production</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/03/03.00.html">3. Mixed Integer Linear Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -198,6 +198,10 @@
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/03-recharging-electric-vehicle.html">3.3 Recharging strategy for an electric vehicle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/04-simple-production-model-gdp.html">3.4 Production model using disjunctions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/05-machine-scheduling.html">3.5 Machine Scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/03/06-facility-location.html">3.6 Facility location problem</a></li>
+
+
+<li class="toctree-l2"><a class="reference internal" href="notebooks/03/07-bim-production-revisited.html">3.7 BIM production revisited</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/08-cryptarithms.html">Extra material: Cryptarithms puzzle</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/09-strip-packing.html">Extra material: Strip packing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/03/10-job-shop-scheduling.html">Extra material: Job shop scheduling</a></li>
@@ -205,62 +209,61 @@
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/04/04.00.html">4. Network Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/dinner-seat-allocation.html">Dinner seating arrangement</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/01-dinner-seat-allocation.html">4.1 Dinner seating arrangement</a></li>
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/graph-coloring.html">Exam room scheduling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/power-network.html">Extra material: Energy dispatch problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/02-mincost-flow.html">4.2 Minimum-Cost Flow Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/03-gasoline-distribution.html">4.3 Gasoline distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/04-exam-room-scheduling.html">4.4 Exam room scheduling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/05-cryptocurrency-arbitrage.html">4.5 Cryptocurrency arbitrage search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/06-power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/07-forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/milk-pooling.html">Milk pooling and blending</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/ols-regression.html">Ordinary Least Squares (OLS) Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/markowitz_portfolio.html">Markowitz portfolio optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/svm.html">Support Vector Machines for Binary Classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/05/cutting-stock.html">Extra Material: Cutting Stock</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/01-milk-pooling.html">5.1 Milk pooling and blending</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/02-ols-regression.html">5.2 Ordinary Least Squares (OLS) Regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/03-markowitz-portfolio.html">5.3 Markowitz portfolio optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/04-svm-binary-classification.html">5.4 Support Vector Machines for Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/05-refinery-production.html">Extra material: Refinery production and shadow pricing with CVXPY</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/05/06-cutting-stock.html">Extra Material: Cutting Stock</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/06/06.00.html">6. Conic Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/economic-order-quantity.html">Economic Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/kelly-criterion.html">The Kelly Criterion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/markowitz_portfolio_revisited.html">Markowitz portfolio optimization revisited</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/svm-conic.html">Training Support Vector Machines with Conic Optimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/06/optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/01-economic-order-quantity.html">Economic Order Quantity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/02-kelly-criterion.html">The Kelly Criterion</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/03-markowitz-portfolio-revisited.html">Markowitz portfolio optimization revisited</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/04-building-insulation.html">Optimal Design of Multilayered Building Insulation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/05-svm-conic.html">Support Vector Machines with Conic Optimization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/06-investment-wheel.html">Extra material: Luenberger’s Investment Wheel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/06/07-optimal-growth-portfolios.html">Extra material: Optimal Growth Portfolios with Risk Aversion</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/07/07.00.html">7. Accounting for Uncertainty: Optimization Meets Reality</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/07/fleet-assignment.html">Fleet assignment problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/07/bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/07/01-fleet-assignment.html">Fleet assignment problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/07/02-bim-robustness-analysis.html">Robustness analysis of BIM production plan via simulations</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/08/08.00.html">8. Robust Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/08/bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/08/01-bim-robust-optimization.html">Robust BIM microchip production problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/09/09.00.html">9. Stochastic Optimization - Single Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/pop-up_shop.html">Pop-up shop</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/markowitz_portfolio_with_chance_constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/seafood.html">Stock optimization for seafood distribution center</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/09/economicdispatch.html">Economic dispatch in energy systems</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/01-pop-up-shop.html">Pop-up shop</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/02-markowitz-portfolio-with-chance-constraint.html">Markowitz portfolio optimization with chance constraints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/03-seafood-distribution-center.html">Stock optimization for seafood distribution center</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/09/04-economic-dispatch.html">Economic dispatch in energy systems</a></li>
 
 
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/10/10.00.html">10. Two-Stage Problems</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/airline-seating.html">Airline seat allocation problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/opf-ldr.html">Optimal power flow problem with recourse actions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/ccg.html">Two-stage Production Planning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/farmer.html">Extra: The farmer’s problem and its variants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/10/opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/02-opf-linear-decision-rule.html">Optimal power flow problem with recourse actions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/03-two-stage-production-planning.html">Two-stage production planning using constraint and column generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/04-farmer-problem.html">Extra: The farmer’s problem and its variants</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/10/05-opf-wind-curtailment.html">Extra: Two-stage energy dispatch optimization with wind curtailment</a></li>
 
 </ul>
 </li>
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"outcom": [2, 15, 30, 40, 44, 48, 55, 57, 61], "while": [2, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 39, 40, 46, 47, 48, 49, 50, 51, 53, 55, 57, 58, 60, 61, 63, 64], "some": [2, 3, 4, 5, 6, 7, 14, 19, 23, 24, 25, 27, 28, 30, 31, 33, 37, 38, 39, 40, 42, 44, 45, 47, 48, 53, 55, 61, 63, 64, 66, 67], "stage": [2, 4, 23, 37, 44, 45, 58, 62, 63, 66, 67], "mai": [2, 3, 4, 5, 11, 12, 13, 15, 16, 19, 23, 24, 25, 27, 29, 30, 33, 35, 37, 39, 40, 43, 44, 48, 50, 53, 55, 56, 62, 66], "prove": [2, 6], "redund": [2, 67], "later": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 39, 43, 45, 50, 51, 57, 58, 64, 67], "creat": [2, 7, 9, 13, 15, 21, 22, 23, 24, 25, 27, 28, 29, 31, 35, 37, 40, 43, 44, 47, 48, 55, 57, 60, 61, 62, 66, 67], "comprehens": [2, 14, 40, 48], "us": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 55, 56, 57, 59, 62, 63, 64, 65], "s": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66, 67], "For": [2, 3, 4, 6, 9, 12, 18, 19, 21, 23, 24, 27, 28, 29, 32, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 47, 48, 51, 53, 55, 57, 58, 60, 61, 62, 63, 64, 66, 67], "below": [2, 4, 9, 10, 11, 13, 16, 18, 24, 27, 28, 33, 35, 37, 40, 42, 43, 44, 48, 53, 55, 58, 60, 61, 64, 67], "symbol": [2, 9, 21, 27, 29, 30, 47, 57, 66, 67], "descript": [2, 15, 24, 30, 49, 53, 64], "upper": [2, 4, 5, 9, 10, 13, 18, 21, 24, 25, 27, 30, 31, 35, 36, 37, 38, 42, 43, 50, 53, 58, 66, 67], "bound": [2, 4, 5, 9, 10, 18, 20, 21, 22, 24, 25, 27, 28, 29, 31, 35, 36, 37, 40, 42, 51, 53, 55, 61, 62, 64, 66], "known": [2, 4, 5, 7, 9, 14, 21, 22, 23, 24, 25, 31, 36, 37, 38, 40, 42, 43, 45, 47, 48, 51, 55, 57, 61, 62, 63, 64], "x_m": [2, 4, 58], "0": [2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "x_a": [2, 4], "x_b": [2, 4], "y_u": [2, 4], "number": [2, 3, 4, 5, 6, 7, 10, 12, 16, 17, 18, 19, 21, 22, 23, 27, 29, 31, 33, 35, 36, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 67], "y_v": [2, 4], "next": [2, 3, 4, 5, 6, 8, 13, 14, 15, 17, 19, 21, 24, 25, 26, 27, 28, 30, 32, 34, 36, 41, 44, 48, 49, 51, 52, 54, 55, 58, 62, 63, 64], "step": [2, 3, 5, 6, 9, 10, 13, 15, 24, 27, 43, 44, 50, 51, 60, 61, 66, 67], "formul": [2, 3, 5, 6, 7, 8, 9, 10, 11, 15, 19, 20, 21, 23, 25, 26, 27, 28, 30, 33, 34, 37, 38, 42, 47, 49, 53, 55, 57, 58, 60, 63, 64, 67], "measur": [2, 9, 12, 14, 17, 18, 25, 27, 37, 40, 48, 49], "valu": [2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 23, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 58, 60, 61, 63, 64, 66, 67], "candid": [2, 3, 14, 24, 35], "In": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67], "case": [2, 3, 4, 5, 6, 7, 14, 17, 19, 20, 23, 24, 27, 28, 30, 35, 36, 37, 38, 40, 44, 45, 47, 48, 53, 55, 57, 58, 64, 66, 67], "max": [2, 5, 6, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 27, 33, 35, 36, 37, 39, 40, 42, 44, 45, 46, 48, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66], "text": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66, 67], "turn": [2, 3, 19, 24, 28, 34, 40, 48, 51, 55, 64], "equal": [2, 4, 6, 8, 12, 13, 14, 15, 17, 19, 20, 23, 25, 27, 28, 30, 31, 32, 36, 37, 38, 39, 42, 43, 44, 45, 47, 51, 53, 55, 57, 58, 60, 61, 63, 64], "between": [2, 4, 5, 6, 7, 9, 12, 17, 18, 23, 24, 27, 29, 30, 32, 33, 34, 36, 37, 38, 40, 42, 44, 45, 46, 48, 51, 55, 62, 63, 64, 66, 67], "revenu": [2, 4, 5, 11, 12, 39, 57, 60, 62, 66], "oper": [2, 3, 15, 21, 23, 24, 27, 33, 35, 37, 39, 40, 42, 43, 51, 57, 60, 61, 63, 64, 66], "begin": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66, 67], "align": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66, 67], "linear": [2, 3, 4, 6, 7, 9, 11, 12, 13, 14, 15, 16, 20, 21, 22, 25, 27, 29, 33, 34, 37, 38, 39, 53, 55, 58, 59, 60, 61, 63], "written": [2, 4, 5, 15, 40, 43, 56, 57, 60, 66, 67], "term": [2, 3, 4, 5, 7, 16, 17, 20, 21, 24, 27, 29, 30, 31, 35, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 51, 55, 57, 58, 61, 63, 64], "As": [2, 4, 5, 6, 9, 17, 24, 27, 28, 29, 37, 40, 43, 44, 48, 51, 57, 58, 61, 64, 67], "shown": [2, 4, 37, 40, 43, 44, 67], "here": [2, 4, 5, 7, 12, 13, 17, 18, 20, 21, 24, 27, 29, 37, 38, 40, 43, 44, 45, 47, 50, 51, 53, 55, 56, 57, 60, 61, 64, 66, 67], "algebra": [2, 4, 22, 39, 40, 43], "combin": [2, 4, 5, 22, 27, 37, 42, 51, 55, 61, 63, 66, 67], "refer": [2, 3, 5, 6, 7, 8, 18, 19, 23, 27, 30, 32, 37, 38, 40, 45, 48, 63, 67], "name": [2, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 18, 21, 24, 27, 28, 30, 35, 38, 39, 40, 46, 48, 57, 58, 60, 63, 64], "when": [2, 4, 5, 6, 7, 13, 14, 17, 18, 19, 21, 24, 25, 27, 29, 30, 33, 36, 37, 39, 40, 41, 43, 44, 45, 48, 55, 57, 58, 61, 62, 63, 64, 65, 66], "multipl": [2, 3, 13, 15, 23, 27, 29, 30, 32, 35, 37, 67], "place": [2, 5, 27, 28, 29, 33, 37, 43, 44, 57, 66, 67], "break": [2, 5, 27, 31, 51, 55, 61, 64], "up": [2, 5, 13, 16, 19, 22, 24, 25, 27, 35, 37, 40, 44, 45, 48, 50, 54, 55, 60, 61, 62], "longer": [2, 24, 28, 43, 48, 51, 55, 60, 66], "smaller": [2, 5, 12, 19, 23, 49, 55], "simplifi": [2, 4, 18, 23, 48, 60, 66, 67], "satisfi": [2, 3, 10, 15, 19, 22, 23, 27, 28, 29, 30, 32, 37, 40, 44, 45, 47, 55, 57, 60, 63], "specif": [2, 3, 4, 6, 7, 8, 9, 12, 15, 16, 17, 27, 28, 32, 33, 35, 37, 39, 40, 43, 46, 51, 53, 55, 57, 58, 60, 61, 63, 65, 66, 67], "condit": [2, 3, 6, 15, 18, 20, 23, 25, 27, 28, 37, 40, 42, 48, 53, 57, 62, 66, 67], "given": [2, 3, 4, 5, 6, 7, 9, 10, 11, 14, 15, 16, 17, 18, 20, 21, 23, 24, 27, 28, 29, 30, 33, 35, 37, 40, 42, 43, 44, 45, 47, 48, 49, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "relationship": [2, 4, 7, 17, 18, 21, 23, 27, 29, 42, 43, 44, 67], "among": [2, 3, 9, 11, 14, 15, 20, 21, 24, 29, 30, 31, 45, 46, 47, 60, 62, 63, 67], "either": [2, 3, 4, 5, 18, 20, 21, 23, 24, 25, 27, 33, 48, 57, 61, 63, 67], "inequ": [2, 6, 8, 10, 37, 45, 53, 63], "overal": [2, 6, 15, 17, 37, 42], "now": [2, 4, 5, 6, 9, 13, 14, 17, 18, 23, 24, 27, 28, 29, 30, 31, 32, 33, 35, 36, 40, 43, 44, 45, 46, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66], "readi": [2, 27, 29, 43, 48, 61], "full": [2, 7, 14, 18, 25, 30, 35, 37, 38, 40, 42, 47, 48, 53, 60, 61, 67], "canon": [2, 8, 36], "wai": [2, 3, 4, 5, 6, 7, 11, 19, 21, 24, 25, 26, 27, 28, 29, 30, 32, 33, 36, 37, 43, 44, 46, 47, 48, 51, 55, 56, 61, 63, 64, 66, 67], "minim": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 21, 23, 24, 27, 31, 32, 33, 35, 38, 40, 42, 43, 48, 50, 51, 53, 55, 62, 63, 64, 67], "lastli": [2, 3, 60, 61, 64], "quad": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 23, 25, 27, 28, 29, 30, 31, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 51, 56, 57, 58, 60, 61, 63, 66, 67], "t": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "nonumb": [2, 5, 28], "customari": [2, 6, 48], "under": [2, 4, 5, 9, 15, 17, 20, 21, 29, 40, 50, 55, 57, 60, 61, 62, 66], "so": [2, 3, 4, 5, 6, 7, 10, 17, 18, 23, 24, 25, 27, 28, 33, 34, 35, 36, 37, 38, 40, 42, 46, 48, 49, 51, 53, 55, 60, 61, 64, 66], "clearli": [2, 5, 14, 17, 25, 37, 51, 61, 64], "distinguish": [2, 27, 64, 67], "paramet": [2, 4, 5, 24, 25, 30, 31, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 53, 55, 57, 58, 60, 61, 63, 64, 66], "might": [2, 15, 19, 23, 27, 29, 31, 33, 34, 37, 40, 49, 57, 63, 66], "also": [2, 3, 4, 5, 6, 9, 11, 13, 15, 17, 21, 25, 27, 28, 33, 34, 36, 37, 39, 40, 43, 51, 53, 56, 60, 61, 63, 64, 67], "letter": [2, 22, 43, 61], "throughout": [2, 4, 6, 27, 61, 67], "websit": 2, "howev": [2, 5, 6, 7, 15, 16, 19, 26, 27, 28, 29, 31, 33, 34, 37, 40, 42, 43, 46, 47, 48, 51, 53, 55, 57, 60, 61, 63, 64, 67], "stick": 2, "convent": [2, 4, 6, 8, 55, 63, 64], "onli": [2, 5, 6, 10, 14, 17, 18, 19, 20, 23, 24, 27, 28, 30, 31, 33, 35, 37, 40, 43, 47, 51, 53, 55, 57, 60, 61, 62, 63, 67], "explicitli": [2, 6, 16, 27, 28, 29, 48, 51, 60, 62, 67], "domain": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 46, 50, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66], "part": [2, 3, 24, 35, 40, 43, 48, 53, 61, 62, 63], "vector": [2, 6, 7, 8, 11, 13, 33, 34, 36, 38, 39, 41, 56, 60, 61], "meet": [2, 3, 4, 13, 17, 21, 27, 28, 30, 33, 35, 37, 41, 50, 55, 57, 63, 64], "maximum": [2, 3, 6, 11, 15, 17, 18, 25, 27, 31, 32, 33, 37, 42, 45, 46, 47, 51, 53, 55, 61, 64, 66], "minimum": [2, 9, 11, 13, 17, 18, 19, 24, 26, 27, 31, 33, 35, 37, 42, 51, 60, 62], "even": [2, 4, 17, 27, 28, 37, 40, 46, 48, 51, 60, 61, 63, 64], "simpl": [2, 3, 4, 5, 13, 20, 24, 28, 29, 34, 35, 37, 39, 40, 43, 48, 53, 66, 67], "like": [2, 5, 6, 9, 10, 11, 13, 27, 28, 29, 30, 35, 37, 40, 48, 50, 60, 61, 63, 64], "immedi": [2, 24, 27], "clear": [2, 10, 16, 34, 55, 61, 67], "what": [2, 6, 15, 17, 21, 24, 27, 29, 37, 39, 41, 44, 45, 49, 53, 55, 57, 60, 61, 62, 64, 66], "exactli": [2, 9, 31, 32, 33, 43, 48, 49, 51, 53, 60, 61, 64], "where": [2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 30, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 51, 53, 55, 57, 58, 60, 61, 63, 64, 66, 67], "algorithm": [2, 3, 23, 27, 31, 43, 51, 61], "come": [2, 5, 9, 15, 28, 34, 40, 48, 61], "plai": [2, 6, 34, 44, 48, 51, 61], "thei": [2, 4, 6, 18, 19, 21, 27, 28, 29, 30, 34, 35, 37, 40, 47, 49, 51, 55, 60, 61, 63, 64, 66, 67], "procedur": [2, 35, 53, 61], "long": [2, 5, 9, 17, 19, 21, 27, 44, 45, 58, 61, 67], "standard": [2, 4, 6, 7, 8, 9, 21, 33, 37, 39, 40, 48, 56, 60, 67], "fashion": [2, 5, 51], "practition": [2, 47], "often": [2, 5, 19, 24, 26, 30, 33, 49, 61, 64, 67], "boil": 2, "down": [2, 16, 25, 28, 33, 45, 60, 61, 62], "pass": [2, 4, 5, 18, 24, 26, 40, 48, 66], "softwar": [2, 20], "regardless": [2, 5, 6, 13, 27, 44, 50, 58], "wa": [2, 4, 9, 14, 17, 27, 28, 30, 33, 34, 35, 37, 43, 44, 45, 46, 47, 51, 53, 57, 61, 63], "origin": [2, 3, 4, 5, 6, 7, 9, 10, 12, 16, 17, 23, 28, 33, 35, 37, 43, 46], "stori": [2, 44, 45], "behind": 2, "To": [2, 4, 6, 17, 18, 21, 23, 24, 25, 27, 28, 33, 35, 37, 40, 42, 43, 47, 48, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66], "do": [2, 5, 6, 17, 25, 27, 28, 29, 31, 33, 37, 39, 43, 51, 53, 55, 60, 61, 63, 64, 66, 67], "interfac": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 39, 43, 50, 51, 57, 58, 67], "commun": [2, 4, 14, 17, 23, 62], "adopt": [2, 6, 20, 30, 40, 42, 45, 53], "python": [2, 4, 6, 7, 16, 17, 24, 27, 28, 30, 35, 38, 39, 40, 43, 48, 51, 55, 64, 66, 67], "base": [2, 7, 13, 14, 20, 22, 24, 27, 30, 33, 38, 39, 40, 45, 47, 48, 49, 50, 55, 61, 63, 66, 67], "correspond": [2, 3, 5, 6, 7, 9, 10, 11, 13, 16, 17, 21, 22, 23, 25, 27, 31, 33, 35, 40, 42, 43, 46, 48, 51, 53, 58, 60, 61, 63, 64, 67], "carri": [2, 18, 33], "out": [2, 3, 18, 23, 25, 27, 28, 30, 34, 35, 51, 55, 63, 64], "broad": 3, "them": [3, 4, 5, 6, 10, 18, 19, 27, 28, 34, 36, 37, 47, 51, 53, 56, 60, 61, 63, 64], "dedic": [3, 53], "consist": [3, 4, 7, 9, 14, 17, 24, 27, 29, 31, 33, 38, 40, 47, 48, 60, 66, 67], "three": [3, 5, 17, 21, 23, 24, 29, 37, 47, 49, 57, 58, 60, 62, 66], "action": [3, 59, 64], "choic": [3, 5, 15, 18, 20, 24, 33, 35, 37, 40, 42, 55, 66, 67], "whether": [3, 6, 18, 20, 21, 23, 27, 29, 31, 33, 34, 42, 43, 51, 55, 61, 64], "new": [3, 4, 5, 6, 7, 14, 16, 17, 20, 23, 24, 25, 27, 28, 30, 33, 35, 37, 39, 40, 43, 46, 53, 57, 60, 61, 64, 66, 67], "manufactur": [3, 6, 16, 23, 32, 39, 61], "facil": [3, 8, 19, 20, 23, 24, 32, 37, 43], "suppli": [3, 5, 28, 30, 32, 33, 37, 55, 64], "rout": [3, 27, 49, 51, 60], "product": [3, 4, 8, 19, 25, 32, 33, 34, 35, 37, 40, 43, 44, 48, 49, 52, 55, 59, 62, 63, 64], "evalu": [3, 7, 40, 48, 61], "i": [3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 19, 21, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 58, 60, 61, 63, 64, 67], "e": [3, 14, 17, 21, 22, 23, 24, 27, 28, 30, 31, 32, 33, 35, 36, 38, 39, 40, 42, 43, 44, 45, 47, 48, 51, 55, 56, 57, 58, 60, 61, 63, 64, 66, 67], "earlier": [3, 4, 12, 24, 40, 43, 46, 50, 55, 57], "specifi": [3, 4, 5, 6, 7, 18, 23, 24, 27, 28, 29, 32, 35, 36, 37, 40, 43, 44, 46, 48, 56, 64, 66, 67], "custom": [3, 24, 27, 30, 35, 37, 40, 49, 55, 60, 67], "restrict": [3, 17, 19, 28, 31, 33, 44, 47, 51, 53, 66, 67], "possibl": [3, 4, 5, 7, 8, 10, 15, 16, 18, 19, 22, 23, 24, 27, 28, 30, 31, 32, 33, 36, 37, 38, 40, 45, 48, 51, 53, 55, 56, 58, 60, 61, 62, 63, 64, 66, 67], "allow": [3, 5, 6, 18, 23, 25, 27, 29, 30, 33, 39, 43, 46, 47, 51, 53, 55, 63, 66], "budget": [3, 13, 35, 50, 61], "import": [3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 56, 57, 58, 61, 62, 67], "met": [3, 33, 37, 55], "capac": [3, 13, 18, 25, 28, 32, 33, 39, 50, 55, 60, 64, 67], "warehous": [3, 43, 49, 58, 67], "exceed": [3, 27, 28, 37, 55], "both": [3, 4, 5, 6, 7, 13, 16, 18, 20, 23, 25, 27, 31, 33, 35, 36, 37, 40, 43, 45, 48, 59, 60, 61], "defin": [3, 4, 5, 6, 7, 9, 10, 13, 17, 19, 20, 21, 23, 30, 31, 33, 35, 37, 38, 39, 40, 43, 44, 48, 51, 53, 55, 60, 61, 63, 64, 66, 67], "feasibl": [3, 4, 6, 10, 16, 28, 33, 35, 39, 42, 44, 45, 47, 51, 58, 61, 63, 64], "region": [3, 6, 30, 33], "set": [3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 50, 51, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66], "best": [3, 4, 6, 14, 27, 28, 33, 42, 55, 61, 64, 67], "global": [3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 44, 46, 48, 50, 51, 53, 55, 56, 57, 58, 60, 64, 67], "optimum": [3, 37, 39, 42], "appli": [3, 4, 5, 18, 33, 35, 37, 40, 42, 43, 47, 63, 64, 66], "relat": [3, 4, 20, 25, 27, 31, 48, 53, 55, 60, 61, 63], "fundament": [3, 9, 37, 66], "question": [3, 6, 15, 21, 27, 40, 43, 49, 57, 62, 63], "translat": [3, 4, 5, 21, 27, 30, 35, 42], "real": [3, 4, 5, 7, 14, 19, 24, 28, 29, 33, 34, 35, 38, 44, 45, 47, 49, 51, 53, 55, 57, 60, 61, 63], "abstract": [3, 5, 6, 66], "represent": [3, 24, 40, 42, 48], "Not": [3, 27], "aspect": [3, 6, 8, 9, 19, 26, 34, 41, 52, 54, 59], "taken": [3, 27, 31, 40, 61, 63, 67], "account": [3, 12, 18, 25, 27, 29, 33, 41, 44, 45, 53, 57, 58, 61, 63, 64, 66], "typic": [3, 24, 33, 55, 57, 61, 66, 67], "signific": [3, 4, 6, 39, 43, 44, 45, 47, 48], "impact": [3, 14, 37, 49, 62], "approach": [3, 4, 6, 7, 9, 12, 14, 16, 20, 21, 22, 23, 28, 31, 35, 37, 42, 44, 47, 55, 61, 63, 66], "There": [3, 4, 17, 20, 21, 22, 27, 28, 33, 35, 37, 42, 43, 57, 61, 66, 67], "equival": [3, 4, 6, 21, 31, 40, 42, 43, 51, 53, 56, 60, 67], "conceptu": 3, "comput": [3, 4, 5, 6, 9, 14, 15, 21, 25, 27, 28, 29, 33, 35, 37, 40, 43, 44, 47, 48, 50, 57, 60, 62, 63, 64, 66, 67], "complex": [3, 4, 5, 15, 21, 24, 25, 27, 29, 35, 66, 67], "interpret": [3, 5, 15, 31, 39], "after": [3, 4, 7, 25, 27, 28, 29, 38, 45, 51, 53, 57, 61, 62, 63, 64, 66], "back": [3, 7, 25, 27, 29, 31, 40, 48, 61, 66], "These": [3, 5, 15, 17, 23, 27, 32, 33, 37, 40, 43, 45, 47, 51, 55, 57, 60, 66, 67], "treat": [3, 24, 28, 37, 49, 64], "continu": [3, 13, 21, 25, 37, 40, 42, 48, 50, 58, 64], "process": [3, 5, 25, 27, 37, 39, 40, 43, 48, 51, 53, 66, 67], "sequenti": 3, "final": [3, 4, 5, 9, 13, 21, 24, 25, 27, 29, 37, 43, 60, 63], "impract": [3, 27], "adjust": [3, 5, 9, 27, 33, 39, 55, 60, 63, 64], "certain": [3, 19, 27, 28, 30, 40, 43, 48, 53, 55, 60, 64], "properti": [3, 6, 28, 34, 42, 44, 46, 47, 55], "cannot": [3, 6, 13, 17, 19, 21, 24, 27, 31, 33, 42, 50, 51, 58, 60, 61, 64], "effici": [3, 9, 12, 23, 24, 27, 28, 46, 47, 48, 53, 67], "perhap": [3, 22, 30, 35], "re": [3, 67], "includ": [3, 4, 8, 9, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 34, 35, 37, 39, 40, 42, 43, 44, 45, 47, 48, 51, 53, 55, 56, 60, 61, 62, 63, 64, 65, 66, 67], "well": [3, 4, 7, 9, 14, 21, 22, 23, 34, 35, 42, 44, 45, 58, 62, 67], "except": [3, 17, 31, 64, 66, 67], "mathematician": 3, "studi": [3, 15, 23, 24, 27, 35, 37, 42, 55, 58, 67], "alwai": [3, 5, 12, 13, 16, 18, 33, 36, 38, 40, 44, 45, 46, 48, 49, 53, 56, 60, 66], "flaw": 3, "challeng": [3, 6, 22, 23, 29, 35, 37, 40], "f": [3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "x": [3, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 18, 19, 20, 23, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 45, 46, 48, 50, 51, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67], "mathbb": [3, 6, 7, 8, 16, 19, 20, 33, 35, 36, 38, 40, 43, 44, 45, 47, 48, 53, 55, 56, 57, 58, 60, 63, 64], "r": [3, 5, 6, 7, 8, 9, 17, 18, 19, 21, 22, 23, 24, 27, 29, 30, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 51, 53, 56, 57, 58, 60, 61, 62, 63, 64, 66], "subseteq": [3, 19, 31, 33, 51], "n": [3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 35, 36, 37, 38, 40, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 58, 60, 61, 67], "being": [3, 4, 6, 8, 31, 33, 36, 37, 48, 51, 64], "y": [3, 5, 6, 7, 9, 10, 14, 18, 20, 22, 23, 25, 27, 28, 29, 30, 32, 33, 36, 37, 38, 39, 40, 42, 43, 45, 46, 47, 48, 53, 55, 57, 58, 61, 62, 64, 66, 67], "geq": [3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 19, 20, 21, 23, 24, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 53, 55, 56, 58, 60, 61, 63, 64, 66], "foral": [3, 5, 7, 9, 11, 13, 14, 15, 17, 18, 21, 22, 23, 25, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 40, 43, 44, 46, 47, 48, 51, 53, 55, 57, 60, 61, 63, 64], "least": [3, 8, 13, 14, 18, 21, 31, 33, 34, 35, 37, 50, 51, 61, 62], "good": [3, 4, 5, 14, 15, 23, 27, 31, 32, 36, 44, 47, 57, 61, 62, 67], "similarli": [3, 6, 7, 33, 38], "chang": [3, 5, 9, 12, 15, 18, 23, 24, 25, 27, 28, 29, 30, 31, 33, 36, 37, 38, 39, 46, 49, 51, 53, 55, 56, 60, 61, 63, 66, 67], "last": [3, 13, 18, 21, 25, 36, 37, 50, 51, 53, 62, 64], "min": [3, 6, 7, 8, 9, 10, 11, 14, 17, 18, 19, 21, 23, 27, 28, 30, 31, 32, 33, 35, 37, 38, 40, 42, 43, 44, 48, 51, 53, 55, 57, 58, 61, 63, 64, 67], "lead": [3, 9, 16, 20, 21, 22, 24, 27, 34, 37, 40, 42, 48, 53, 55, 61, 64, 67], "techniqu": [3, 7, 11, 14, 20, 21, 24, 25, 30, 40, 43, 47, 55, 61, 63], "introductori": [3, 59], "present": [3, 4, 5, 6, 7, 9, 14, 18, 27, 29, 30, 33, 35, 37, 38, 39, 40, 44, 45, 46, 47, 48, 51, 60, 62, 64, 66, 67], "context": [3, 15, 17, 31, 32, 56, 61, 64], "plan": [3, 4, 5, 11, 12, 15, 17, 18, 19, 23, 43, 49, 57, 59], "explain": [3, 14, 33, 37, 48, 55, 64], "structur": [3, 5, 6, 16, 20, 26, 27, 28, 35, 42, 60, 61, 63, 64, 66, 67], "formal": [3, 19], "implement": [3, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 21, 22, 23, 26, 27, 30, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 49, 52, 54, 55, 56, 57, 59, 60, 61, 66, 67], "second": [3, 4, 5, 7, 12, 23, 24, 27, 29, 30, 31, 33, 35, 36, 37, 40, 48, 58, 59, 60, 61, 62, 63, 64], "serv": [3, 4, 5, 32, 51, 61], "tutori": [3, 17, 35, 44, 45, 67], "discuss": [3, 30, 40, 43, 48, 49, 61], "featur": [3, 4, 5, 6, 7, 8, 19, 21, 23, 24, 25, 32, 37, 38, 45, 47, 58, 60, 66, 67], "third": [3, 4, 5, 30], "go": [3, 6, 7, 8, 9, 19, 24, 26, 28, 33, 34, 37, 38, 41, 49, 51, 52, 54, 61, 62, 63, 66], "about": [3, 6, 8, 18, 19, 26, 27, 28, 34, 41, 49, 52, 54, 55, 57, 60, 61, 63], "mathemat": [4, 15, 25, 27, 28, 30, 34, 35, 37, 42, 46, 47, 51, 57, 67], "integr": [4, 16, 39, 51, 65, 66, 67], "program": [4, 14, 15, 22, 23, 25, 35, 41, 43, 44, 47, 48, 60, 62, 65, 67], "environ": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "It": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 37, 38, 39, 40, 42, 43, 44, 46, 48, 50, 51, 53, 55, 56, 57, 58, 61, 63, 64], "enabl": [4, 5, 26, 27, 43, 48, 66], "user": [4, 27, 28, 39, 40, 48, 62, 67], "varieti": [4, 33], "project": [4, 37, 40, 43, 48, 65, 67], "ti": 4, "vendor": 4, "class": [4, 5, 6, 8, 11, 17, 21, 23, 26, 28, 40, 48, 60, 66, 67], "constantli": 4, "evolv": 4, "through": [4, 7, 24, 27, 30, 32, 33, 40, 42, 51, 55, 63, 66], "contribut": [4, 9, 14, 22], "parti": [4, 27, 30], "small": [4, 5, 6, 13, 17, 20, 27, 28, 29, 31, 32, 35, 37, 39, 61, 63, 67], "solverfactori": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "direct": [4, 7, 10, 23, 29, 30, 32, 33, 35, 43, 51, 53], "compon": [4, 5, 6, 7, 9, 20, 21, 36, 43, 48, 56, 67], "directli": [4, 5, 9, 14, 21, 25, 30, 33, 42, 43, 48, 57], "simplic": [4, 31, 33], "method": [4, 5, 7, 9, 20, 21, 25, 26, 27, 35, 40, 42, 48, 61, 63, 66, 67], "reus": [4, 24], "anoth": [4, 11, 13, 19, 21, 22, 24, 27, 29, 36, 39, 40, 48, 51, 53, 56, 64], "write": [4, 24, 29, 30, 33, 53, 60, 65, 67], "more": [4, 5, 6, 7, 9, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 29, 30, 33, 35, 37, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 57, 58, 60, 61, 62, 63, 64, 66, 67], "driven": [4, 49], "decor": [4, 5, 6, 10, 17, 30, 60], "design": [4, 5, 9, 21, 22, 23, 30, 37, 38, 40, 41, 64, 66], "unfamiliar": [4, 67], "current": [4, 6, 15, 18, 20, 27, 30, 37, 44, 49, 53, 61, 64, 66], "offer": [4, 6, 27, 29, 30, 44, 45, 55, 61, 67], "improv": [4, 5, 17, 37, 40, 42, 43, 45, 62], "readabl": [4, 6, 17, 30], "rel": [4, 6, 17, 18, 20, 27, 29, 46, 61, 66], "recent": [4, 35, 37, 42, 44, 45, 47, 67], "collect": [4, 5, 7, 8, 11, 14, 15, 23, 24, 29, 30, 31, 33, 38, 40, 48, 60, 61, 67], "intend": [4, 17, 67], "cloud": 4, "person": [4, 14, 24, 43], "verifi": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 44, 46, 48, 50, 51, 53, 55, 56, 57, 58, 60, 64], "appropri": [4, 5, 40, 48], "done": [4, 5, 21, 24, 37, 40, 57, 61], "session": 4, "high": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 40, 42, 47, 48, 50, 51, 53, 55, 57, 58, 61, 63, 64, 67], "mix": [4, 8, 16, 20, 25, 34, 35, 37, 53, 55], "integ": [4, 8, 13, 14, 16, 20, 22, 25, 28, 34, 35, 37, 55, 57, 60, 67], "assum": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 46, 47, 48, 49, 50, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66, 67], "been": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 55, 56, 57, 58, 60, 62, 64, 66, 67], "previous": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 42, 44, 45, 46, 48, 50, 51, 55, 56, 57, 58, 60, 63, 64, 66, 67], "suitabl": [4, 21, 31, 40, 48, 66], "coin": 4, "OR": [4, 23, 24, 25, 43], "cbc": [4, 24, 25, 28, 60, 61, 62, 63, 64, 66], "glpk": 4, "cplex": [4, 53], "gurobi": [4, 28, 43, 53], "mosek": [4, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 55, 56], "elsewher": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 42, 44, 46, 48, 50, 51, 55, 56, 57, 58, 64, 67], "via": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 42, 44, 46, 48, 49, 51, 55, 56, 57, 58, 64], "appsi": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 39, 50, 51, 57, 58, 64], "modul": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66], "test": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 39, 40, 47, 48, 50, 51, 57, 58, 66, 67], "store": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 39, 40, 43, 48, 50, 51, 57, 58, 61, 63, 64, 66, 67], "sy": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66], "pip": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66], "dev": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64], "null": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64], "highspi": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 50, 51, 53, 57, 58, 60, 61, 62, 63, 64], "appsi_high": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 50, 51, 53, 57, 58, 60, 61, 62, 63, 64], "assert": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58], "commonli": [4, 9, 20, 37, 40, 48, 67], "although": [4, 19, 28, 53], "previou": [4, 5, 10, 13, 15, 26, 27, 33, 40, 43, 48, 49, 61], "mention": [4, 6, 11], "again": [4, 27, 28, 29, 33, 40, 46, 48, 61], "emphas": [4, 67], "uniform": [4, 6, 25, 30, 37, 43, 44, 58, 61, 63, 67], "maintain": [4, 5, 6, 23, 27, 43, 64, 67], "alia": 4, "instanc": [4, 5, 6, 13, 28, 32, 35, 40, 45, 48, 55, 57, 64, 66, 67], "short": [4, 9, 24, 27, 29, 33, 36, 37, 43, 47, 55, 66], "sinc": [4, 6, 10, 17, 18, 23, 28, 31, 33, 35, 38, 40, 42, 43, 46, 48, 49, 53, 55, 58, 60, 61, 63, 64], "prefix": 4, "accept": [4, 5, 21, 24, 27, 30, 40, 44, 47, 55, 61, 67], "option": [4, 5, 9, 23, 24, 27, 31, 39, 40, 48, 61, 63, 66, 67], "string": [4, 27, 30, 40, 48, 66], "argument": [4, 5, 31, 37, 40, 48, 53, 60, 61, 63, 66, 67], "subsequ": [4, 7, 9, 14, 24, 27, 33, 35, 37, 38, 51, 57, 60, 67], "time": [4, 5, 6, 8, 9, 17, 18, 20, 23, 25, 28, 29, 31, 33, 34, 35, 37, 38, 40, 43, 44, 47, 48, 49, 50, 51, 55, 57, 60, 61, 63, 64, 66, 67], "construct": [4, 5, 9, 10, 14, 24, 27, 30, 35, 36, 40, 42, 43, 48, 51, 63, 64, 66, 67], "altern": [4, 6, 7, 12, 14, 15, 20, 30, 31, 45, 48, 64, 67], "abstractmodel": 4, "But": [4, 15, 24, 30, 35, 37, 40, 48, 64], "normal": [4, 9, 29, 37, 40, 44, 47, 48, 51, 56, 60, 67], "displai": [4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 21, 23, 27, 28, 32, 35, 37, 38, 39, 40, 42, 43, 46, 48, 50, 51, 56, 57, 60, 61, 62, 63, 66], "content": [4, 27, 37, 43, 44, 45], "At": [4, 13, 17, 18, 23, 24, 29, 45, 50, 62], "major": [4, 30], "empti": 4, "none": [4, 5, 14, 23, 24, 27, 28, 30, 32, 33, 38, 40, 43, 45, 48, 50, 55, 64, 66], "var": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 46, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "assign": [4, 5, 15, 17, 18, 19, 21, 22, 27, 28, 29, 30, 31, 35, 39, 40, 48, 49, 64], "valid": [4, 40, 45, 48, 67], "identifi": [4, 5, 18, 19, 23, 27, 29, 32, 35, 37, 40, 42, 43, 45, 48, 60, 64, 67], "dot": [4, 6, 7, 9, 13, 14, 15, 19, 25, 27, 30, 31, 36, 38, 40, 42, 43, 45, 46, 48, 50, 55, 56, 60, 63], "notat": [4, 22, 27, 29, 40, 61, 63], "chosen": [4, 36], "reflect": [4, 12], "keyword": [4, 40, 48, 67], "By": [4, 5, 9, 14, 28, 29, 30, 53, 67], "default": [4, 5, 6, 9, 17, 18, 24, 27, 30, 31, 37, 40, 48, 64, 67], "nonnegativer": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 39, 40, 46, 50, 53, 55, 56, 58, 61, 63, 64, 66, 67], "nonnegativeinteg": [4, 16, 28, 35, 53, 57, 60], "binari": [4, 17, 19, 20, 21, 22, 23, 25, 28, 31, 33, 34, 35, 42, 48, 51, 53, 55, 59, 60, 61, 64], "tupl": [4, 5, 6, 17, 24, 27, 35, 66], "contain": [4, 5, 7, 17, 20, 33, 35, 38, 40, 48, 53, 55, 64, 66, 67], "fix": [4, 5, 8, 9, 13, 14, 17, 18, 23, 28, 30, 33, 38, 42, 43, 44, 45, 47, 55, 58, 60, 61, 62, 63, 64, 66], "placehold": [4, 28], "unknown": [4, 36, 37, 40, 56, 58, 61, 63], "show": [4, 5, 7, 9, 10, 13, 14, 17, 18, 20, 21, 23, 24, 27, 28, 29, 30, 31, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 49, 50, 51, 53, 55, 57, 58, 60, 61, 64, 66, 67], "yet": [4, 53], "updat": [4, 18, 21, 24, 36, 37, 38, 39, 40, 43, 46, 48, 49, 50, 53], "size": [4, 5, 7, 9, 13, 14, 17, 18, 21, 23, 27, 30, 35, 36, 37, 38, 40, 43, 45, 46, 47, 48, 50, 51, 53, 55, 57, 58, 60, 67], "index": [4, 5, 6, 7, 9, 13, 15, 17, 18, 21, 22, 23, 24, 25, 27, 28, 29, 30, 33, 35, 37, 38, 39, 40, 42, 43, 47, 48, 50, 51, 55, 57, 60, 62, 63, 66], "kei": [4, 5, 6, 9, 24, 27, 30, 31, 32, 33, 35, 38, 44, 47, 50, 51, 55, 57, 58, 61, 63, 64, 66, 67], "stale": [4, 5, 30, 38], "fals": [4, 5, 6, 9, 17, 20, 21, 24, 28, 30, 31, 33, 35, 37, 38, 40, 43, 48, 51, 53, 55, 58, 60, 61, 63, 64, 66], "true": [4, 5, 7, 9, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 42, 43, 44, 45, 47, 48, 50, 51, 53, 55, 58, 60, 61, 62, 63, 64, 66, 67], "formula": [4, 40, 44, 45, 58], "respect": [4, 5, 6, 9, 20, 23, 24, 27, 28, 30, 32, 33, 35, 37, 40, 44, 53, 55, 60, 61, 62, 64, 66], "print": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 22, 23, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 42, 43, 44, 45, 47, 48, 50, 51, 53, 55, 57, 58, 60, 61, 62, 63, 64, 66], "could": [4, 6, 9, 15, 16, 19, 24, 25, 27, 28, 29, 35, 36, 37, 38, 43, 46, 53, 55, 57, 60, 61, 62, 64, 67], "expr": [4, 6, 10, 11, 12, 16, 37, 48, 53, 58, 61, 63, 67], "sens": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 44, 45, 46, 47, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "releas": [4, 21, 51], "With": [4, 13, 20, 27, 37, 45, 48, 61, 64], "def": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "m": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "return": [4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66, 67], "behavior": [4, 37, 47], "line": [4, 6, 7, 9, 21, 27, 33, 39, 42, 58, 64, 67], "take": [4, 5, 6, 10, 11, 14, 16, 18, 19, 21, 23, 24, 25, 27, 29, 31, 33, 36, 37, 40, 43, 45, 46, 48, 49, 51, 53, 55, 56, 57, 58, 60, 61, 63, 64, 66], "add": [4, 6, 11, 13, 17, 21, 24, 25, 27, 28, 29, 30, 35, 39, 40, 44, 45, 51, 53, 55, 58, 60, 61, 67], "attribut": [4, 5, 9, 14, 27, 33, 35, 43, 63, 66, 67], "effect": [4, 5, 24, 25, 32, 42, 44, 45, 51, 66], "tag": [4, 51, 67], "insert": [4, 5, 30, 43, 45], "syntax": [4, 43, 53, 67], "logic": [4, 5, 6, 13, 16, 20, 21, 22, 27, 50, 53, 67], "separ": [4, 24, 27, 33, 34, 37, 40, 42, 48, 55, 63, 64, 67], "less": [4, 17, 23, 24, 27, 44, 47, 48, 55, 61, 67], "than": [4, 5, 6, 12, 13, 15, 16, 17, 18, 19, 23, 24, 25, 27, 28, 30, 31, 33, 35, 36, 37, 40, 44, 45, 47, 48, 50, 51, 53, 55, 57, 58, 60, 61, 63], "greater": [4, 15, 16, 17, 47], "raw_materi": 4, "labor_a": [4, 67], 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35, 36, 37, 39, 40, 42, 43, 44, 46, 47, 48, 51, 53, 56, 57, 58, 60, 61, 62, 63, 66, 67], "mit": [4, 5, 17, 20, 21, 29, 40], "licenc": [4, 5, 17, 20, 21, 29, 40], "warn": [4, 28, 40, 48, 55, 64], "No": [4, 17, 25, 35, 53, 57, 61], "semi": [4, 36, 38, 40, 46, 48, 56], "non": [4, 6, 8, 10, 16, 22, 23, 24, 27, 29, 32, 35, 36, 37, 40, 43, 44, 45, 48, 53, 55, 57, 60, 63, 66, 67], "presolv": 4, "row": [4, 5, 6, 14, 21, 27, 29, 33, 51, 61, 64, 67], "col": 4, "nonzero": [4, 50], "reduct": [4, 6, 14, 62, 63], "column": [4, 5, 9, 13, 14, 15, 17, 18, 21, 27, 28, 29, 30, 33, 37, 40, 43, 47, 48, 50, 51, 60, 62, 64, 67], "lp": [4, 11, 14, 15, 40, 51], "ekk": 4, "dual": [4, 8, 30, 39, 53], "simplex": 4, "serial": [4, 42], "iter": [4, 5, 27, 53, 61, 66, 67], "infeas": [4, 13, 31, 55, 56, 61, 63, 64], "num": [4, 55], "sum": [4, 5, 6, 7, 9, 10, 14, 15, 17, 21, 22, 23, 24, 25, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67], "0999953995e": 4, "02": [4, 9, 16, 27, 46, 55, 60], "ph1": 4, "du": 4, "0s": 4, "6000000000e": 4, "03": [4, 9, 27, 29, 37, 46], "pr": 4, "postsolv": 4, "statu": [4, 5, 28, 46, 53, 56, 58, 66], "00": [4, 10, 11, 12, 13, 14, 17, 20, 21, 27, 30, 33, 35, 37, 44, 47, 50, 53, 58, 60, 61, 62], "format": [4, 6, 12, 16, 24, 25, 43, 50], "tabular": [4, 5], "graphic": [4, 35, 39, 42, 58], "librari": [4, 5, 6, 7, 24, 28, 30, 31, 38, 39, 42, 43, 45, 47, 48, 67], "overview": [4, 29, 37, 40], "visual": [4, 6, 9, 13, 18, 21, 28, 30, 32, 33, 40, 53], "see": [4, 5, 17, 20, 24, 28, 29, 38, 42, 43, 44, 46, 48, 53, 55, 58, 61, 62, 63, 64, 66], "appendix": 4, "sever": [4, 5, 8, 11, 14, 18, 19, 20, 22, 26, 27, 34, 35, 37, 40, 47, 55, 67], "entir": [4, 6, 30, 32, 33, 60], "individu": [4, 9, 17, 24, 27, 28, 30, 37, 53, 55, 67], "whole": [4, 42, 60], "740": [4, 5, 53, 61, 66], "20": [4, 5, 9, 14, 17, 18, 20, 21, 23, 24, 25, 27, 28, 29, 31, 35, 42, 43, 44, 46, 47, 51, 53, 55, 60, 61, 62, 64, 66, 67], "60": [4, 5, 14, 18, 20, 27, 33, 47, 57, 62, 63, 64, 66], "successfulli": [4, 14, 37, 40], "2600": [4, 5, 20, 61], "conveni": [4, 7, 17, 21, 30, 63, 66], "2f": [4, 7, 10, 11, 12, 20, 27, 30, 31, 33, 36, 37, 43, 44, 46, 47, 50, 53, 56, 58, 60, 61, 62, 63, 64], "18000": [4, 30, 62], "15400": 4, "shortcut": 4, "wide": [4, 23, 27, 33], "scienc": [4, 5, 17, 30, 42, 43, 44, 60, 62], "seri": [4, 6, 9, 14, 15, 24, 30, 40, 43, 47, 48, 50, 60, 62, 66], "hold": [4, 5, 6, 9, 10, 13, 18, 19, 23, 27, 33, 43, 50, 55, 57, 58, 60, 61, 63, 64], "matplotlib": [4, 7, 9, 13, 14, 17, 18, 21, 23, 24, 25, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 58, 60, 61, 62, 63, 64], "bar": [4, 7, 9, 14, 15, 21, 25, 27, 30, 37, 38, 40, 43, 48, 53, 58, 61, 63], "chart": [4, 9, 17, 27, 30, 43, 55, 60], "pd": [4, 9, 13, 14, 15, 17, 18, 21, 23, 24, 25, 27, 28, 29, 30, 33, 35, 37, 39, 40, 42, 43, 47, 48, 50, 51, 53, 55, 57, 60, 61, 62, 63, 64, 66], "dtype": [4, 15, 48, 58, 60, 66], "float64": [4, 15, 48, 60, 66], "pyplot": [4, 7, 9, 13, 14, 17, 18, 21, 23, 24, 25, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 58, 60, 61, 62, 63, 64], "plt": [4, 7, 9, 13, 14, 17, 18, 21, 23, 24, 25, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 58, 60, 61, 62, 63, 64], "1x2": 4, "grid": [4, 7, 9, 13, 14, 15, 18, 24, 27, 33, 37, 38, 42, 43, 44, 45, 47, 50, 62, 64, 66], "subplot": [4, 7, 9, 14, 17, 18, 21, 23, 24, 25, 27, 30, 32, 35, 37, 39, 40, 42, 43, 44, 45, 47, 48, 50, 55, 58, 60, 61], "configur": [4, 23, 60, 63], "fig": [4, 7, 9, 14, 17, 18, 21, 23, 24, 25, 27, 30, 32, 35, 37, 39, 40, 42, 43, 44, 45, 47, 48, 50, 51, 55, 58, 60, 61], "ax": [4, 7, 9, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 27, 30, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 55, 58, 60, 61, 62, 66], "figsiz": [4, 9, 13, 14, 17, 18, 21, 23, 24, 25, 27, 28, 30, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 47, 48, 50, 51, 55, 58, 60, 61], "rcparam": [4, 7, 9, 13, 14, 17, 18, 21, 28, 36, 37, 38, 43, 46, 50, 53], "font": [4, 7, 9, 13, 14, 17, 18, 21, 36, 37, 38, 43, 46, 50, 53], "12": [4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 23, 24, 25, 27, 28, 29, 30, 33, 35, 40, 43, 45, 46, 48, 51, 53, 57, 60, 61, 67], "color": [4, 6, 7, 9, 13, 14, 15, 17, 18, 21, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 44, 45, 46, 48, 50, 51, 53, 55, 58], "cm": [4, 17, 24, 36, 46, 50], "tab20c": [4, 13, 14, 17, 36, 46, 50], "color_set": 4, "dataset": [4, 7, 14, 27, 38, 40, 48, 55], "plot": [4, 7, 9, 13, 14, 15, 18, 21, 24, 27, 28, 30, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 55, 58, 60, 61, 62, 64, 66], "enumer": [4, 9, 10, 17, 21, 24, 27, 35, 48, 58, 60], "zip": [4, 9, 13, 14, 23, 24, 27, 28, 31, 50, 55], "kind": [4, 14, 15, 27, 30, 35, 40, 43, 48, 60, 61], "barh": [4, 9, 60], "alpha": [4, 7, 14, 17, 18, 20, 21, 23, 24, 30, 32, 35, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 55, 56, 58, 60, 61, 63], "set_xlabel": [4, 7, 9, 13, 14, 17, 18, 21, 23, 24, 25, 27, 37, 39, 42, 43, 44, 45, 47, 50, 60, 61], "invert_yaxi": 4, "tight_layout": [4, 7, 9, 13, 14, 17, 18, 21, 23, 24, 25, 27, 28, 30, 35, 36, 37, 38, 40, 42, 43, 44, 46, 47, 50, 58, 60], "discov": [4, 27], "ll": [5, 37], "revisit": [5, 17, 19, 41, 59], "capabl": [5, 14, 20, 37, 53], "scale": [5, 14, 27, 28, 33, 35, 37, 40, 55, 58, 60, 61, 64], "vari": [5, 13, 36, 48, 50, 53, 55, 62], "addit": [5, 8, 13, 14, 15, 17, 23, 24, 25, 35, 40, 43, 45, 47, 48, 57, 60, 61, 64, 66, 67], "essenti": [5, 21, 27, 28, 35, 40, 48, 51, 53, 61], "scalabl": [5, 8], "analysi": [5, 6, 7, 13, 14, 24, 27, 30, 40, 44, 45, 48, 49, 51, 53, 60, 67], "examin": [5, 14, 16, 47], "underli": [5, 38, 61], "check": [5, 6, 27, 29, 36, 37, 43, 46, 48, 49, 56, 63, 64, 66, 67], "doe": [5, 15, 17, 23, 24, 25, 27, 28, 33, 35, 38, 40, 43, 48, 53, 60, 64], "quiet": 5, "pyo": [5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 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Convex Optimization", "Extra Material: Cutting Stock", "Economic Order Quantity", "The Kelly Criterion", "Markowitz portfolio optimization revisited", "Optimal Design of Multilayered Building Insulation", "Support Vector Machines with Conic Optimization", "Extra material: Luenberger\u2019s Investment Wheel", "6. Conic Optimization", "Extra material: Optimal Growth Portfolios with Risk Aversion", "Fleet assignment problem", "Robustness analysis of BIM production plan via simulations", "7. Accounting for Uncertainty: Optimization Meets Reality", "Robust BIM microchip production problem", "8. Robust Optimization - Single Stage Problems", "Pop-up shop", "Markowitz portfolio optimization with chance constraints", "Stock optimization for seafood distribution center", "Economic dispatch in energy systems", "9. Stochastic Optimization - Single Stage Problems", "Optimal power flow problem with recourse actions", "Two-stage production planning using constraint and column generation", "Extra: The farmer\u2019s problem and its variants", "Extra: Two-stage energy dispatch optimization with wind curtailment", "10. 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54, 55, 61, 62, 64, 68], "each": [2, 4, 5, 6, 7, 9, 10, 13, 17, 18, 20, 21, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 42, 44, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "made": [2, 3, 12, 13, 18, 22, 23, 33, 52, 56, 62, 64], "same": [2, 3, 4, 5, 7, 9, 11, 12, 15, 17, 21, 23, 24, 25, 26, 28, 29, 30, 31, 33, 35, 37, 39, 43, 47, 48, 51, 54, 58, 59, 61, 62, 64, 67, 68], "raw": [2, 4, 6, 13, 20, 23, 33, 34, 36, 39, 47, 52, 59, 61, 62, 64], "cost": [2, 3, 4, 5, 8, 9, 11, 13, 15, 20, 22, 23, 28, 31, 32, 33, 34, 35, 36, 40, 42, 43, 46, 51, 52, 56, 58, 59, 61, 62, 63, 64, 67], "10": [2, 4, 5, 7, 9, 11, 12, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 42, 43, 44, 46, 47, 48, 50, 51, 52, 56, 58, 59, 61, 62, 63, 64, 67, 68], "per": [2, 6, 9, 12, 13, 17, 18, 20, 23, 27, 29, 30, 33, 42, 43, 46, 52, 58, 61, 62, 63], "gram": [2, 23, 39, 47], "requir": [2, 3, 5, 6, 9, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 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25, 26, 27, 28, 30, 31, 33, 35, 36, 37, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 59, 61, 62, 63, 64], "hour": [2, 5, 17, 18, 20, 26, 51, 62], "b": [2, 4, 5, 6, 7, 8, 14, 15, 17, 19, 20, 21, 23, 24, 26, 29, 30, 33, 34, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 58, 61, 62, 64, 67, 68], "due": [2, 5, 9, 13, 23, 33, 34, 36, 38, 52, 53, 54, 56, 61, 63, 68], "market": [2, 5, 9, 15, 33, 35, 36, 44, 48, 50, 62, 63], "demand": [2, 3, 5, 8, 20, 22, 23, 28, 29, 30, 34, 36, 42, 43, 52, 53, 56, 59, 61, 62, 63, 64, 67, 68], "limit": [2, 4, 13, 17, 18, 20, 22, 23, 26, 30, 33, 34, 36, 40, 42, 46, 48, 50, 52, 59, 61, 62, 64, 68], "40": [2, 4, 5, 14, 17, 18, 20, 30, 31, 33, 42, 43, 46, 48, 56, 62, 63, 67, 68], "week": [2, 17, 31, 33], "v": [2, 4, 5, 9, 18, 22, 26, 28, 29, 31, 33, 34, 36, 39, 43, 47, 48, 51, 59, 61, 62, 64, 67, 68], "lower": [2, 4, 5, 9, 13, 26, 30, 33, 35, 36, 37, 42, 54, 56, 61, 63, 68], "unlimit": [2, 5, 20, 42, 62], "210": [2, 4, 5, 20, 62, 63, 67], "9": [2, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 24, 25, 26, 27, 28, 29, 30, 33, 34, 36, 39, 40, 42, 43, 47, 51, 52, 53, 54, 62, 64, 67], "summar": [2, 5, 17, 30, 34, 36, 39, 47, 56, 62, 63], "follow": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 47, 48, 50, 51, 52, 53, 54, 56, 58, 59, 61, 62, 63, 64, 67, 68], "tabl": [2, 5, 18, 20, 21, 23, 25, 26, 35, 39, 48, 51, 52, 54, 56, 58, 63, 68], "g": [2, 5, 6, 9, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 43, 46, 47, 51, 54, 58, 61, 64, 67], "hr": [2, 5, 18, 51], "2": [2, 5, 14, 15, 16, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 33, 35, 38, 39, 40, 42, 43, 44, 45, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "leq": [2, 3, 5, 6, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 54, 56, 57, 59, 61, 62, 64, 68], "weekli": [2, 17], "avail": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 62, 64, 67], "inventori": [2, 13, 23, 33, 43, 52, 56, 58], "ha": [2, 3, 4, 5, 9, 12, 13, 14, 17, 18, 20, 21, 23, 25, 26, 28, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 52, 54, 56, 58, 59, 61, 62, 64, 67], "shelf": 2, "life": [2, 21, 28, 41, 46, 51], "must": [2, 3, 4, 20, 24, 25, 26, 27, 30, 33, 34, 35, 36, 38, 46, 51, 61, 62, 63, 64, 67], "order": [2, 5, 12, 13, 17, 21, 23, 26, 28, 30, 34, 39, 40, 42, 47, 49, 51, 52, 56, 62], "advanc": [2, 3, 4, 27, 36, 43, 53, 61, 62, 64], "left": [2, 6, 7, 10, 12, 13, 14, 17, 18, 21, 25, 26, 27, 28, 30, 36, 37, 39, 42, 43, 44, 46, 47, 50, 51, 52, 58, 59, 61, 62], "over": [2, 5, 9, 11, 12, 17, 23, 26, 27, 28, 33, 34, 36, 39, 40, 42, 43, 44, 47, 52, 56, 62, 63, 67, 68], "end": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "discard": 2, "detail": [2, 6, 17, 22, 39, 46, 47, 56], "resourc": [2, 5, 15, 17, 25, 26, 28, 33, 42, 52, 61, 63, 67], "amount": [2, 5, 13, 15, 16, 17, 19, 21, 23, 26, 29, 30, 33, 35, 36, 38, 52, 53, 54, 57, 58, 59, 61, 62, 63, 68], "80": [2, 4, 5, 20, 30, 33, 34, 40, 44, 47, 50, 62, 63, 64, 67], "50": [2, 4, 5, 9, 10, 18, 20, 23, 30, 33, 34, 35, 36, 39, 42, 43, 46, 52, 54, 58, 62, 63, 64, 67, 68], "100": [2, 4, 5, 12, 18, 20, 21, 22, 23, 24, 30, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44, 47, 50, 52, 54, 58, 59, 61, 62, 63, 64, 67, 68], "want": [2, 9, 16, 22, 23, 30, 31, 33, 37, 38, 39, 45, 54, 57, 62, 68], "maxim": [2, 3, 4, 5, 6, 8, 10, 12, 16, 20, 22, 23, 27, 28, 33, 34, 35, 36, 38, 40, 42, 44, 45, 48, 50, 54, 57, 58, 62, 63, 67, 68], "its": [2, 4, 6, 7, 9, 10, 14, 21, 22, 28, 29, 37, 39, 42, 43, 47, 54, 61, 62, 67, 68], "gross": [2, 44, 48, 50], "profit": [2, 4, 5, 6, 8, 10, 12, 16, 20, 27, 30, 33, 36, 40, 42, 54, 62, 63, 67, 68], "much": [2, 6, 7, 13, 16, 18, 23, 25, 27, 28, 31, 42, 44, 46, 53, 61, 62, 64], "should": [2, 3, 6, 9, 15, 17, 18, 20, 21, 23, 27, 29, 31, 33, 36, 39, 41, 42, 44, 47, 51, 54, 56, 59, 61, 62, 63, 64, 67, 68], "mani": [2, 3, 5, 6, 19, 21, 22, 24, 25, 27, 28, 31, 33, 35, 39, 42, 43, 44, 47, 48, 50, 51, 52, 54, 56, 61, 62, 64, 68], "abov": [2, 5, 6, 7, 8, 9, 11, 13, 16, 18, 21, 23, 27, 28, 30, 31, 33, 34, 37, 42, 43, 45, 46, 47, 48, 50, 52, 54, 58, 59, 61, 62, 68], "describ": [2, 3, 5, 6, 8, 13, 17, 18, 21, 23, 25, 26, 27, 29, 30, 33, 34, 38, 39, 40, 42, 43, 45, 46, 50, 56, 57, 59, 64, 68], "optim": [2, 4, 5, 7, 10, 11, 12, 16, 17, 18, 22, 25, 26, 27, 28, 29, 30, 31, 33, 35, 40, 43, 48, 51, 52, 54, 56, 59, 65, 66, 68], "reformul": [2, 7, 9, 12, 44, 46, 47, 48, 50, 54], "involv": [2, 4, 6, 8, 11, 26, 35, 36, 39, 43, 44, 48, 50], "few": [2, 4, 5, 30, 31, 33, 67], "crucial": [2, 34], "element": [2, 4, 5, 6, 17, 28, 30, 39, 44, 46, 47, 61, 67, 68], "express": [2, 3, 5, 6, 10, 11, 12, 13, 14, 19, 20, 21, 23, 24, 28, 29, 30, 33, 34, 36, 39, 40, 43, 46, 47, 48, 50, 52, 54, 58, 59, 61, 63, 64, 67, 68], "function": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 17, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 48, 50, 51, 54, 57, 58, 59, 61, 62, 63, 64, 66, 68], "start": [2, 4, 6, 7, 9, 14, 18, 21, 23, 25, 26, 27, 31, 33, 35, 39, 42, 50, 54, 62], "point": [2, 5, 6, 7, 14, 19, 22, 25, 27, 29, 33, 36, 37, 39, 40, 43, 44, 47, 54, 58, 64, 68], "list": [2, 5, 6, 9, 17, 18, 20, 24, 26, 28, 31, 33, 34, 35, 42, 46, 47, 48, 50, 51, 52, 58, 59, 61, 62, 67, 68], "relev": [2, 5, 22, 23, 25, 33, 64, 67, 68], "hand": [2, 5, 6, 10, 16, 17, 18, 22, 28, 33, 35, 39, 47, 54, 56, 59], "quantiti": [2, 6, 11, 13, 23, 29, 34, 36, 42, 49, 52, 53, 54, 62], "modifi": [2, 4, 16, 22, 26, 27, 33, 35, 39, 54, 59, 61, 64, 67, 68], "achiev": [2, 9, 11, 17, 27, 38, 39, 47, 61, 67], "desir": [2, 3, 13, 17, 23, 28, 41, 52, 67], "outcom": [2, 15, 30, 39, 47, 48, 56, 59, 62], "while": [2, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 39, 40, 42, 45, 47, 50, 51, 52, 53, 54, 56, 58, 59, 61, 62, 64], "some": [2, 3, 4, 5, 6, 7, 14, 19, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 39, 40, 44, 46, 47, 48, 50, 54, 59, 61, 62, 64, 67, 68], "stage": [2, 4, 25, 36, 44, 48, 58, 61, 63, 67, 68], "mai": [2, 3, 4, 5, 11, 12, 13, 15, 16, 19, 22, 23, 25, 26, 27, 30, 33, 34, 35, 36, 39, 40, 42, 43, 47, 48, 52, 54, 57, 59, 63, 67], "prove": [2, 6], "redund": [2, 68], "later": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 40, 43, 44, 51, 52, 56, 58, 64, 68], "creat": [2, 7, 9, 13, 15, 21, 24, 25, 26, 27, 28, 31, 33, 35, 36, 39, 42, 43, 47, 48, 50, 56, 59, 62, 63, 67, 68], "comprehens": [2, 14, 39, 47], "us": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 48, 50, 51, 52, 56, 57, 59, 61, 63, 64, 65, 66], "s": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 42, 43, 45, 46, 47, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "For": [2, 3, 4, 6, 9, 12, 18, 19, 21, 22, 23, 25, 26, 28, 29, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 54, 56, 58, 59, 61, 62, 63, 64, 67, 68], "below": [2, 4, 9, 10, 11, 13, 16, 18, 22, 26, 28, 33, 34, 36, 39, 42, 43, 46, 47, 48, 54, 58, 59, 62, 64, 68], "symbol": [2, 9, 21, 30, 33, 35, 50, 56, 67, 68], "descript": [2, 15, 26, 30, 53, 54, 64], "upper": [2, 4, 5, 9, 10, 13, 18, 21, 26, 27, 30, 31, 33, 36, 37, 38, 42, 43, 46, 52, 54, 58, 67, 68], "bound": [2, 4, 5, 9, 10, 18, 20, 21, 24, 26, 27, 28, 31, 33, 35, 36, 38, 39, 42, 46, 51, 54, 59, 62, 63, 64, 67], "known": [2, 4, 5, 7, 9, 14, 21, 24, 25, 26, 27, 31, 36, 37, 38, 39, 43, 44, 46, 47, 50, 51, 56, 59, 61, 62, 63, 64], "x_m": [2, 4, 58], "0": [2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "x_a": [2, 4], "x_b": [2, 4], "y_u": [2, 4], "number": [2, 3, 4, 5, 6, 7, 10, 12, 16, 17, 18, 19, 21, 22, 23, 24, 25, 31, 33, 34, 35, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 68], "y_v": [2, 4], "next": [2, 3, 4, 5, 6, 8, 13, 14, 15, 17, 19, 21, 23, 26, 27, 28, 29, 30, 32, 33, 38, 41, 47, 48, 49, 51, 53, 55, 58, 59, 60, 61, 63, 64], "step": [2, 3, 5, 6, 9, 10, 13, 15, 23, 26, 33, 43, 48, 51, 52, 62, 67, 68], "formul": [2, 3, 5, 6, 7, 8, 9, 10, 11, 15, 19, 20, 21, 23, 25, 27, 28, 30, 32, 33, 34, 36, 37, 41, 46, 50, 53, 54, 56, 58, 59, 61, 64, 68], "measur": [2, 9, 12, 14, 17, 18, 27, 33, 36, 39, 47, 53], "valu": [2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 22, 23, 25, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 57, 58, 59, 61, 62, 64, 67, 68], "candid": [2, 3, 14, 26, 42], "In": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68], "case": [2, 3, 4, 5, 6, 7, 14, 17, 19, 20, 22, 25, 26, 28, 30, 33, 36, 37, 38, 39, 42, 44, 47, 48, 50, 54, 56, 58, 59, 64, 67, 68], "max": [2, 5, 6, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 25, 26, 27, 33, 34, 36, 38, 39, 40, 42, 44, 45, 46, 47, 48, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67], "text": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "turn": [2, 3, 19, 26, 28, 39, 41, 47, 51, 59, 64], "equal": [2, 4, 6, 8, 12, 13, 14, 15, 17, 19, 20, 25, 27, 28, 29, 30, 31, 33, 36, 37, 38, 40, 43, 44, 46, 48, 50, 51, 54, 56, 58, 59, 61, 62, 64], "between": [2, 4, 5, 6, 7, 9, 12, 17, 18, 22, 25, 26, 29, 30, 33, 34, 35, 36, 37, 38, 39, 41, 44, 45, 46, 47, 48, 51, 59, 61, 63, 64, 67, 68], "revenu": [2, 4, 5, 11, 12, 40, 56, 63, 67], "oper": [2, 3, 15, 21, 25, 26, 33, 34, 36, 39, 40, 42, 43, 46, 51, 56, 61, 62, 64, 67], "begin": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "align": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "linear": [2, 3, 4, 6, 7, 9, 11, 12, 13, 14, 15, 16, 20, 21, 22, 24, 27, 33, 34, 35, 36, 37, 40, 41, 54, 58, 59, 61, 62, 65], "written": [2, 4, 5, 15, 39, 43, 56, 57, 67, 68], "term": [2, 3, 4, 5, 7, 16, 17, 20, 21, 22, 23, 26, 30, 31, 33, 35, 36, 37, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 56, 58, 59, 61, 62, 64], "As": [2, 4, 5, 6, 9, 17, 23, 26, 28, 33, 35, 36, 39, 43, 47, 48, 51, 56, 58, 62, 64, 68], "shown": [2, 4, 36, 39, 43, 48, 68], "here": [2, 4, 5, 7, 12, 13, 17, 18, 20, 21, 26, 33, 35, 36, 37, 39, 43, 44, 48, 50, 51, 52, 54, 56, 57, 59, 62, 64, 67, 68], "algebra": [2, 4, 24, 39, 40, 43], "combin": [2, 4, 5, 23, 24, 33, 36, 46, 51, 59, 61, 62, 67, 68], "refer": [2, 3, 5, 6, 7, 8, 18, 19, 22, 25, 29, 30, 33, 36, 37, 39, 44, 47, 61, 68], "name": [2, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 18, 21, 22, 23, 26, 28, 30, 33, 37, 39, 40, 42, 45, 47, 56, 58, 61, 64], "when": [2, 4, 5, 6, 7, 13, 14, 17, 18, 19, 21, 22, 26, 27, 30, 33, 34, 35, 36, 38, 39, 40, 43, 44, 47, 48, 49, 56, 58, 59, 61, 62, 63, 64, 66, 67], "multipl": [2, 3, 13, 15, 23, 25, 29, 30, 33, 35, 36, 42, 68], "place": 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25, 26, 27, 33, 34, 47, 56, 61, 62, 68], "inequ": [2, 6, 8, 10, 36, 44, 54, 61], "overal": [2, 6, 15, 17, 36, 46], "now": [2, 4, 5, 6, 9, 13, 14, 17, 18, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 38, 39, 42, 43, 44, 45, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 67], "readi": [2, 33, 35, 43, 47, 62], "full": [2, 7, 14, 18, 27, 30, 36, 37, 39, 42, 46, 47, 50, 54, 62, 68], "canon": [2, 8, 38], "wai": [2, 3, 4, 5, 6, 7, 11, 19, 21, 22, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 38, 43, 45, 47, 48, 50, 51, 57, 59, 61, 62, 64, 67, 68], "minim": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 21, 22, 23, 25, 26, 29, 31, 33, 34, 37, 39, 42, 43, 46, 47, 51, 52, 54, 59, 61, 63, 64, 68], "lastli": [2, 3, 62, 64], "quad": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 25, 27, 28, 30, 31, 33, 35, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 56, 57, 58, 61, 62, 67, 68], "t": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "nonumb": [2, 5, 28], "customari": [2, 6, 47], "under": [2, 4, 5, 9, 15, 17, 20, 21, 22, 23, 35, 39, 52, 56, 59, 62, 63, 67], "so": [2, 3, 4, 5, 6, 7, 10, 17, 18, 23, 25, 26, 27, 28, 33, 34, 36, 37, 38, 39, 41, 42, 45, 46, 47, 51, 53, 54, 59, 62, 64, 67], "clearli": [2, 5, 14, 17, 22, 23, 27, 36, 51, 62, 64], "distinguish": [2, 33, 64, 68], "paramet": [2, 4, 5, 23, 26, 27, 30, 31, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 53, 54, 56, 58, 59, 61, 62, 64, 67], "might": [2, 15, 19, 25, 31, 33, 34, 35, 36, 39, 41, 53, 56, 61, 67], "also": [2, 3, 4, 5, 6, 9, 11, 13, 15, 17, 21, 22, 23, 27, 28, 33, 34, 36, 38, 39, 40, 41, 43, 51, 54, 57, 61, 62, 64, 68], "letter": [2, 24, 43, 62], "throughout": [2, 4, 6, 33, 62, 68], "websit": 2, "howev": [2, 5, 6, 7, 15, 16, 19, 22, 23, 28, 31, 32, 33, 34, 35, 36, 39, 41, 43, 45, 46, 47, 50, 51, 54, 56, 59, 61, 62, 64, 68], 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28, 30, 33, 35, 36, 39, 42, 47, 52, 61, 62, 64], "immedi": [2, 26, 33], "clear": [2, 10, 16, 41, 59, 62, 68], "what": [2, 6, 15, 17, 21, 26, 33, 35, 36, 40, 44, 48, 49, 53, 54, 56, 59, 62, 63, 64, 67], "exactli": [2, 9, 29, 31, 34, 43, 47, 51, 53, 54, 62, 64], "where": [2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 33, 34, 35, 36, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 54, 56, 58, 59, 61, 62, 64, 67, 68], "algorithm": [2, 3, 25, 31, 33, 43, 51, 62], "come": [2, 5, 9, 15, 28, 39, 41, 47, 62], "plai": [2, 6, 41, 47, 48, 51, 62], "thei": [2, 4, 6, 18, 19, 21, 22, 23, 28, 30, 33, 35, 36, 39, 41, 42, 50, 51, 53, 59, 61, 62, 64, 67, 68], "procedur": [2, 42, 54, 62], "long": [2, 5, 9, 17, 19, 21, 33, 44, 48, 58, 62, 68], "standard": [2, 4, 6, 7, 8, 9, 21, 34, 36, 39, 40, 47, 57, 68], "fashion": [2, 5, 51], "practition": [2, 50], "often": [2, 5, 19, 26, 30, 32, 34, 53, 62, 64, 68], "boil": 2, "down": [2, 16, 27, 28, 34, 44, 62, 63], "pass": [2, 4, 5, 18, 22, 23, 26, 32, 39, 47, 67], "softwar": [2, 20], "regardless": [2, 5, 6, 13, 23, 33, 48, 52, 58], "wa": [2, 4, 9, 14, 17, 28, 30, 33, 34, 36, 41, 42, 43, 44, 45, 48, 50, 51, 54, 56, 61, 62], "origin": [2, 3, 4, 5, 6, 7, 9, 10, 12, 16, 17, 25, 28, 34, 36, 42, 43, 45], "stori": [2, 44, 48], "behind": 2, "To": [2, 4, 6, 17, 18, 21, 23, 25, 26, 27, 28, 33, 34, 36, 39, 42, 43, 46, 47, 50, 51, 54, 56, 57, 58, 59, 61, 62, 64, 67], "do": [2, 5, 6, 17, 22, 27, 28, 31, 33, 34, 35, 36, 40, 43, 51, 54, 59, 61, 62, 64, 67, 68], "interfac": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 40, 43, 51, 52, 56, 58, 64, 68], "commun": [2, 4, 14, 17, 25, 63], "adopt": [2, 6, 20, 30, 39, 44, 46, 54], "python": [2, 4, 6, 7, 16, 17, 22, 26, 28, 30, 33, 37, 39, 40, 42, 43, 47, 51, 59, 64, 67, 68], "base": [2, 7, 13, 14, 20, 24, 26, 30, 33, 34, 37, 39, 40, 44, 47, 50, 52, 53, 59, 61, 62, 67, 68], "correspond": [2, 3, 5, 6, 7, 9, 10, 11, 13, 16, 17, 21, 23, 24, 25, 27, 31, 33, 34, 39, 42, 43, 45, 46, 47, 51, 54, 58, 61, 62, 64, 68], "carri": [2, 18, 34], "out": [2, 3, 18, 25, 27, 28, 30, 33, 41, 42, 51, 59, 61, 64], "broad": 3, "them": [3, 4, 5, 6, 10, 18, 19, 23, 28, 33, 36, 38, 41, 50, 51, 54, 57, 61, 62, 64], "dedic": [3, 54], "consist": [3, 4, 7, 9, 14, 17, 22, 26, 31, 33, 34, 35, 37, 39, 47, 50, 67, 68], "three": [3, 5, 17, 21, 22, 23, 25, 26, 35, 36, 50, 53, 56, 58, 63, 67], "action": [3, 64, 65], "choic": [3, 5, 15, 18, 20, 26, 34, 36, 39, 42, 46, 59, 67, 68], "whether": [3, 6, 18, 20, 21, 25, 31, 33, 34, 35, 41, 43, 46, 51, 59, 62, 64], "new": [3, 4, 5, 6, 7, 14, 16, 17, 20, 25, 26, 27, 28, 30, 33, 34, 36, 39, 40, 42, 43, 45, 54, 56, 62, 64, 67, 68], "manufactur": [3, 6, 16, 25, 29, 40, 62], "facil": [3, 8, 19, 20, 25, 26, 29, 36, 43], "suppli": [3, 5, 28, 29, 30, 34, 36, 59, 64], "rout": [3, 33, 51, 53], "product": [3, 4, 8, 19, 22, 27, 29, 34, 36, 39, 41, 42, 43, 47, 48, 53, 55, 59, 61, 63, 64, 65], "evalu": [3, 7, 39, 47, 62], "i": [3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 19, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 57, 58, 59, 61, 62, 64, 68], "e": [3, 14, 17, 21, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 37, 38, 39, 40, 42, 43, 44, 46, 47, 48, 50, 51, 56, 57, 58, 59, 61, 62, 64, 67, 68], "earlier": [3, 4, 12, 26, 39, 43, 45, 52, 56, 59], "specifi": [3, 4, 5, 6, 7, 18, 25, 26, 28, 29, 33, 35, 36, 38, 39, 42, 43, 45, 47, 48, 57, 64, 67, 68], "custom": [3, 22, 26, 30, 33, 36, 39, 42, 53, 59, 68], "restrict": [3, 17, 19, 28, 31, 34, 48, 50, 51, 54, 67, 68], "possibl": [3, 4, 5, 7, 8, 10, 15, 16, 18, 19, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39, 44, 47, 51, 54, 57, 58, 59, 61, 62, 63, 64, 67, 68], "allow": [3, 5, 6, 18, 23, 25, 27, 30, 33, 34, 35, 40, 43, 45, 50, 51, 54, 59, 61, 67], "budget": [3, 13, 42, 52, 62], "import": [3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 58, 62, 63, 68], "met": [3, 34, 36, 59], "capac": [3, 13, 18, 23, 27, 28, 29, 34, 40, 52, 59, 64, 68], "warehous": [3, 23, 43, 53, 58, 68], "exceed": [3, 28, 33, 36, 59], "both": [3, 4, 5, 6, 7, 13, 16, 18, 20, 22, 25, 27, 31, 33, 34, 36, 38, 39, 42, 43, 44, 47, 62, 65], "defin": [3, 4, 5, 6, 7, 9, 10, 13, 17, 19, 20, 21, 22, 25, 30, 31, 34, 36, 37, 39, 40, 42, 43, 47, 48, 51, 54, 59, 61, 62, 64, 67, 68], "feasibl": [3, 4, 6, 10, 16, 22, 28, 34, 40, 42, 44, 46, 48, 50, 51, 58, 61, 62, 64], "region": [3, 6, 22, 30, 34], "set": [3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 48, 50, 51, 52, 56, 57, 58, 59, 61, 62, 63, 64, 67], "best": [3, 4, 6, 14, 28, 33, 34, 46, 59, 62, 64, 68], "global": [3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 64, 68], "optimum": [3, 36, 40, 46], "appli": [3, 4, 5, 18, 23, 34, 36, 39, 42, 43, 46, 50, 61, 64, 67], "relat": [3, 4, 20, 23, 27, 31, 33, 47, 54, 59, 61, 62], "fundament": [3, 9, 36, 67], "question": [3, 6, 15, 21, 33, 39, 43, 53, 56, 61, 63], "translat": [3, 4, 5, 21, 23, 30, 33, 42, 46], "real": [3, 4, 5, 7, 14, 19, 26, 28, 34, 35, 37, 41, 42, 44, 48, 50, 51, 53, 54, 56, 59, 61, 62], "abstract": [3, 5, 6, 67], "represent": [3, 26, 39, 46, 47], "Not": [3, 33], "aspect": [3, 6, 8, 9, 19, 32, 41, 49, 55, 60, 65], "taken": [3, 31, 33, 39, 61, 62, 68], "account": [3, 12, 18, 23, 27, 33, 34, 35, 44, 48, 49, 54, 56, 58, 61, 62, 64, 67], "typic": [3, 26, 34, 56, 59, 62, 67, 68], "signific": [3, 4, 6, 40, 43, 44, 47, 48, 50], "impact": [3, 14, 36, 53, 63], "approach": [3, 4, 6, 7, 9, 12, 14, 16, 20, 21, 22, 24, 25, 28, 31, 36, 42, 46, 48, 50, 59, 61, 62, 67], "There": [3, 4, 17, 20, 21, 23, 24, 28, 33, 34, 36, 42, 43, 46, 56, 62, 67, 68], "equival": [3, 4, 6, 21, 22, 23, 31, 39, 43, 46, 51, 54, 57, 68], "conceptu": 3, "comput": [3, 4, 5, 6, 9, 14, 15, 21, 23, 27, 28, 33, 34, 35, 36, 39, 42, 43, 47, 48, 50, 52, 56, 61, 63, 64, 67, 68], "complex": [3, 4, 5, 15, 21, 26, 27, 33, 35, 42, 67, 68], "interpret": [3, 5, 15, 31, 40], "after": [3, 4, 7, 27, 28, 33, 35, 37, 44, 51, 54, 56, 61, 62, 63, 64, 67], "back": [3, 7, 27, 31, 33, 35, 39, 47, 62, 67], "These": [3, 5, 15, 17, 25, 29, 33, 34, 36, 39, 43, 44, 50, 51, 56, 59, 67, 68], "treat": [3, 26, 28, 36, 53, 64], "continu": [3, 13, 21, 23, 27, 36, 39, 46, 47, 52, 58, 64], "process": [3, 5, 27, 33, 36, 39, 40, 43, 47, 51, 54, 67, 68], "sequenti": 3, "final": [3, 4, 5, 9, 13, 21, 26, 27, 33, 35, 36, 43, 61], "impract": [3, 33], "adjust": [3, 5, 9, 33, 34, 40, 59, 61, 64], "certain": [3, 19, 28, 30, 33, 39, 43, 47, 54, 59, 64], "properti": [3, 6, 28, 41, 45, 46, 48, 50, 59], "cannot": [3, 6, 13, 17, 19, 21, 26, 31, 33, 34, 46, 51, 52, 58, 62, 64], "effici": [3, 9, 12, 25, 26, 28, 33, 45, 47, 50, 54, 68], "perhap": [3, 24, 30, 42], "re": [3, 68], "includ": [3, 4, 8, 9, 18, 19, 21, 23, 24, 25, 26, 27, 30, 32, 33, 35, 36, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 54, 57, 59, 61, 62, 63, 64, 66, 67, 68], "well": [3, 4, 7, 9, 14, 21, 24, 25, 41, 42, 44, 46, 48, 58, 63, 68], "except": [3, 17, 22, 31, 64, 67, 68], "mathematician": 3, "studi": [3, 15, 25, 26, 33, 36, 42, 46, 58, 59, 68], "alwai": [3, 5, 12, 13, 16, 18, 34, 37, 38, 39, 44, 45, 47, 48, 53, 54, 57, 67], "flaw": 3, "challeng": [3, 6, 24, 25, 35, 36, 39, 42], "f": [3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "x": [3, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, 25, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "mathbb": [3, 6, 7, 8, 16, 19, 20, 23, 34, 37, 38, 39, 42, 43, 44, 47, 48, 50, 54, 56, 57, 58, 59, 61, 64], "r": [3, 5, 6, 7, 8, 9, 17, 18, 19, 21, 23, 24, 25, 26, 30, 33, 34, 35, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 54, 56, 57, 58, 61, 62, 63, 64, 67], "subseteq": [3, 19, 31, 34, 51], "n": [3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 30, 31, 33, 34, 35, 36, 37, 38, 39, 42, 43, 44, 45, 47, 48, 50, 51, 52, 54, 57, 58, 59, 62, 68], "being": [3, 4, 6, 8, 31, 34, 36, 38, 47, 51, 64], "y": [3, 5, 6, 7, 9, 10, 14, 18, 20, 22, 23, 24, 25, 27, 28, 29, 30, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 50, 54, 56, 58, 59, 62, 63, 64, 67, 68], "geq": [3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 19, 20, 21, 23, 25, 26, 28, 29, 30, 33, 34, 35, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50, 54, 57, 58, 59, 61, 62, 64, 67], "foral": [3, 5, 7, 9, 11, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39, 42, 43, 45, 47, 48, 50, 51, 54, 56, 59, 61, 62, 64], "least": [3, 8, 13, 14, 18, 21, 23, 31, 34, 36, 41, 42, 51, 52, 62, 63], "good": [3, 4, 5, 14, 15, 22, 25, 29, 31, 33, 38, 48, 50, 56, 62, 63, 68], "similarli": [3, 6, 7, 23, 34, 37], "chang": [3, 5, 9, 12, 15, 18, 25, 26, 27, 28, 30, 31, 33, 34, 35, 36, 37, 38, 40, 45, 51, 53, 54, 57, 59, 61, 62, 67, 68], "last": [3, 13, 18, 21, 23, 27, 36, 38, 51, 52, 54, 63, 64], "min": [3, 6, 7, 8, 9, 10, 11, 14, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 36, 37, 39, 42, 43, 46, 47, 48, 51, 54, 56, 58, 59, 61, 62, 64, 68], "lead": [3, 9, 16, 20, 21, 22, 23, 24, 26, 33, 36, 39, 41, 46, 47, 54, 59, 62, 64, 68], "techniqu": [3, 7, 11, 14, 20, 21, 26, 27, 30, 39, 43, 50, 59, 61, 62], "introductori": [3, 65], "present": [3, 4, 5, 6, 7, 9, 14, 18, 30, 33, 34, 35, 36, 37, 39, 40, 42, 44, 45, 47, 48, 50, 51, 63, 64, 67, 68], "context": [3, 15, 17, 29, 31, 57, 62, 64], "plan": [3, 4, 5, 11, 12, 15, 17, 18, 19, 23, 25, 43, 53, 56, 65], "explain": [3, 14, 34, 36, 47, 59, 64], "structur": [3, 5, 6, 16, 20, 28, 32, 33, 42, 46, 61, 62, 64, 67, 68], "formal": [3, 19], "implement": [3, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 21, 24, 25, 30, 32, 33, 34, 36, 37, 38, 41, 42, 43, 46, 48, 49, 53, 55, 56, 57, 59, 60, 62, 65, 67, 68], "second": [3, 4, 5, 7, 12, 23, 25, 26, 30, 31, 33, 34, 35, 36, 38, 39, 42, 47, 58, 61, 62, 63, 64, 65], "serv": [3, 4, 5, 22, 29, 51, 62], "tutori": [3, 17, 42, 44, 48, 68], "discuss": [3, 30, 39, 43, 47, 53, 62], "featur": [3, 4, 5, 6, 7, 8, 19, 21, 22, 25, 26, 27, 29, 36, 37, 44, 50, 58, 67, 68], "third": [3, 4, 5, 30], "go": [3, 6, 7, 8, 9, 19, 26, 28, 32, 34, 36, 37, 41, 49, 51, 53, 55, 60, 61, 62, 63, 67], "about": [3, 6, 8, 18, 19, 22, 28, 32, 33, 41, 49, 53, 55, 56, 59, 60, 61, 62], "mathemat": [4, 15, 22, 27, 28, 30, 33, 36, 41, 42, 45, 46, 50, 51, 56, 68], "integr": [4, 16, 40, 51, 66, 67, 68], "program": [4, 14, 15, 24, 25, 27, 42, 43, 47, 48, 50, 63, 66, 68], "environ": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "It": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 43, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64], "enabl": [4, 5, 32, 33, 43, 47, 67], "user": [4, 28, 33, 39, 40, 47, 63, 68], "varieti": [4, 34], "project": [4, 36, 39, 43, 47, 66, 68], "ti": 4, "vendor": 4, "class": [4, 5, 6, 8, 11, 17, 21, 25, 28, 32, 39, 47, 67, 68], "constantli": 4, "evolv": 4, "through": [4, 7, 26, 29, 30, 33, 34, 39, 46, 51, 59, 61, 67], "contribut": [4, 9, 14, 24], "parti": [4, 30, 33], "small": [4, 5, 6, 13, 17, 20, 28, 29, 31, 33, 35, 36, 40, 42, 61, 62, 68], "solverfactori": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "direct": [4, 7, 10, 25, 29, 30, 34, 35, 42, 43, 51, 54], "compon": [4, 5, 6, 7, 9, 20, 21, 23, 38, 43, 47, 57, 68], "directli": [4, 5, 9, 14, 21, 27, 30, 34, 43, 46, 47, 56], "simplic": [4, 31, 34], "method": [4, 5, 7, 9, 20, 21, 27, 32, 33, 39, 42, 46, 47, 61, 62, 67, 68], "reus": [4, 23, 26], "anoth": [4, 11, 13, 19, 21, 23, 24, 26, 33, 35, 38, 39, 40, 47, 51, 54, 57, 64], "write": [4, 26, 30, 34, 35, 54, 66, 68], "more": [4, 5, 6, 7, 9, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 30, 33, 34, 35, 36, 39, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 58, 59, 61, 62, 63, 64, 67, 68], "driven": [4, 53], "decor": [4, 5, 6, 10, 17, 30], "design": [4, 5, 9, 21, 24, 25, 30, 36, 37, 39, 49, 64, 67], "unfamiliar": [4, 68], "current": [4, 6, 15, 18, 20, 30, 33, 36, 48, 53, 54, 62, 64, 67], "offer": [4, 6, 22, 23, 30, 33, 35, 44, 48, 59, 62, 68], "improv": [4, 5, 17, 36, 39, 43, 44, 46, 63], "readabl": [4, 6, 17, 30], "rel": [4, 6, 17, 18, 20, 33, 35, 45, 62, 67], "recent": [4, 36, 42, 44, 46, 48, 50, 68], "collect": [4, 5, 7, 8, 11, 14, 15, 25, 26, 30, 31, 34, 35, 37, 39, 47, 62, 68], "intend": [4, 17, 68], "cloud": 4, "person": [4, 14, 26, 43], "verifi": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 64], "appropri": [4, 5, 39, 47], "done": [4, 5, 21, 26, 36, 39, 56, 62], "session": 4, "high": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 42, 46, 47, 50, 51, 52, 54, 56, 58, 59, 61, 62, 64, 68], "mix": [4, 8, 16, 20, 27, 36, 41, 42, 54, 59], "integ": [4, 8, 13, 14, 16, 20, 22, 23, 24, 27, 28, 36, 41, 42, 56, 59, 68], "assum": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 45, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "been": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 56, 57, 58, 59, 63, 64, 67, 68], "previous": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 44, 45, 46, 47, 48, 51, 52, 56, 57, 58, 59, 61, 64, 67, 68], "suitabl": [4, 21, 31, 39, 47, 67], "coin": 4, "OR": [4, 25, 26, 27, 43], "cbc": [4, 22, 26, 27, 28, 61, 62, 63, 67], "glpk": 4, "cplex": [4, 22, 54], "gurobi": [4, 22, 28, 43, 54], "mosek": [4, 22, 36, 37, 38, 39, 43, 44, 45, 46, 47, 48, 50, 57, 59], "elsewher": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 48, 51, 52, 56, 57, 58, 59, 64, 68], "via": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 48, 51, 53, 56, 57, 58, 59, 64], "appsi": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 40, 51, 52, 56, 58, 64], "modul": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67], "test": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 39, 40, 42, 47, 50, 51, 52, 56, 58, 64, 67, 68], "store": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 39, 40, 43, 47, 51, 52, 56, 58, 61, 62, 64, 67, 68], "sy": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 48, 50, 51, 52, 56, 57, 58, 59, 61, 62, 63, 64, 67], "pip": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67], "dev": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64], "null": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64], "highspi": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 51, 52, 54, 56, 58, 61, 62, 63, 64], "appsi_high": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 51, 52, 54, 56, 58, 61, 62, 63, 64], "assert": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 64], "commonli": [4, 9, 20, 36, 39, 47, 68], "although": [4, 19, 28, 54], "previou": [4, 5, 10, 13, 15, 23, 32, 33, 34, 39, 43, 47, 53, 62], "mention": [4, 6, 11], "again": [4, 23, 28, 33, 34, 35, 39, 45, 47, 62], "emphas": [4, 68], "uniform": [4, 6, 27, 30, 36, 43, 48, 58, 61, 62, 68], "maintain": [4, 5, 6, 25, 33, 43, 64, 68], "alia": 4, "instanc": [4, 5, 6, 13, 22, 28, 29, 39, 42, 44, 47, 56, 59, 64, 67, 68], "short": [4, 9, 26, 33, 34, 35, 36, 38, 43, 50, 59, 67], "sinc": [4, 6, 10, 17, 18, 22, 23, 25, 28, 31, 34, 37, 39, 42, 43, 45, 46, 47, 53, 54, 58, 59, 61, 62, 64], "prefix": 4, "accept": [4, 5, 21, 26, 30, 33, 39, 48, 50, 59, 62, 68], "option": [4, 5, 9, 25, 26, 31, 33, 39, 40, 47, 61, 62, 67, 68], "string": [4, 30, 33, 39, 47, 67], "argument": [4, 5, 23, 31, 36, 39, 47, 54, 61, 62, 67, 68], "subsequ": [4, 7, 9, 14, 26, 33, 34, 36, 37, 42, 51, 56, 68], "time": [4, 5, 6, 8, 9, 17, 18, 20, 22, 23, 25, 27, 28, 31, 34, 35, 36, 37, 39, 41, 42, 43, 47, 48, 50, 51, 52, 53, 56, 59, 61, 62, 64, 67, 68], "construct": [4, 5, 9, 10, 14, 23, 26, 30, 33, 38, 39, 42, 43, 46, 47, 51, 61, 64, 67, 68], "altern": [4, 6, 7, 12, 14, 15, 20, 30, 31, 44, 47, 64, 68], "abstractmodel": 4, "But": [4, 15, 26, 30, 36, 39, 42, 47, 64], "normal": [4, 9, 35, 36, 39, 47, 48, 50, 51, 57, 68], "displai": [4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 21, 23, 25, 28, 29, 33, 36, 37, 39, 40, 42, 43, 45, 46, 47, 51, 52, 56, 57, 61, 62, 63, 67], "content": [4, 33, 36, 43, 44, 48], "At": [4, 13, 17, 18, 25, 26, 35, 44, 52, 63], "major": [4, 30], "empti": 4, "none": [4, 5, 14, 22, 25, 26, 28, 29, 30, 33, 34, 37, 39, 43, 44, 47, 52, 59, 64, 67], "var": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 45, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "assign": [4, 5, 15, 17, 18, 19, 21, 24, 28, 30, 31, 33, 35, 39, 40, 42, 47, 53, 64], "valid": [4, 39, 44, 47, 68], "identifi": [4, 5, 18, 19, 23, 25, 29, 33, 35, 36, 39, 42, 43, 44, 46, 47, 64, 68], "dot": [4, 6, 7, 9, 13, 14, 15, 19, 23, 27, 30, 31, 33, 37, 38, 39, 43, 44, 45, 46, 47, 52, 57, 59, 61], "notat": [4, 24, 33, 35, 39, 61, 62], "chosen": [4, 38], "reflect": [4, 12], "keyword": [4, 39, 47, 68], "By": [4, 5, 9, 14, 28, 30, 35, 54, 68], "default": [4, 5, 6, 9, 17, 18, 26, 30, 31, 33, 36, 39, 47, 64, 68], "nonnegativer": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23, 28, 29, 30, 33, 34, 35, 36, 37, 38, 39, 40, 42, 45, 52, 54, 57, 58, 59, 61, 62, 64, 67, 68], "nonnegativeinteg": [4, 16, 23, 28, 42, 54, 56], "binari": [4, 17, 19, 20, 21, 22, 24, 25, 27, 28, 31, 34, 41, 42, 46, 47, 51, 54, 59, 62, 64, 65], "tupl": [4, 5, 6, 17, 26, 33, 42, 67], "contain": [4, 5, 7, 17, 20, 22, 34, 37, 39, 42, 47, 54, 59, 64, 67, 68], "fix": [4, 5, 8, 9, 13, 14, 17, 18, 25, 28, 30, 34, 37, 43, 44, 46, 48, 50, 58, 59, 61, 62, 63, 64, 67], "placehold": [4, 28], "unknown": [4, 36, 38, 39, 57, 58, 61, 62], "show": [4, 5, 7, 9, 10, 13, 14, 17, 18, 20, 21, 22, 23, 25, 26, 28, 30, 31, 33, 34, 35, 36, 37, 38, 39, 42, 43, 44, 45, 46, 48, 50, 51, 52, 53, 54, 56, 58, 59, 62, 64, 67, 68], "yet": [4, 54], "updat": [4, 18, 21, 26, 36, 37, 38, 39, 40, 43, 45, 47, 52, 53, 54], "size": [4, 5, 7, 9, 13, 14, 17, 18, 21, 25, 30, 33, 36, 37, 38, 39, 42, 43, 44, 45, 47, 50, 51, 52, 54, 56, 58, 59, 68], "index": [4, 5, 6, 7, 9, 13, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 30, 33, 34, 35, 36, 37, 39, 40, 42, 43, 46, 47, 50, 51, 52, 56, 59, 61, 63, 67], "kei": [4, 5, 6, 9, 22, 26, 29, 30, 31, 33, 34, 37, 42, 48, 50, 51, 52, 56, 58, 59, 61, 62, 64, 67, 68], "stale": [4, 5, 30, 37], "fals": [4, 5, 6, 9, 17, 20, 21, 26, 28, 30, 31, 34, 36, 37, 39, 42, 43, 47, 51, 54, 58, 59, 61, 62, 64, 67], "true": [4, 5, 7, 9, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 46, 47, 48, 50, 51, 52, 54, 58, 59, 61, 62, 63, 64, 67, 68], "formula": [4, 39, 44, 48, 58], "respect": [4, 5, 6, 9, 20, 23, 25, 26, 28, 29, 30, 33, 34, 36, 39, 42, 48, 54, 59, 62, 63, 64, 67], "print": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44, 46, 47, 48, 50, 51, 52, 54, 56, 58, 59, 61, 62, 63, 64, 67], "could": [4, 6, 9, 15, 16, 19, 26, 27, 28, 33, 35, 36, 37, 38, 42, 43, 45, 54, 56, 59, 62, 63, 64, 68], "expr": [4, 6, 10, 11, 12, 16, 36, 47, 54, 58, 61, 62, 68], "sens": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 44, 45, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "releas": [4, 21, 51], "With": [4, 13, 20, 23, 33, 36, 44, 47, 62, 64], "def": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "m": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 67, 68], "return": [4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 67, 68], "behavior": [4, 36, 50], "line": [4, 6, 7, 9, 21, 22, 33, 34, 40, 46, 58, 64, 68], "take": [4, 5, 6, 10, 11, 14, 16, 18, 19, 21, 23, 25, 26, 27, 31, 33, 34, 35, 36, 38, 39, 43, 44, 45, 47, 51, 53, 54, 56, 57, 58, 59, 61, 62, 64, 67], "add": [4, 6, 11, 13, 17, 21, 22, 26, 27, 28, 30, 33, 35, 39, 40, 42, 44, 48, 51, 54, 58, 59, 62, 68], "attribut": [4, 5, 9, 14, 33, 34, 42, 43, 61, 67, 68], "effect": [4, 5, 26, 27, 29, 44, 46, 48, 51, 67], "tag": [4, 51, 68], "insert": [4, 5, 30, 43, 44], "syntax": [4, 43, 54, 68], "logic": [4, 5, 6, 13, 16, 20, 21, 23, 24, 33, 52, 54, 68], "separ": [4, 22, 26, 33, 34, 36, 39, 41, 46, 47, 59, 61, 64, 68], "less": [4, 17, 22, 25, 26, 33, 47, 48, 50, 59, 62, 68], "than": [4, 5, 6, 12, 13, 15, 16, 17, 18, 19, 22, 25, 26, 27, 28, 30, 31, 33, 34, 36, 38, 39, 42, 44, 47, 48, 50, 51, 52, 54, 56, 58, 59, 61, 62], "greater": [4, 15, 16, 17, 50], "raw_materi": 4, "labor_a": [4, 68], "labor_b": [4, 68], "output": [4, 22, 23, 34, 39, 47, 51, 61, 64, 67], "whenev": [4, 17, 26, 31, 62], "declar": [4, 5, 34, 61, 64, 68], "activ": [4, 5, 17, 20, 30, 33, 34, 54, 64], "bodi": [4, 5, 30, 47], "inf": [4, 5, 62], "fulli": [4, 6, 28, 54, 59], "tee": [4, 6], "caus": [4, 5, 36, 67], "debug": 4, "aris": [4, 35, 36], "result": [4, 6, 9, 14, 15, 17, 18, 22, 23, 27, 28, 31, 33, 34, 35, 36, 39, 42, 43, 44, 45, 47, 48, 50, 52, 54, 56, 57, 59, 61, 62, 63, 64, 67, 68], "date": [4, 5, 7, 9, 17, 20, 22, 23, 35, 39, 50], "2023": [4, 5, 17, 18, 20, 21, 22, 23, 25, 27, 28, 33, 35, 39, 59], "05": [4, 5, 13, 17, 18, 20, 21, 22, 23, 33, 35, 36, 39, 42, 43, 45, 46, 47, 48, 50, 52, 54, 57, 58, 61, 64], "16": [4, 5, 14, 17, 18, 20, 21, 22, 23, 28, 33, 35, 36, 39, 42, 43, 45, 46, 47, 50, 51, 61, 63, 64], "git": [4, 5, 17, 20, 21, 22, 23, 35, 39], "hash": [4, 5, 17, 20, 21, 22, 23, 35, 39], "594fa5a9d": [4, 5, 17, 20, 21, 22, 23, 35, 39], "copyright": [4, 5, 17, 20, 21, 22, 23, 35, 39], "c": [4, 5, 6, 8, 10, 11, 15, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 33, 35, 36, 38, 39, 40, 42, 43, 45, 46, 47, 48, 50, 51, 54, 56, 57, 58, 61, 62, 63, 67, 68], "mit": [4, 5, 17, 20, 21, 22, 23, 35, 39], "licenc": [4, 5, 17, 20, 21, 22, 23, 35, 39], "warn": [4, 22, 28, 39, 47, 59, 64], "No": [4, 17, 27, 42, 54, 56, 62], "semi": [4, 37, 38, 39, 45, 47, 57], "non": [4, 6, 8, 10, 16, 23, 24, 25, 26, 29, 33, 35, 36, 38, 39, 42, 43, 44, 47, 48, 54, 56, 59, 61, 67, 68], "presolv": [4, 22], "row": [4, 5, 6, 14, 21, 33, 34, 35, 51, 62, 64, 68], "col": 4, "nonzero": [4, 52], "reduct": 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