IOE591_script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8ed418e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
    "from gurobipy import GRB, Model, quicksum, Column\n",
    "\n",
    "# To ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Plotting frame\n",
    "plt.rcParams[\"figure.figsize\"] = (7, 7)\n",
    "#plt.rcParams[\"figure.figsize\"] = plt.rcParamsDefault[\"figure.figsize\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93445fef",
   "metadata": {},
   "source": [
    "#### _____\n",
    "### Multi-Depot Constrained Vehicle Routing Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "619ce817",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MDCVRP(cust_x, cust_y, cust_demand, depot_x, depot_y, vehicles, veh_depot, veh_capacity, time_lim):\n",
    "    \"\"\"\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    cust_x : Integer Arrary\n",
    "        Array containing x-coordinates of customers.\n",
    "    \n",
    "    cust_y : Integer Arrary\n",
    "        Array containing y-coordinates of customers.\n",
    "    \n",
    "    cust_demand : Integer Arrary\n",
    "        Array containing cuulative demand of each customer.\n",
    "    \n",
    "    depot_x : Integer Arrary\n",
    "        Array containing x-coordinates of depots.\n",
    "    \n",
    "    depot_y : Integer Arrary\n",
    "        Array containing y-coordinates of depots.\n",
    "    \n",
    "    vehicles : Integer Arrary\n",
    "        Array containing numbered vehicles alloted to passed \n",
    "        depots.\n",
    "    \n",
    "    veh_depot : Integer Arrary\n",
    "        Array containing depot allocations to corresponding \n",
    "        vehicles.\n",
    "    \n",
    "    veh_capacity : Integer Arrary\n",
    "        Array containing maximum load capacity corresponding to \n",
    "        each vehicles.\n",
    "    \n",
    "    time_lim : Integer\n",
    "        The maximum time limit upto which the optimization model \n",
    "        will work.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    routes: Array\n",
    "        Array of all vehicle routes\n",
    "        \n",
    "    veh_routes: Array\n",
    "        List of vehicles associated with each routes in routes\n",
    "    \n",
    "    depot_routes: \n",
    "        List of depots associated with each route in routes\n",
    "    \n",
    "    X: Array\n",
    "        List of x coordinates for all nodes\n",
    "    \n",
    "    Y: Array\n",
    "        List of y coordinates for all nodes\n",
    "    \n",
    "    depot: Array\n",
    "        List of depot nodes linked with X and Y\n",
    "        \n",
    "    obj_val: Integer\n",
    "        The final objective value of model\n",
    "      \n",
    "    \"\"\"\n",
    "    \n",
    "    n, m = len(cust_x), len(depot_x)\n",
    "    customer = [i for i in range(n)]\n",
    "    depot = [i for i in range(n, m+n)]\n",
    "    nodes = [i for i in range(m+n)]\n",
    "    veh_depot = np.array(veh_depot) + n\n",
    "    \n",
    "    q = {i: cust_demand[i] for i in customer}\n",
    "    for i in depot:\n",
    "        q[i] = 0\n",
    "    Q = veh_capacity\n",
    "        \n",
    "    X = list(cust_x) + list(depot_x)\n",
    "    Y = list(cust_y) + list(depot_y)\n",
    "    \n",
    "    distance = {(i,j): np.hypot(X[i]-X[j],Y[i]-Y[j]) for i in nodes \n",
    "                for j in nodes if i!=j}\n",
    "    arcs = [(i,j) for i in nodes for j in nodes if i!=j]\n",
    "    arcs_vehicle = [(i,j,k) for i in nodes for j in nodes                      # constructing arcs between all points\n",
    "                    for k in vehicles if i!=j]\n",
    "\n",
    "    # MDCVRP Model\n",
    "    model = Model('MDCVRP')\n",
    "    x = model.addVars(arcs_vehicle,vtype=GRB.BINARY,name='x')                  # Binary Decision Variable\n",
    "    u = model.addVars(customer, vtype=GRB.CONTINUOUS, name='u')\n",
    "    \n",
    "    model.setObjective(quicksum(distance[i,j]*x[i,j,k]                         # setting model objective\n",
    "                                for i,j,k in arcs_vehicle), GRB.MINIMIZE)\n",
    "    model.addConstrs(quicksum(x[i,j,k] for i in nodes                          # each node visited by only one vehicle\n",
    "                              for k in vehicles if i!=j)==1 for j in customer)\n",
    "    model.addConstrs(quicksum(x[i,j,k] for j in nodes                          # each node visited by only one vehicle \n",
    "                              for k in vehicles if i!=j)==1 for i in customer) \n",
    "    model.addConstrs(quicksum(x[i,h,k] for i in nodes if i!=h) -               # flow balance constraint:vehicle coming in \n",
    "                     quicksum(x[h,j,k] for j in nodes if h!=j)==0              # and out of customer location is same\n",
    "                     for h in nodes for k in vehicles)\n",
    "    model.addConstrs(quicksum(q[i]*quicksum(x[i,j,k] for j in nodes if i!=j)   # vehicle capacity constraint\n",
    "                              for i in nodes)<= Q[l] for l,k in enumerate(vehicles))\n",
    "    model.addConstrs(quicksum(x[i,j,k] for i in depot                          # vehicle leaves from depot to node\n",
    "                              for j in customer )<=1 for k in vehicles)\n",
    "    model.addConstrs(quicksum(x[i,j,k] for j in depot for i in customer) <= 1  # vehicle returns from node to depot\n",
    "                     for k in vehicles)\n",
    "    model.addConstrs((u[i] - u[j] + (m+n) * x[i,j,k]) <= (n+m-1)               # subtour elimination\n",
    "                     for i in customer for j in customer \n",
    "                     if i!=j for k in vehicles)\n",
    "    for l, vd in enumerate(veh_depot):                                         # vehicle allocation to depot\n",
    "        k = vehicles[l]\n",
    "        rst_depot = depot.copy()\n",
    "        rst_depot.remove(vd)\n",
    "        model.addConstrs(x[i, j, k] ==0 for i in rst_depot for j in customer)\n",
    "        model.addConstrs(x[i, j, k] ==0 for j in rst_depot for i in customer)\n",
    "    \n",
    "    model.Params.timelimit = time_lim\n",
    "    model.optimize()\n",
    "    obj_val = round(model.ObjVal)\n",
    "    \n",
    "    routes, veh_routes, depot_routes = [], [], []\n",
    "    for n in depot:\n",
    "        for k in vehicles:\n",
    "            for i in nodes:\n",
    "                if i!=n and x[n,i,k].x>0.9:\n",
    "                    aux=[n,i]\n",
    "                    while i!=n:\n",
    "                        j=i\n",
    "                        for h in nodes:\n",
    "                            if j!=h and x[j,h,k].x>0.9:\n",
    "                                aux.append(h)\n",
    "                                i=h\n",
    "                    routes.append(aux)\n",
    "                    veh_routes.append(k)\n",
    "                    depot_routes.append(n)\n",
    "    \n",
    "    depot_routes = np.array(depot_routes) - n\n",
    "    return routes, veh_routes, depot_routes, X, Y, depot, obj_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c74bc7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MDCVRP_BP(cust_x, cust_y, cust_demand, depot_x, depot_y, vehicles, veh_depot, veh_capacity, time_lim):\n",
    "    \"\"\"\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    cust_x : Integer Arrary\n",
    "        Array containing x-coordinates of customers.\n",
    "    \n",
    "    cust_y : Integer Arrary\n",
    "        Array containing y-coordinates of customers.\n",
    "    \n",
    "    cust_demand : Integer Arrary\n",
    "        Array containing cuulative demand of each customer.\n",
    "    \n",
    "    depot_x : Integer Arrary\n",
    "        Array containing x-coordinates of depots.\n",
    "    \n",
    "    depot_y : Integer Arrary\n",
    "        Array containing y-coordinates of depots.\n",
    "    \n",
    "    vehicles : Integer Arrary\n",
    "        Array containing numbered vehicles alloted to passed \n",
    "        depots.\n",
    "    \n",
    "    veh_depot : Integer Arrary\n",
    "        Array containing depot allocations to corresponding \n",
    "        vehicles.\n",
    "    \n",
    "    veh_capacity : Integer Arrary\n",
    "        Array containing maximum load capacity corresponding to \n",
    "        each vehicles.\n",
    "    \n",
    "    time_lim : Integer\n",
    "        The maximum time limit upto which the optimization model \n",
    "        will work.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    routes: Array\n",
    "        Array of all vehicle routes\n",
    "        \n",
    "    veh_routes: Array\n",
    "        List of vehicles associated with each routes in routes\n",
    "    \n",
    "    depot_routes: \n",
    "        List of depots associated with each route in routes\n",
    "    \n",
    "    X: Array\n",
    "        List of x coordinates for all nodes\n",
    "    \n",
    "    Y: Array\n",
    "        List of y coordinates for all nodes\n",
    "    \n",
    "    depot: Array\n",
    "        List of depot nodes linked with X and Y\n",
    "        \n",
    "    obj_val: Integer\n",
    "        The final objective value of model\n",
    "    \"\"\"\n",
    "    \n",
    "    n, m = len(cust_x), len(depot_x)\n",
    "    customer = [i for i in range(n)]\n",
    "    depot = [i for i in range(n, m+n)]\n",
    "    nodes = [i for i in range(m+n)]\n",
    "    veh_depot = np.array(veh_depot) + n\n",
    "    \n",
    "    num_item = len(cust_demand[0])\n",
    "    q = {i: cust_demand[i] for i in customer}\n",
    "    for i in depot:\n",
    "        q[i] = [0 for it in range(num_item)]\n",
    "    Q = {i: veh_capacity[i-1] for i in vehicles}\n",
    "    \n",
    "    X = list(cust_x) + list(depot_x)\n",
    "    Y = list(cust_y) + list(depot_y)\n",
    "    \n",
    "    distance = {(i,j): np.hypot(X[i]-X[j],Y[i]-Y[j]) for i in nodes \n",
    "                for j in nodes if i!=j}\n",
    "    arcs = [(i,j) for i in nodes for j in nodes if i!=j]\n",
    "    arcs_vehicle = [(i,j,k) for i in nodes for j in nodes                      # constructing arcs between all points\n",
    "                    for k in vehicles if i!=j]\n",
    "\n",
    "    # MDCVRP Model\n",
    "    model = Model('MDCVRP_BP')\n",
    "    x = model.addVars(arcs_vehicle,vtype=GRB.BINARY,name='x')                  # Binary Decision Variable\n",
    "    u = model.addVars(customer, vtype=GRB.CONTINUOUS, name='u')\n",
    "    \n",
    "    model.setObjective(quicksum(distance[i,j]*x[i,j,k]                         # setting model objective\n",
    "                                for i,j,k in arcs_vehicle), GRB.MINIMIZE)\n",
    "    model.addConstrs(quicksum(x[i,j,k] for i in nodes                          # each node visited by more than one vehicle\n",
    "                              for k in vehicles if i!=j)>=1 for j in customer)\n",
    "    model.addConstrs(quicksum(x[i,j,k] for j in nodes                          # each node visited by more than vehicle \n",
    "                              for k in vehicles if i!=j)>=1 for i in customer) \n",
    "    model.addConstrs(quicksum(x[i,h,k] for i in nodes if i!=h) -               # flow balance constraint:vehicle coming in \n",
    "                     quicksum(x[h,j,k] for j in nodes if h!=j)==0              # and out of customer location is same\n",
    "                     for h in nodes for k in vehicles)\n",
    "    \n",
    "    for kv in vehicles:\n",
    "        for nit in range(num_item):\n",
    "            if Q[kv][nit]>10:\n",
    "                model.addConstrs(quicksum(q[i][n]*quicksum(x[i,j,k] for j in nodes if i!=j) for i in nodes) \n",
    "                                 <= Q[k][n] for n in range(nit, nit+1) for k in range(kv, kv+1))\n",
    "                # vehicle capacity constraint\n",
    "                \n",
    "    \n",
    "    model.addConstrs(quicksum(x[i,j,k] for i in depot                          # vehicle leaves from depot to node\n",
    "                              for j in customer )<=1 for k in vehicles)\n",
    "    model.addConstrs(quicksum(x[i,j,k] for j in depot for i in customer) <= 1  # vehicle returns from node to depot\n",
    "                     for k in vehicles)\n",
    "    model.addConstrs((u[i] - u[j] + (m+n) * x[i,j,k]) <= (n+m-1)               # subtour elimination\n",
    "                     for i in customer for j in customer \n",
    "                     if i!=j for k in vehicles)\n",
    "    for l, vd in enumerate(veh_depot):                                         # vehicle allocation to depot\n",
    "        k = vehicles[l]\n",
    "        rst_depot = depot.copy()\n",
    "        rst_depot.remove(vd)\n",
    "        model.addConstrs(x[i, j, k] ==0 for i in rst_depot for j in customer)\n",
    "        model.addConstrs(x[i, j, k] ==0 for j in rst_depot for i in customer)\n",
    "    \n",
    "    model.Params.timelimit = time_lim\n",
    "    model.optimize()\n",
    "    obj_val = round(model.ObjVal)\n",
    "    \n",
    "    routes, veh_routes, depot_routes = [], [], []\n",
    "    for n in depot:\n",
    "        for k in vehicles:\n",
    "            for i in nodes:\n",
    "                if i!=n and x[n,i,k].x>0.9:\n",
    "                    aux=[n,i]\n",
    "                    while i!=n:\n",
    "                        j=i\n",
    "                        for h in nodes:\n",
    "                            if j!=h and x[j,h,k].x>0.9:\n",
    "                                aux.append(h)\n",
    "                                i=h\n",
    "                    routes.append(aux)\n",
    "                    veh_routes.append(k)\n",
    "                    depot_routes.append(n)\n",
    "    \n",
    "    depot_routes = np.array(depot_routes) - n\n",
    "    return routes, veh_routes, depot_routes, X, Y, depot, obj_val"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4019d4",
   "metadata": {},
   "source": [
    "#### _____\n",
    "### Customer and Depot Data Points Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b293bb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of X - 10 , 97 \n",
      "Range of Y - 4 , 100\n",
      "Item weights -  [3 2 1 3]\n",
      "Customer item demands - [array([2, 5, 1, 2]), array([4, 5, 2, 1]), array([2, 3, 1, 2]), array([1, 3, 1, 5])]\n",
      "Customer demands - [23, 27, 19, 25]\n",
      "-----\n",
      "Depot Nodes -  [[20, 50], [73, 80], [89, 29], [93, 12]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(20)\n",
    "\n",
    "num_depot = 4\n",
    "num_cust = 100\n",
    "num_item = 4\n",
    "num_vehicle = 20\n",
    "\n",
    "#-----\n",
    "# Depot Allocation - Kroger locations\n",
    "depot_x = [20, 73, 89, 93]\n",
    "depot_y = [50, 80, 29, 12]\n",
    "\n",
    "\n",
    "#-----\n",
    "# Customer Allocation\n",
    "\n",
    "# random allocations across the grid\n",
    "cust_x = list(np.random.randint(1, 100, size=15))\n",
    "cust_y = list(np.random.randint(1, 100, size=15))\n",
    "\n",
    "# normal distributions in populated household areas\n",
    "# (28, 47) - (5, 10)\n",
    "for i in range(25):\n",
    "    cust_x.append(round(np.random.normal(28, 5)))\n",
    "    cust_y.append(round(np.random.normal(47, 10)))\n",
    "    \n",
    "# (65, 25) - (7, 7)\n",
    "for i in range(25):\n",
    "    cust_x.append(round(np.random.normal(65, 7)))\n",
    "    cust_y.append(round(np.random.normal(25, 7)))\n",
    "\n",
    "# (80, 70) - (5, 5)\n",
    "for i in range(25):\n",
    "    cust_x.append(round(np.random.normal(70, 10)))\n",
    "    cust_y.append(round(np.random.normal(80, 10)))\n",
    "\n",
    "# (50, 50) - (20, 20)\n",
    "for i in range(10):\n",
    "    cust_x.append(round(np.random.normal(50, 10)))\n",
    "    cust_y.append(round(np.random.normal(50, 10)))\n",
    "\n",
    "#-----\n",
    "# Creating Demands and Vehicle Allocation\n",
    "np.random.seed(10)\n",
    "#cust_demand = np.random.randint(5, 15, size=len(cust_x))\n",
    "cust_demand_item = [np.random.randint(1, 6, size=num_item) for i in range(num_cust)]\n",
    "weight_item = np.random.randint(1, 4, size=num_item)\n",
    "cust_demand = cust_demand_item*np.transpose(weight_item)\n",
    "cust_demand = [sum(cust_demand[i]) for i in range(num_cust)]\n",
    "\n",
    "\n",
    "#-----\n",
    "# Plotting attributes\n",
    "colors = ['blue', 'green', 'orange', 'yellow', 'brown', 'grey']\n",
    "\n",
    "#-----\n",
    "# Data Point Verification\n",
    "print('Range of X -', min(cust_x), ',',max(cust_x), \n",
    "      '\\nRange of Y -', min(cust_y), ',', max(cust_y))\n",
    "print('Item weights - ', weight_item)\n",
    "print('Customer item demands -', cust_demand_item[:4])\n",
    "print('Customer demands -', cust_demand[:4])\n",
    "\n",
    "depot_nodes = [[depot_x[i], depot_y[i]] for i in range(len(depot_x))]\n",
    "print('-----\\nDepot Nodes - ', depot_nodes)\n",
    "\n",
    "# Plotting customer & depot points\n",
    "plt.scatter(depot_x, depot_y, marker='x', color='r')\n",
    "plt.scatter(cust_x, cust_y, marker='.', color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2804037c",
   "metadata": {},
   "source": [
    "#### _____\n",
    "### Bin Packing - Integer Programming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2afa05ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of `place holder' vehicles = 9\n",
      "Academic license - for non-commercial use only - expires 2022-09-03\n",
      "Using license file C:\\Users\\krishrao\\gurobi.lic\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 13 rows, 45 columns and 81 nonzeros\n",
      "Model fingerprint: 0x9a0a85c1\n",
      "Variable types: 0 continuous, 45 integer (9 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 4e+02]\n",
      "  Objective range  [1e+00, 1e+00]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [3e+02, 3e+02]\n",
      "Found heuristic solution: objective 8.0000000\n",
      "Presolve time: 0.01s\n",
      "Presolved: 13 rows, 45 columns, 81 nonzeros\n",
      "Variable types: 0 continuous, 45 integer (9 binary)\n",
      "\n",
      "Root relaxation: objective 6.502500e+00, 30 iterations, 0.01 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0    6.50250    0    5    8.00000    6.50250  18.7%     -    0s\n",
      "H    0     0                       7.0000000    6.50250  7.11%     -    0s\n",
      "     0     0    6.50250    0    5    7.00000    6.50250  7.11%     -    0s\n",
      "\n",
      "Explored 1 nodes (30 simplex iterations) in 0.04 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 2: 7 8 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 7.000000000000e+00, best bound 7.000000000000e+00, gap 0.0000%\n",
      "\n",
      "-----\n",
      "Number of vehicles required - 7\n"
     ]
    }
   ],
   "source": [
    "q_node = cust_demand_item\n",
    "weight = weight_item\n",
    "Q = 400\n",
    "\n",
    "# Bin Packing\n",
    "b = [0, 0, 0, 0]\n",
    "for j in range(len(q_node)):\n",
    "    for i in range(len(weight)):\n",
    "        b[i] += q_node[j][i]\n",
    "\n",
    "T = 0\n",
    "for i in range(num_item):\n",
    "    T = T + math.ceil(b[i] / math.floor(Q/weight[i]))      # getting an upper limit for the number of vehicles that can be used\n",
    "print(\"Number of `place holder' vehicles = \"+str(T))\n",
    "\n",
    "ipmod = Model(\"Cutting stock IP\")\n",
    "y = ipmod.addVars(range(T),obj=1, vtype=GRB.BINARY,        # these are binary variables indicating\n",
    "                  name=\"vehicle_use\")                      # whether/not each vehicle is used\n",
    "\n",
    "z = ipmod.addVars(range(num_item), range(T),               # these are integer variables for the\n",
    "                  vtype=GRB.INTEGER, name=\"assignment\")    # number of units of each item in each vehicle\n",
    "\n",
    "# bin packing model\n",
    "ipmod.modelSense = GRB.MINIMIZE\n",
    "for j in range(T):\n",
    "    ipmod.addConstr(quicksum(weight[i]*z[i,j] for i in range(num_item))    # the weight limit constraint for each vehicle\n",
    "                    - Q*y[j] <= 0, name=\"vehicle\"+str(j))\n",
    "for i in range(num_item):\n",
    "    ipmod.addConstr(quicksum(z[i,j] for j in range(T))     # the demand constraint for each item\n",
    "                    == b[i], name=\"demand\"+str(i))\n",
    "ipmod.optimize()\n",
    "\n",
    "print('\\n-----\\nNumber of vehicles required -', int(ipmod.ObjVal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1c8acb",
   "metadata": {},
   "source": [
    "_____\n",
    "### Bin Packing - Column Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "edc142c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.0, 1: 1.46, 2: 0.6515225563909774, 3: 0.0, 4: 2.308270676691729, 5: 2.082706766917293}\n",
      "Optimal value =6.5025\n",
      "Final constraint matrix:\n",
      "[[133   0   0   0 133   0]\n",
      " [  0 200   0   0   0   0]\n",
      " [  0   0 400   0   1   1]\n",
      " [  0   0   0 133   0 133]]\n",
      "Approximate solution (by roundup) has value 9\n",
      "Approximate solution (by IP) has value 9.0\n",
      "\n",
      "-----\n",
      "Number of vehicles required - 9\n"
     ]
    }
   ],
   "source": [
    "# Master LP\n",
    "master = Model(\"CSP column generation LP\")\n",
    "A = []\n",
    "s = []\n",
    "for i in range(num_item):\n",
    "    s=[0]*num_item\n",
    "    s[i] = int(Q/weight[i])\n",
    "    A.append(s)\n",
    "    \n",
    "demand = {}\n",
    "x = {}\n",
    "for i in range(num_item):\n",
    "    x[i] = master.addVar(obj=1, vtype=GRB.CONTINUOUS, name=\"num[%d]\"%i)\n",
    "    # initial patterns for a basic-feasible-solution\n",
    "    demand[i] = master.addConstr( int(Q/weight[i]) * x[i] >= b[i], name=\"dem[%d]\"%i)\n",
    "    \n",
    "n = num_item                            # initially number of patterns equals #items\n",
    "master.update()\n",
    "master.setParam(\"OutputFlag\",0)         # turn off output reporting\n",
    "\n",
    "while 1:\n",
    "# resolve master LP\n",
    "    master.optimize()\n",
    "    profit = [demand[i].Pi for i in range(num_item)]    # optimal dual vars\n",
    "    \n",
    "# knapsack instance for the reduced-cost oracle\n",
    "    kmod = Model(\"knapsack\")\n",
    "    select = kmod.addVars(range(num_item), vtype=GRB.INTEGER, name=\"sel\")\n",
    "    kmod.setObjective(quicksum(profit[i]*select[i] for i in range(num_item)), GRB.MAXIMIZE)\n",
    "    kmod.addConstr(quicksum(weight[i]*select[i] for i in range(num_item)) <= Q, \"weight-limit\")\n",
    "    kmod.setParam(\"OutputFlag\",0)       # turn off output reporting\n",
    "    kmod.optimize()                     # solve knapsack IP\n",
    "    s= kmod.getAttr('x', select)        # get the optimal knapsack solution (pattern)\n",
    "#    print(s)\n",
    "\n",
    "# add column to master LP\n",
    "    if(kmod.ObjVal < 1.000001): break   # no negative reduced cost\n",
    "\n",
    "    svalues = [int(s[i]) for i in range(num_item)]\n",
    "    A.append(svalues)                   # add new column (pattern) to constraint matrix\n",
    "    \n",
    "    col = Column()\n",
    "    for i in range(num_item):\n",
    "        col.addTerms(s[i], demand[i])        \n",
    "    x[n] = master.addVar(obj=1, vtype=GRB.CONTINUOUS, name=\"x[%d]\"%n, column=col)\n",
    "                                   # add the new variable (pattern) to model \n",
    "    master.update()   \n",
    "    n += 1                         # number of decision variables increases\n",
    "    \n",
    "xsol = master.getAttr('x', x)    \n",
    "print(xsol)                        # optimal solution to column-generation LP\n",
    "\n",
    "print(\"Optimal value =\"+str(master.ObjVal))\n",
    "print(\"Final constraint matrix:\")\n",
    "print(np.transpose(np.matrix(A)))\n",
    "\n",
    "############ Get approximate solution ##############\n",
    "\n",
    "# Method 1: round-up\n",
    "apx1 = [math.ceil(xsol[j]) for j in range(n)]\n",
    "print(\"Approximate solution (by roundup) has value \" + str(sum(apx1[j] for j in range(n))))\n",
    "\n",
    "# Method 2: re-solve model with integer restrictions on the generated columns/variables\n",
    "for j in range(n):\n",
    "    x[j].setAttr(\"vtype\", GRB.INTEGER)  #enforce integer restriction for IP\n",
    "master.update()\n",
    "master.optimize()\n",
    "apx2 = master.getAttr('x', x)\n",
    "print(\"Approximate solution (by IP) has value \" + str(sum(apx2[j] for j in range(n))))\n",
    "print(\"\\n-----\\nNumber of vehicles required -\", int(sum(apx2[j] for j in range(n))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f24d20b",
   "metadata": {},
   "source": [
    "#### _____\n",
    "### Constrained Vehicle Routing Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a93e82da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changed value of parameter timelimit to 7200.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 95663 rows, 96508 columns and 750816 nonzeros\n",
      "Model fingerprint: 0x0c02e2fe\n",
      "Variable types: 100 continuous, 96408 integer (96408 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+02]\n",
      "  Objective range  [1e+00, 1e+02]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 5400 rows and 5400 columns\n",
      "Presolve time: 0.92s\n",
      "Presolved: 90263 rows, 91108 columns, 721116 nonzeros\n",
      "Variable types: 100 continuous, 91008 integer (91008 binary)\n",
      "\n",
      "Deterministic concurrent LP optimizer: primal and dual simplex\n",
      "Showing first log only...\n",
      "\n",
      "Concurrent spin time: 0.00s\n",
      "\n",
      "Solved with dual simplex\n",
      "\n",
      "Root relaxation: objective 4.960003e+02, 617 iterations, 0.75 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  496.00032    0  201          -  496.00032      -     -    3s\n",
      "     0     0  499.57626    0  207          -  499.57626      -     -   21s\n",
      "     0     0  499.57626    0  205          -  499.57626      -     -   45s\n",
      "     0     0  499.57626    0  202          -  499.57626      -     -   69s\n",
      "     0     0  499.57626    0  211          -  499.57626      -     -   71s\n",
      "     0     0  499.57626    0  211          -  499.57626      -     -   81s\n",
      "     0     0  499.57626    0  208          -  499.57626      -     -   83s\n",
      "     0     0  499.57626    0  212          -  499.57626      -     -  101s\n",
      "     0     0  499.57626    0  211          -  499.57626      -     -  103s\n",
      "     0     0  499.57626    0  211          -  499.57626      -     -  106s\n",
      "     0     2  508.49155    0  211          -  508.49155      -     -  135s\n",
      "    42    43  529.52993    9  201          -  508.49155      -  30.3  140s\n",
      "    97    90  542.37334   18  195          -  508.49155      -  28.3  145s\n",
      "   149   133  557.55659   27  188          -  508.49155      -  26.9  150s\n",
      "   204   179  561.06646   39  173          -  508.49155      -  25.7  155s\n",
      "   249   215  590.91397   47  176          -  508.49155      -  27.2  161s\n",
      "   293   256  593.16795   52  177          -  508.49155      -  29.5  166s\n",
      "   335   291  615.96190   58  172          -  508.49155      -  30.4  171s\n",
      "   361   312  620.77942   64  176          -  508.49155      -  32.6  175s\n",
      "   398   339  623.33575   67  186          -  508.49155      -  36.8  181s\n",
      "   431   364  624.18133   69  182          -  508.49155      -  37.9  185s\n",
      "   488   409  646.15502   73  173          -  508.49155      -  38.5  191s\n",
      "   524   429  653.01251   79  178          -  508.49155      -  39.6  197s\n",
      "   541   444  670.70700   80  173          -  508.49155      -  41.3  200s\n",
      "   585   463  684.17434   86  177          -  508.49155      -  44.2  205s\n",
      "   634   495  710.11838   89  165          -  508.49155      -  47.5  212s\n",
      "   667   510  710.62533   91  165          -  508.49155      -  48.1  215s\n",
      "   766   579  526.96434   14  196          -  508.49155      -  46.6  222s\n",
      "   819   614  533.58306   19  190          -  508.49155      -  45.8  226s\n",
      "   864   650  541.50873   24  180          -  508.49155      -  45.9  230s\n",
      "   976   741  547.25398   36  165          -  508.49155      -  44.8  238s\n",
      "  1033   780  550.08062   41  181          -  508.49155      -  45.3  243s\n",
      "  1094   844  564.73680   46  176          -  508.49155      -  45.5  247s\n",
      "  1170   922  565.49167   52  180          -  508.49155      -  44.2  252s\n",
      "  1270  1004  574.73199   63  163          -  508.49155      -  41.7  257s\n",
      "  1374  1054  574.73199   72  164          -  508.49155      -  40.2  264s\n",
      "  1448  1109  584.98696   78  161          -  508.49155      -  39.7  270s\n",
      "  1539  1154  585.48616   91  158          -  508.49155      -  38.7  276s\n",
      "  1608  1185  611.90897   95  156          -  508.49155      -  38.4  283s\n",
      "  1693  1226  637.33293   98  151          -  508.49155      -  38.3  289s\n",
      "  1796  1297 infeasible  107               -  508.49155      -  37.5  296s\n",
      "  1901  1384  671.99348  113  149          -  508.49155      -  36.7  302s\n",
      "* 1981  1384             461    2287.8367819  508.49155  77.8%  35.8  302s\n",
      "H 2035  1386                    2280.4314439  508.49155  77.7%  35.2  387s\n",
      "H 2035  1375                    2087.5506206  508.49155  75.6%  35.2  387s\n",
      "H 2037  1368                    2009.7071152  508.49155  74.7%  35.2  387s\n",
      "  2039  1426  675.07542  120  153 2009.70712  508.49155  74.7%  35.2  426s\n",
      "H 2064  1410                    1905.5503869  508.49155  73.3%  35.0  426s\n",
      "H 2121  1392                    1757.3488608  508.49155  71.1%  35.0  490s\n",
      "H 2123  1386                    1697.2017870  508.49155  70.0%  35.0  490s\n",
      "  2125  1459  675.28642  128  147 1697.20179  508.49155  70.0%  35.1  496s\n",
      "  2216  1532  688.30857  140  141 1697.20179  508.49155  70.0%  35.1  503s\n",
      "  2302  1626  700.55177  158  150 1697.20179  508.49155  70.0%  35.2  510s\n",
      "  2399  1715  700.97073  180  140 1697.20179  508.49155  70.0%  34.8  518s\n",
      "  2508  1790  701.22903  191  138 1697.20179  508.49155  70.0%  34.6  526s\n",
      "  2610  1853  702.46609  215  149 1697.20179  508.49155  70.0%  34.1  534s\n",
      "  2675  1854  554.27374   19  211 1697.20179  508.49155  70.0%  34.3  543s\n",
      "H 2676  1762                    1675.7434678  508.49155  69.7%  34.3  588s\n",
      "H 2676  1674                    1643.3168043  508.49155  69.1%  34.3  588s\n",
      "H 2677  1590                    1581.0699927  508.49155  67.8%  34.2  655s\n",
      "H 2679  1512                    1572.8454686  508.49155  67.7%  34.2  747s\n",
      "H 2679  1437                    1516.6701007  508.49155  66.5%  34.2  747s\n",
      "  2682  1439  508.49155    7  201 1516.67010  508.49155  66.5%  34.2  778s\n",
      "  2683  1442  508.49155   10  203 1516.67010  508.49155  66.5%  35.8  811s\n",
      "  2685  1444  508.49155   11  203 1516.67010  508.49155  66.5%  35.9  815s\n",
      "H 2709  1382                    1514.9127414  508.49155  66.4%  35.9  881s\n",
      "H 2711  1313                    1464.3399388  508.49155  65.3%  36.0  881s\n",
      "  2732  1331  508.49155   18  201 1464.33994  508.49155  65.3%  35.9  885s\n",
      "  2778  1358  508.49155   24  198 1464.33994  508.49155  65.3%  35.9  890s\n",
      "  2831  1390  508.49155   33  199 1464.33994  508.49155  65.3%  35.7  895s\n",
      "  2891  1435  519.68790   44  201 1464.33994  508.49155  65.3%  35.6  900s\n",
      "  2949  1467  527.02342   53  186 1464.33994  508.49155  65.3%  35.4  906s\n",
      "  2999  1497  538.59290   57  186 1464.33994  508.49155  65.3%  35.3  910s\n",
      "  3053  1519  538.00623   64  189 1464.33994  508.49155  65.3%  35.4  916s\n",
      "  3102  1548  538.96843   72  180 1464.33994  508.49155  65.3%  35.6  922s\n",
      "  3144  1562  563.52329   77  171 1464.33994  508.49155  65.3%  36.0  926s\n",
      "  3193  1574  539.52143   83  179 1464.33994  508.49155  65.3%  36.3  931s\n",
      "  3238  1596  539.73103   89  179 1464.33994  508.49155  65.3%  36.8  936s\n",
      "  3286  1611  539.95683   94  179 1464.33994  508.49155  65.3%  37.5  942s\n",
      "  3338  1647  569.53905  100  180 1464.33994  508.49155  65.3%  38.1  947s\n",
      "  3366  1666  571.96822  103  173 1464.33994  508.49155  65.3%  38.3  950s\n",
      "  3429  1701  540.67274  108  179 1464.33994  508.49155  65.3%  38.9  957s\n",
      "  3463  1714  570.40136  112  184 1464.33994  508.49155  65.3%  39.2  964s\n",
      "  3497  1710  574.90722  117  182 1464.33994  508.49155  65.3%  39.5  966s\n",
      "  3515  1721  575.17954  119  175 1464.33994  508.49155  65.3%  39.7  971s\n",
      "  3538  1763  657.98350  122  173 1464.33994  508.49155  65.3%  40.0  978s\n",
      "  3583  1780  575.65083  128  182 1464.33994  508.49155  65.3%  40.4  982s\n",
      "  3621  1811  586.30501  133  161 1464.33994  508.49155  65.3%  41.0  986s\n",
      "  3679  1851  576.33061  140  173 1464.33994  508.49155  65.3%  41.1  990s\n",
      "  3804  1924  576.77122  157  173 1464.33994  508.49155  65.3%  41.8  999s\n",
      "  3860  1948  606.91968  163  178 1464.33994  508.49155  65.3%  42.1 1004s\n",
      "  3907  1979  577.19860  170  181 1464.33994  508.49155  65.3%  42.6 1009s\n",
      "  3957  2002  678.38396  176  173 1464.33994  508.49155  65.3%  43.9 1014s\n",
      "H 4002  1925                    1454.1114590  508.49155  65.0%  44.8 1072s\n",
      "H 4003  1862                    1440.7481474  508.49155  64.7%  44.8 1072s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H 4004  1804                    1436.3477822  508.49155  64.6%  44.8 1072s\n",
      "  4006  1819  580.90435  182  172 1436.34778  508.49155  64.6%  44.9 1077s\n",
      "  4023  1855  577.58776  184  177 1436.34778  508.49155  64.6%  45.4 1082s\n",
      "  4071  1887  577.77377  191  173 1436.34778  508.49155  64.6%  46.3 1088s\n",
      "  4129  1919  578.70957  201  174 1436.34778  508.49155  64.6%  47.3 1094s\n",
      "  4199  1955  720.48009  210  175 1436.34778  508.49155  64.6%  48.2 1101s\n",
      "  4264  2004  579.26882  216  174 1436.34778  508.49155  64.6%  49.2 1108s\n",
      "  4337  2056  579.36922  223  174 1436.34778  508.49155  64.6%  50.1 1115s\n",
      "  4425  2101  579.63112  231  177 1436.34778  508.49155  64.6%  50.9 1122s\n",
      "  4506  2141  579.70141  236  176 1436.34778  508.49155  64.6%  52.3 1130s\n",
      "  4579  2192  579.87373  244  180 1436.34778  508.49155  64.6%  53.5 1138s\n",
      "  4666  2258  579.98226  250  177 1436.34778  508.49155  64.6%  54.4 1146s\n",
      "  4768  2316  580.06299  259  170 1436.34778  508.49155  64.6%  55.4 1154s\n",
      "  4864  2363  580.19126  267  180 1436.34778  508.49155  64.6%  56.6 1163s\n",
      "  4951  2424  580.26287  277  170 1436.34778  508.49155  64.6%  57.7 1173s\n",
      "  5047  2485  580.39793  285  174 1436.34778  508.49155  64.6%  59.0 1182s\n",
      "  5149  2479  580.57335  294  173 1436.34778  508.49155  64.6%  60.6 1273s\n",
      "H 5155  2421                    1415.3655252  508.49155  64.1%  60.8 1273s\n",
      "H 5161  2366                    1407.2710512  508.49155  63.9%  60.8 1273s\n",
      "  5179  2418  677.51135  297  171 1407.27105  508.49155  63.9%  61.0 1281s\n",
      "  5244  2510  581.28780  301  180 1407.27105  508.49155  63.9%  61.4 1292s\n",
      "  5363  2572  695.84790  314  185 1407.27105  508.49155  63.9%  62.2 1302s\n",
      "  5496  2609  821.42081  329  167 1407.27105  508.49155  63.9%  62.9 1314s\n",
      "  5652  2670  798.57299  344  169 1407.27105  508.49155  63.9%  63.2 1325s\n",
      "  5802  2788 1044.78364  378  150 1407.27105  508.49155  63.9%  63.7 1337s\n",
      "  5951  2902 1032.94184  402  155 1407.27105  508.49155  63.9%  64.0 1348s\n",
      "  6087  3025 1026.83571  420  144 1407.27105  508.49155  63.9%  64.7 1361s\n",
      "  6222  3167 1112.25360  436  135 1407.27105  508.49155  63.9%  66.1 1374s\n",
      "  6390  3300 1058.06714  461  144 1407.27105  508.49155  63.9%  67.0 1388s\n",
      "  6546  3427 1058.38568  481  132 1407.27105  508.49155  63.9%  67.7 1402s\n",
      "  6697  3560 1106.46995  503  142 1407.27105  508.49155  63.9%  68.4 1417s\n",
      "  6843  3701 1107.00532  526  130 1407.27105  508.49155  63.9%  69.5 1471s\n",
      "H 6936  3700                    1397.6988889  508.49155  63.6%  70.4 1471s\n",
      "  7012  3860 1107.37031  549  134 1397.69889  508.49155  63.6%  70.6 1489s\n",
      "  7280  3999 1132.85730  572  126 1397.69889  508.49155  63.6%  71.7 1504s\n",
      "  7445  4020 1249.56780  603  122 1397.69889  508.49155  63.6%  71.7 1518s\n",
      "  7501  4044  508.49155   22  200 1397.69889  508.49155  63.6%  71.7 1535s\n",
      "  7531  4068  518.64292   32  187 1397.69889  508.49155  63.6%  72.0 1553s\n",
      "  7559  4096  519.89545   43  174 1397.69889  508.49155  63.6%  72.2 1570s\n",
      "  7592  4128  534.23476   54  181 1397.69889  508.49155  63.6%  72.1 1585s\n",
      "  7631  4152  544.00314   65  172 1397.69889  508.49155  63.6%  72.3 1600s\n",
      "  7662  4194  544.62930   76  180 1397.69889  508.49155  63.6%  72.6 1614s\n",
      "  7674  4194  814.73755  522  130 1397.69889  508.49155  63.6%  72.5 1615s\n",
      "  7709  4243  551.39060   88  180 1397.69889  508.49155  63.6%  72.6 1630s\n",
      "  7761  4489  567.37624   99  172 1397.69889  508.49155  63.6%  72.7 1652s\n",
      "  8029  4762  607.19340  162  162 1397.69889  508.49155  63.6%  72.0 1675s\n",
      "  8320  5004  641.72354  214  195 1397.69889  508.49155  63.6%  71.0 1698s\n",
      "  8575  5025  665.26387  244  174 1397.69889  508.49155  63.6%  70.6 1792s\n",
      "H 8576  5021                    1390.1879022  508.49155  63.4%  70.6 1792s\n",
      "H 8587  5021                    1390.1559224  508.49155  63.4%  70.6 1792s\n",
      "  8599  5143  666.12095  246  177 1390.15592  508.49155  63.4%  70.6 1812s\n",
      "  8734  5575  672.20266  257  188 1390.15592  508.49155  63.4%  70.3 1837s\n",
      "  9193  5895  685.47210  304  181 1390.15592  508.49155  63.4%  68.2 1862s\n",
      "  9574  6282  711.14879  342  178 1390.15592  508.49155  63.4%  67.2 1891s\n",
      "  9989  6609  724.42609  401  157 1390.15592  508.49155  63.4%  66.2 1922s\n",
      " 10386  7037  738.53292  469  171 1390.15592  508.49155  63.4%  65.0 1973s\n",
      " 10901  7277  775.34866  602  142 1390.15592  508.49155  63.4%  63.5 1999s\n",
      " 11165  7680  779.92632  642  140 1390.15592  508.49155  63.4%  62.8 2032s\n",
      " 11597  7996  883.13519  694  146 1390.15592  508.49155  63.4%  61.4 2060s\n",
      " 11921  8288  916.92867  748  139 1390.15592  508.49155  63.4%  60.2 2091s\n",
      " 12284  8620  932.58489  797  166 1390.15592  508.49155  63.4%  59.7 2123s\n",
      " 12656  8918  951.24102  854  164 1390.15592  508.49155  63.4%  58.9 2155s\n",
      " 13020  9121  967.14602  908  154 1390.15592  508.49155  63.4%  58.0 2187s\n",
      " 13320  9545  996.81108  956  132 1390.15592  508.49155  63.4%  57.8 2213s\n",
      " 13820  9980 1060.19951 1009  133 1390.15592  508.49155  63.4%  56.9 2240s\n",
      " 14345 10355 1093.08711 1061  136 1390.15592  508.49155  63.4%  56.0 2270s\n",
      " 14737 10525 1119.78142 1110  132 1390.15592  508.49155  63.4%  55.3 2317s\n",
      "H14754 10522                    1388.0948947  508.49155  63.4%  55.3 2317s\n",
      " 14960 10976 1125.66338 1128  130 1388.09489  508.49155  63.4%  54.8 2350s\n",
      " 15482 11330 1161.26679 1220  147 1388.09489  508.49155  63.4%  53.8 2382s\n",
      " 15849 11793 1190.02644 1271  166 1388.09489  508.49155  63.4%  53.1 2411s\n",
      " 16378 12351 1273.86482 1351  137 1388.09489  508.49155  63.4%  52.6 2440s\n",
      " 16944 12817 1332.74850 1439  120 1388.09489  508.49155  63.4%  52.0 2470s\n",
      " 17520 13071  554.04712   46  189 1388.09489  508.49155  63.4%  51.4 2497s\n",
      " 17783 13538  635.62802  142  189 1388.09489  508.49155  63.4%  51.5 2524s\n",
      " 18318 13962  701.79214  289  174 1388.09489  508.49155  63.4%  51.7 2554s\n",
      " 18844 14493 1006.20301  433  165 1388.09489  508.49155  63.4%  51.6 2578s\n",
      " 19456 14721  691.79108  117  170 1388.09489  508.49155  63.4%  51.1 2609s\n",
      " 19773 14857  684.02653   98  155 1388.09489  508.49155  63.4%  50.8 2635s\n",
      " 19987 15387  692.38794  139  157 1388.09489  508.49155  63.4%  50.6 2662s\n",
      " 20654 15388  709.50112  165  211 1388.09489  508.49155  63.4%  49.9 2932s\n",
      "H20655 14619                    1387.9470205  508.49155  63.4%  49.9 2987s\n",
      "H20655 13888                    1377.8499384  508.49155  63.1%  49.9 2987s\n",
      "H20656 13194                    1373.4107213  508.49155  63.0%  49.9 3057s\n",
      "H20658 12536                    1369.8137220  508.49155  62.9%  49.9 3146s\n",
      "H20659 11909                    1362.2152815  508.49155  62.7%  49.9 3175s\n",
      " 20660 11910  601.62803   83  202 1362.21528  508.49155  62.7%  49.9 3188s\n",
      " 20661 11911  666.44499  313  208 1362.21528  508.49155  62.7%  49.9 3211s\n",
      " 20665 11913  878.86743  462  239 1362.21528  508.59456  62.7%  49.9 3220s\n",
      " 20666 11914  571.28840   58  227 1362.21528  508.59456  62.7%  49.9 3239s\n",
      " 20667 11915 1185.68355  862  237 1362.21528  508.59456  62.7%  49.9 3245s\n",
      " 20668 11915 1352.80725  628  226 1362.21528  508.59456  62.7%  49.9 3265s\n",
      " 20669 11916 1231.97155  770  229 1362.21528  508.59456  62.7%  49.9 3272s\n",
      "H20669 11320                    1361.1039052  508.59456  62.6%  49.9 3280s\n",
      " 20671 11321  885.51434  498  207 1361.10391  508.59456  62.6%  49.9 3292s\n",
      " 20672 11322 1136.49106  538  213 1361.10391  508.59456  62.6%  49.9 3322s\n",
      " 20673 11323  711.55837  444  228 1361.10391  508.59456  62.6%  49.9 3328s\n",
      " 20674 11323  596.14491  102  203 1361.10391  508.59456  62.6%  49.9 3336s\n",
      " 20675 11324 1197.14067  293  208 1361.10391  508.59456  62.6%  49.9 3342s\n",
      " 20676 11325  648.02075  119  212 1361.10391  508.59456  62.6%  49.9 3353s\n",
      " 20677 11325 1068.32695  455  209 1361.10391  508.59456  62.6%  49.9 3363s\n",
      " 20678 11326  976.23606  422  209 1361.10391  508.59456  62.6%  49.9 3408s\n",
      " 20679 11330  508.59456   19  206 1361.10391  508.59456  62.6%  50.7 3419s\n",
      " 20681 11332  509.73589   20  206 1361.10391  509.73589  62.5%  50.7 3429s\n",
      " 20685 11335  510.39623   21  208 1361.10391  509.76779  62.5%  50.7 3430s\n",
      " 20693 11337  536.56323   22  205 1361.10391  510.39623  62.5%  50.7 3437s\n",
      " 20701 11340  536.56323   23  205 1361.10391  511.36948  62.4%  50.7 3441s\n",
      " 20718 11348  537.02752   24  204 1361.10391  511.40094  62.4%  50.7 3448s\n",
      " 20724 11350  537.17448   25  206 1361.10391  511.40094  62.4%  50.7 3450s\n",
      " 20744 11360  553.14163   26  201 1361.10391  511.40094  62.4%  50.6 3456s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 20761 11368  556.10391   28  200 1361.10391  511.40094  62.4%  50.6 3462s\n",
      " 20778 11377  560.37724   29  193 1361.10391  511.40094  62.4%  50.6 3466s\n",
      " 20793 11383  560.50251   30  201 1361.10391  511.40094  62.4%  50.6 3471s\n",
      " 20807 11391  561.54050   32  200 1361.10391  511.40094  62.4%  50.6 3476s\n",
      " 20816 11394  570.76487   33  201 1361.10391  511.40094  62.4%  50.6 3480s\n",
      " 20836 11407  569.67506   35  187 1361.10391  511.40094  62.4%  50.6 3488s\n",
      " 20846 11411  580.39667   36  180 1361.10391  511.40094  62.4%  50.6 3491s\n",
      "H20856 10845                    1349.8070496  511.40094  62.1%  50.6 3570s\n",
      " 20866 10854  594.07856   39  182 1349.80705  511.40094  62.1%  50.6 3577s\n",
      " 20877 10857  581.61934   40  180 1349.80705  511.40094  62.1%  50.5 3580s\n",
      " 20899 10871  617.80208   43  177 1349.80705  511.40094  62.1%  50.5 3588s\n",
      " 20910 10880  605.02934   44  181 1349.80705  511.40094  62.1%  50.5 3593s\n",
      " 20924 10887  606.29369   46  178 1349.80705  511.40094  62.1%  50.5 3598s\n",
      " 20941 10892  609.73619   50  169 1349.80705  511.40094  62.1%  50.5 3602s\n",
      " 20955 10897  615.59420   53  172 1349.80705  511.40094  62.1%  50.6 3606s\n",
      " 20970 10894 infeasible   56      1349.80705  511.40094  62.1%  50.6 3668s\n",
      "H20972 10355                    1349.3363125  511.40094  62.1%  50.6 3668s\n",
      "H20973  9843                    1341.3221064  511.40094  61.9%  50.6 3668s\n",
      "H20974  9356                    1338.3615647  511.40094  61.8%  50.6 3668s\n",
      " 20975  9368  620.66190   57  175 1338.36156  511.40094  61.8%  50.6 3673s\n",
      " 20994  9379  635.32713   63  169 1338.36156  511.40094  61.8%  50.6 3678s\n",
      " 21021  9386  648.72785   69  172 1338.36156  511.40094  61.8%  50.6 3684s\n",
      " 21051  9390  651.79610   80  175 1338.36156  511.40094  61.8%  50.6 3690s\n",
      " 21076  9391  652.15553   87  175 1338.36156  511.40094  61.8%  50.6 3695s\n",
      " 21097  9413  753.62194   93  170 1338.36156  511.40094  61.8%  50.6 3701s\n",
      " 21131  9426  753.93170  103  166 1338.36156  511.40094  61.8%  50.6 3707s\n",
      " 21164  9450  754.33612  111  170 1338.36156  511.40094  61.8%  50.5 3713s\n",
      " 21205  9456  788.06255  120  160 1338.36156  511.40094  61.8%  50.5 3719s\n",
      " 21235  9473  755.36860  126  165 1338.36156  511.40094  61.8%  50.5 3726s\n",
      " 21277  9486  813.76560  134  166 1338.36156  511.40094  61.8%  50.5 3733s\n",
      " 21317  9505  814.15133  143  163 1338.36156  511.40094  61.8%  50.5 3739s\n",
      " 21355  9520  814.87690  149  162 1338.36156  511.40094  61.8%  50.5 3747s\n",
      " 21398  9535  872.68372  153  146 1338.36156  511.40094  61.8%  50.5 3754s\n",
      " 21434  9555  815.36018  157  162 1338.36156  511.40094  61.8%  50.5 3762s\n",
      " 21480  9567  815.48042  161  162 1338.36156  511.40094  61.8%  50.5 3770s\n",
      " 21534  9590  886.97640  164  163 1338.36156  511.40094  61.8%  50.5 3779s\n",
      " 21596  9608  815.64703  175  159 1338.36156  511.40094  61.8%  50.5 3787s\n",
      " 21642  9637  815.74397  180  163 1338.36156  511.40094  61.8%  50.6 3796s\n",
      " 21702  9697  815.89184  186  163 1338.36156  511.40094  61.8%  50.5 3806s\n",
      " 21782  9732  897.96517  192  150 1338.36156  511.40094  61.8%  50.5 3815s\n",
      " 21850  9770  816.18802  198  162 1338.36156  511.40094  61.8%  50.5 3826s\n",
      " 21921  9811  909.06564  203  144 1338.36156  511.40094  61.8%  50.5 3836s\n",
      " 21991  9880  816.82893  209  160 1338.36156  511.40094  61.8%  50.5 3846s\n",
      " 22090  9932  929.33695  214  162 1338.36156  511.40094  61.8%  50.5 3857s\n",
      " 22175  9997  930.38620  219  141 1338.36156  511.40094  61.8%  50.5 3868s\n",
      " 22269 10051  931.48330  230  138 1338.36156  511.40094  61.8%  50.4 3880s\n",
      " 22365 10059  932.22845  242  143 1338.36156  511.40094  61.8%  50.4 3975s\n",
      "H22397  9587                    1325.8721364  511.40094  61.4%  50.5 3975s\n",
      " 22407  9668  932.45397  248  147 1325.87214  511.40094  61.4%  50.5 3987s\n",
      " 22502  9741  971.65137  263  141 1325.87214  511.40094  61.4%  50.6 4001s\n",
      " 22619  9844  942.47232  280  137 1325.87214  511.40094  61.4%  50.5 4016s\n",
      " 22769  9925 1136.90032  309  122 1325.87214  511.40094  61.4%  50.4 4030s\n",
      " 22908  9977 1162.06587  328  128 1325.87214  511.40094  61.4%  50.4 4047s\n",
      " 23034 10012 1174.44450  347  119 1325.87214  511.40094  61.4%  50.6 4063s\n",
      " 23154 10023 1255.37634  373  112 1325.87214  511.40094  61.4%  50.6 4080s\n",
      " 23234 10005 1184.19079  383  113 1325.87214  511.40094  61.4%  50.6 4552s\n",
      " 23285 10129 1264.41549  383  110 1325.87214  511.40094  61.4%  50.6 4571s\n",
      "H23499  9635                    1321.4058657  511.40094  61.3%  50.5 4619s\n",
      " 23517  9771 1186.71134  416  110 1321.40587  511.40094  61.3%  50.5 4637s\n",
      " 23696  9834  632.77504   49  183 1321.40587  511.40094  61.3%  50.5 4656s\n",
      " 23852  9907 1255.79646  103  115 1321.40587  511.61352  61.3%  50.4 4676s\n",
      " 24024  9992  547.31372   56  204 1321.40587  511.61352  61.3%  50.4 4697s\n",
      " 24166 10076  551.47447  109  209 1321.40587  511.61352  61.3%  50.4 4718s\n",
      "H24198  9650                    1321.1377216  511.61352  61.3%  50.3 4718s\n",
      "H24232  9240                    1308.8898076  511.61352  60.9%  50.3 4718s\n",
      "H24270  8784                    1266.2723113  511.61352  59.6%  50.4 4718s\n",
      " 24299  8873  552.08258  144  191 1266.27231  511.61352  59.6%  50.4 4816s\n",
      "H24323  8503                    1263.4168969  511.61352  59.5%  50.4 4816s\n",
      "H24398  8131                    1257.4867781  511.61352  59.3%  50.4 4816s\n",
      " 24399  8282  685.38265  169  197 1257.48678  511.61352  59.3%  50.4 4839s\n",
      "H24550  7978                    1257.4420478  511.61352  59.3%  50.4 4863s\n",
      "H24589  7632                    1242.6562448  511.61352  58.8%  50.5 4863s\n",
      "H24590  7340                    1240.8791206  511.61352  58.8%  50.5 4863s\n",
      " 24620  7485  708.88031  232  189 1240.87912  511.61352  58.8%  50.5 4889s\n",
      " 24779  7598  719.57602  276  190 1240.87912  511.61352  58.8%  50.7 4915s\n",
      " 24959  7711  725.34119  335  184 1240.87912  511.61352  58.8%  50.9 4943s\n",
      " 25135  7695  731.22090  400  182 1240.87912  511.61352  58.8%  51.0 4975s\n",
      " 25180  7858  733.81047  425  180 1240.87912  511.61352  58.8%  50.9 5008s\n",
      " 25469  8008  516.65195   37  207 1240.87912  511.61352  58.8%  50.8 5070s\n",
      "H25567  7698                    1238.6140742  511.61352  58.7%  50.7 5070s\n",
      " 25740  7954  558.99269  133  194 1238.61407  511.61352  58.7%  50.7 5101s\n",
      " 26070  8049  564.13101  223  209 1238.61407  511.61352  58.7%  50.6 5134s\n",
      " 26338  8257  699.09758  281  177 1238.61407  511.61352  58.7%  50.6 5170s\n",
      " 26657  8183  717.03805  367  203 1238.61407  511.61352  58.7%  50.5 5210s\n",
      "H26661  7919                    1238.3183192  511.61352  58.7%  50.5 5210s\n",
      " 26689  8162  717.68161  370  206 1238.31832  511.61352  58.7%  50.5 5247s\n",
      " 27022  8394  888.14774  418  177 1238.31832  511.61352  58.7%  50.6 5284s\n",
      " 27243  8321 1043.46649  753  125 1238.31832  511.61352  58.7%  50.5 5285s\n",
      " 27367  8603  910.64831  538  168 1238.31832  511.61352  58.7%  50.4 5324s\n",
      " 27716  8830 1070.91746  636  154 1238.31832  511.75844  58.7%  50.4 5368s\n",
      " 28123  9032  857.72515  144  144 1238.31832  511.75844  58.7%  50.4 5414s\n",
      " 28543  9288  555.62673   43  184 1238.31832  511.75844  58.7%  50.2 5460s\n",
      " 29031  9254  878.98589  160  161 1238.31832  511.75844  58.7%  50.1 5509s\n",
      " 29220  9380  879.10604  161  162 1238.31832  511.75844  58.7%  50.3 5554s\n",
      " 29462  9402  611.99774  122  199 1238.31832  511.83535  58.7%  50.5 5640s\n",
      "H29565  9118                    1237.5689156  511.83535  58.6%  50.4 5640s\n",
      " 29566  9646  630.96058  143  210 1237.56892  511.83535  58.6%  50.4 5690s\n",
      " 30097  9942  704.55467  258  183 1237.56892  511.83535  58.6%  50.6 5743s\n",
      " 30603  9923  755.07324  360  208 1237.56892  511.83535  58.6%  50.8 5800s\n",
      " 30753 10306  793.50977  401  157 1237.56892  511.83535  58.6%  50.8 5851s\n",
      "H31228 10434                    1228.6943907  511.83535  58.3%  50.8 5934s\n",
      "H31327 10175                    1225.8901106  511.83535  58.2%  51.0 5934s\n",
      "H31476  9911                    1224.0450315  511.83535  58.2%  51.1 5934s\n",
      "H31792 10054                    1222.4719706  511.83535  58.1%  51.1 5993s\n",
      " 32267 10359 1173.06348  715  157 1222.47197  511.86885  58.1%  51.1 6044s\n",
      " 32840 10363  641.35474   57  160 1222.47197  511.89487  58.1%  51.0 6292s\n",
      "H32842 10360                    1220.6599737  511.89487  58.1%  51.0 6292s\n",
      " 32844 10548  659.21069   60  156 1220.65997  511.89487  58.1%  51.0 6335s\n",
      " 33090 10687  711.66870   68  152 1220.65997  511.89487  58.1%  51.0 6400s\n",
      " 33293 11162 1113.88066  108  124 1220.65997  512.02366  58.1%  50.9 6490s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 33836 11726  574.60437  112  189 1220.65997  512.02739  58.1%  50.6 6539s\n",
      " 34487 12371  743.37698  263  143 1220.65997  512.02739  58.1%  50.2 6589s\n",
      " 35144 12826  945.38232  400  130 1220.65997  512.02739  58.1%  49.8 6633s\n",
      " 35605 12900 1076.95890  493  118 1220.65997  512.02739  58.1%  49.5 6690s\n",
      " 35679 13285 1077.00287  494  115 1220.65997  512.02739  58.1%  49.5 6735s\n",
      " 36112 13656  556.88868   89  199 1220.65997  512.02739  58.1%  49.2 6779s\n",
      " 36509 14084  655.27172  136  188 1220.65997  512.02739  58.1%  49.3 6829s\n",
      " 37013 14475  571.16812  182  194 1220.65997  512.02739  58.1%  49.4 6871s\n",
      " 37475 14835  564.62842  215  196 1220.65997  512.02739  58.1%  49.4 6914s\n",
      "H37946 15003                    1220.5978889  512.02739  58.1%  49.5 6973s\n",
      " 38133 15288  568.53489  272  196 1220.59789  512.02739  58.1%  49.6 7018s\n",
      " 38559 15773 1043.42325  347  141 1220.59789  512.02739  58.1%  49.5 7065s\n",
      " 39094 16274 1043.04854  409  132 1220.59789  512.02739  58.1%  49.3 7108s\n",
      " 39719 16573 1204.05336  484  118 1220.59789  512.02739  58.1%  49.2 7166s\n",
      " 40050 16781 1157.07177  485  116 1220.59789  512.02739  58.1%  49.0 7200s\n",
      "\n",
      "Cutting planes:\n",
      "  Gomory: 106\n",
      "  MIR: 4\n",
      "  Flow cover: 26\n",
      "  Zero half: 77\n",
      "  RLT: 8\n",
      "\n",
      "Explored 40294 nodes (1983525 simplex iterations) in 7200.38 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 10: 1220.6 1220.66 1222.47 ... 1240.88\n",
      "\n",
      "Time limit reached\n",
      "Best objective 1.220597888895e+03, best bound 5.120273899897e+02, gap 58.0511%\n"
     ]
    }
   ],
   "source": [
    "for l in range(1): #num_depot):\n",
    "    #depot_x1, depot_y1 = [depot_x[l]], [depot_y[l]]\n",
    "    num_vehicle = 9\n",
    "    vehicles = np.array(range(1, num_vehicle+1))\n",
    "    veh_capacity = [400 for i in range(num_vehicle)]\n",
    "    veh_depot = [l for i in range(num_vehicle)]\n",
    "    time_limit = 7200\n",
    "    vrp_routes, vrp_veh_routes, vrp_node_routes, vrp_X, vrp_Y, vrp_depot, vrp_obj_val = MDCVRP(cust_x, cust_y, cust_demand, \n",
    "                                                                                               depot_x, depot_y, vehicles, \n",
    "                                                                                               veh_depot, veh_capacity, \n",
    "                                                                                               time_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24a18c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "Overall objective funtion value - 1221\n"
     ]
    },
    {
     "data": {
      "image/png": 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Sp0+Phg0bIk8eFrvv2LEj/vnnH7Rq1cqj8iXGETPjVADNE63rD2CliJQBsNL6GkqpCgDaAaho/cx3SqlQj0mr0Wg0aRl7hWbgBUV27NgxhIaGIm/evBARjB07Fjt27MCOHTtw/Pjx/2ZmiUPqlVIwq+FzqspMRP4BcC3R6pYAplmfTwPQym79XBGJFpHjAI4AqOkZUTUaTTAzbhzQsycQEWG2JH6M4SOzx96H5gEuX76MHj16oFevXlBKoVmzZvj+++8RGxsLADh06BDu3r0LgGbG48ePw2KxYN68eahXrx4ee+wxrF27FleuXEF8fDzmzJmDBg0aAADSp0//3348jas+s3wich4AROS8UiqvdX0hABvttjtjXafRaDTJsm4d0Ls3n3//PfD448CTTwJVq3LJl89c+fwCQ5EZPjJ7nxng1gzNCMyIjY1FunTp0KlTJ7z77rsAgG7duuHEiROoWrUqRAR58uTBb7/9BgCoXbs2+vfvj927d/8XDBISEoJhw4bhiSeegIigRYsWaNmyJQCge/fuqFy5MqpWrYpZs2a5fUrs8XQASFJnMskhg1KqO4DuAFC0aFEPi6HRaAKJ9ettz0WAPXuAtWtt6woWpFKrVs2m4AoV8rh1zb9RCsiRI6GPzDA55sjh1smIj49P9r2QkBAMHTo0ybD6zJkzY968efet79ChAzp06HDf+hEjRmDEiBEuy5kSriqzi0qpAtZZWQEAl6zrzwAoYrddYQDnktqBiPwI4EcAqF69ujlGVpOJiADWrAEaNgRq1zZbGo3GPBo2BDJmBKKjgXTpgMWLgYoVge3bgW3bbMuyZYCRy5snT0LlVrUqULx4kCu4gQMTRi0aCi2ov7RjuKrMfgfQGcBw6+Miu/WzlVKjABQEUAbAZneFDEbCw4H69fnHzJQJWLlSKzRN2qV2bWD1auC114Dz54FHHwXCwoAGDbgY3L0L7NyZUMF9+SUQF8f3c+ZMqNyqVQNKlQJCgimjNrHiMkmRNWzYEA0bNjTl2EnhSGj+HAANAeRWSp0BMABUYj8rpV4DcApAWwAQkb1KqZ8B7AMQB6CniCQ/f03D/P23bYQZHc0ZmlZmmrRM7drA2LFAkybAzz8Dr7xy/zZZsgB16nAxiIoCdu9OqODGjAFiYvh+1qxUjvYK7qGHgFA/irMWkTRdbNgTEZCpKjMRaZ/MW42T2X4IgCHuCJUWsP8jiQD16pkni0bjLzRqBJQvT6XWqZNjk46wMKBGDS4GMTHAvn025bZ1KzBhAhAZyfczZwYeeSShgqtQAXCjN6TLhIWF4erVq2m2DYzRzywsLMyt/egKICYREUGb/xNPcBS6di3NjhpNWkYpoFcvhuhv2gTUquXafjJkAKpU4fLqq1wXFwccPGhTbtu2AdOmAePH2z5TubJNuT36KHDrFrB5s3f92oULF8aZM2dw+fJl7xwgADA6TbuDMivBzZ7q1avLli1bzBbDZ5w/DxQuDHz4ITB0KNChA/DLL8DGjfwTaTRpmTt3GKn4zDOAh6O378NiAY4cSajgtm1j1Sh7tF/bHJRSW0WkuiPbBpNbNGCYMYN/oi5d+Hr8eCBvXppVDDOIRpNWeeABoGtXDvAuXPDusUJCgLJlgXbtgJEjqbCuXQOOHWOem0FMDP3aGv9FKzMfIwJMnQrUrcs/EcAIrClTgP37gU8+MVU8jcYveOstIDYW+PFH3x9bKaBECVsUZUgITZB+FLinSQKtzHzM5s1UWsaszKBpU/oJRo9miLJGk5YpWxZo3hz44QdbVKKvyZSJjx9/rE2MgYBWZj5myhT+SV588f73RowAypShort50+eiaTR+Re/e9C8vWGDO8U+fZtTj559rRRYIaGXmQyIjgblzgTZtgGzZ7n8/Sxb6086cAfr29bl4Go1f0bw5E57HjTPn+KdPA0WK6OIagYJWZj7kt98440psYrTnscdo1pg6ldtrNGmVkBCa3jdsYFkrX2MoM01goJWZD5kyhbXjUnMkf/opc126dwcuXUp5W40mmOnalaa+sWN9f2ytzAILrcx8xOnTLGHVuXPqdeIyZKC58dYt4PXXPdqqSKMJKHLkYFmr2bOBK1d8d9yYGKYFaGUWOGhl5iOmT6dS6tzZse0rVACGDQN+/50mR40mrdKzJ+uXTp7su2OeO8f/q1ZmgYNWZj7AyC1r2JD5K47Spw8/06cPcOKEd2TTaPydSpVY9u2772zV8b3N6dN81MoscNDKzAds2MCSOV27Ove5kBDbrKxzZ1uVfY0mrdG7N3DqFPuc+QKtzAIPrcx8wJQpLNHzwgvOf7ZYMeDbb4F//rE1ldVo0hrPPgsULeq7QBCtzAIPrcy8zN27rIr/4ovMI3OFzp2BVq0Ysr9nj0fF02gCgnTpgDffZHWcvXu9f7zTp4Hs2dkLTRMYaGXmZX79lVXAU8otSw2l2IspRw4WIzarvI9GYybdugEZM/omiVqH5QceWpl5mSlTgNKl3W++mTcvi67u2MHyOhpNWiN3brZLmj79/hYtnkYrs8BDKzMvcuwY20Z06eKZkjgtWzKIZNgwNvfUaNIavXoB9+5xkOhNtDILPLQy8yLTp1OJvfKK5/b5zTf8k73yCv1xGk1aompVoE4d9gD0VnRvZCQTtLUyCyy0MvMSFgvD6ps08eyfIls2tno/ehT44APP7VejCRR69+b1v3y5d/Z/5gwftTILLLQy8xJr1gAnTzqfW+YIDRoA774LfP898Oefnt+/RuPPtG4NFCjgvTB9HZYfmGhl5iWmTmVob6tW3tn/4MFAxYpUlteueecYGo0/kiED8MYbnJm9957n/cdamQUmWpl5gVu3gPnzgXbtbN1qPU1YGIsRX77M2nUaTVqiWjU+jhoFNG7sWYVmKLPChT23T4330crMC/z8M53I3jAx2vPoo8DAgWz4OXeud4+l0fgTa9fankdF0azvKU6fZhqAtwaiGu+glZkXmDoVKFcOqFnT+8f68EOgVi3grbeAs2e9fzyNxmxOnwZmzeJzpWilSK1HoLP71ybGwEMrMw9z6BALC3ft6pt26+nSMQUgOhp47TXd+0wT3Jw/DzRqxLSUyZOBIUOAlSuB2rU9dwytzAKTdGYLEGxMm8Zq9506+e6YZcoAX33F2dkPP7CGnUYTbFy6RP/YhQvAX395VoHZc/o0I4Y1gYWemXmQ+Hgqs+bNGTrsS3r0AJo1A95/Hzh82LfH1mi8zdWrzNk8cQJYutR7iuz2beDmTT0zC0S0MvMgf/9Nv5W3Az+SQimaXTJmZHUQXzUx1Gi8zY0bQNOmNOH//jvw+OPeO5YOyw9ctDLzIFOnArlysfeSGRQqxG68GzcCI0aYI4NG40lu3aKlY/duYOFCzs68iVZmgYtWZh7i+nX+2Tp04OzILNq14zJwILB9u3lyaDTucucO8PTTwNatwC+/AE895f1jamUWuGhl5iHmzmVEoRkmxsSMH8+WMS+/zBwcjSbQuHcPeO45IDwcmD2bHSN8wenTNNkXKuSb42k8h1ZmHmLqVKByZSYym02uXMBPPwH79gH/+5/Z0mg0zhEVBTz/PBOhp08H2rb13bFPnwby5wfSp/fdMTWeQSszD7BvH7B5s+f6lnmCZs0Yoj9qVMJqCRqNPxMTQ+X111/ApElAx46+Pb7OMQtctDLzAFOmMHn55ZfNliQhI0cCpUoBnTvTka7R+DNxcUD79sCSJQxkevVV38uglVngopWZm8TFseDvM88AefKYLU1CsmShbKdPA337mi2NRpM88fEsNLBgARvQmpH4L6KVWSCjlZmbLF8OXLxIE6M/UqsW8NFHnD0uWmS2NBrN/VgsLMU2dy5TSvr0MUeO69cZeHLokOeq8K9ZA3z+uefb1GjuR4kfFPOrXr26bNmyxWwxXOKFF4B165gs7a9O45gYKrUzZ4A9exjpqNH4AyKsXvPjj8CgQcBnn5kny/TpNMkDLEn3yCPs7O4q164xPw5gMeRVq7xXuSRYUUptFZHqjmyrZ2ZucOUKsHgxfWX+qsgANjOcMYN+s+7ddTFijX8gwlnYjz8CH38MfPqpufIYigfgbPHGDdf3FRUFHDhgex0b69k2NZr70YWG3WD2bF6k/pBblhoVK7LC+Pvvs36kv5pFNWkDEaBfP2DsWODdd9k53exI4NatmaMZE8MB4KxZzs+kIiL4ublzORtTiv7ADBk826ZGcz/azOgGjz4KhIYCgSK6xcL2Gdu2cRRarJjZEmnSKp9+SgXWsycVmtmKzCAigjOohg1dU2SPP86gMKWAmTOBEiVc35/GOTOjnpm5yI4dXMaONVsSxwkJsSV3d+nCPlAh2tCs8TGDB3Pp1g349lv/UWQAFY6rSmfyZFuBb6WAkydZ3k4rMd+gb2UuMnUqTQcdOpgtiXMULw6MGcPR4pgxZkujSWuMHMlZWadOwIQJwTOYOn4c+PVXPleK9Vm1WdG36JmZC8TE0C7esiVLRwUaXboAv/3GkP2mTelP02i8zbff0k/20ksst5aaIgsPZ/UafzfR3bzJPFMAmDOHis3fZQ5GtDJzgaVLGckYqEEUSgETJwKVKnGEvHEjZ5kajbeYMIGRi88/z8jadInuPBYL01uOHOHyzz/0OQH+HdYeFwe8+CJz0/76C3jiCbMlSrtoZeYCU6awk3TTpmZL4jp58zIk+vnngS++4KLReIMpU5hL1qIFfWWrVtmU1tGjfDx2jF0nDEJDbc+jooDRo5kr6U/+NRHg7bdtdSS1IjMXHc3oJBcuAIULM8R9+HCzpXGfrl2ZLLphA28WGo07REVRMRlKatkydmDPlInKymKxbZspE1C6NOuHli6d8PmZMxwsRkdTaYgAdeow4KpqVfO+nz1jxrBMXL9+uhmut3AmmlErMyf5+msqsv37gXLlzJbGfW7eZHRjxoxs5pkli9kSafyd27eprAyFZT/LOnPm/qT8zJnZWPOhhxIqrQIFUp5pGWHyjz8OHDwI9O9P83737pzh5c7t1a+ZIkuWsN/a88+zcWiwBLL4G1qZeQkR4OGHgaxZg6vW2po1NJH07AmMG2e2NBp/4Nq1+02BxvOLFxNumyePTUkZimrDBuD77/m+p3xeN26w5NXYsSwz9cUXwBtv3O9/8zY7dwJ163Iw+88/VNYa76CVmZf491+gZk06s7t3N1saz/Luu/RL/PlnYPsCNY4hQqWUlMI6cuT+Uk6FCydtDixVKun6hWXLAocP83loKBXPRx95Rva9e+mrWrWKVoWxYzl78wXnz/MeAACbNgEFC/rmuGkVnTTtJYYP5yiwRAmzJfE8Q4dSkXXtymLEOXOaLZHGXeLjE0YI2iuto0eBu3dt24aEMAexdGn2FLNXWiVL0r/lKH/8QUWWPj19ZJ4u5VSxIv1wv/4KvPce0KAB0K4dl337vBcWf+8eTYvXr3PmqRWZf6FnZg7yyy8MwQX4x1650j9Dhd1h2zbgscfY6Xf2bLOl0ThCbCxw4sT9pkAjQjAmxrZthgxUTIlnV6VLs7SZJ4plx8ay2nxcHCP8Nmzwbs7VvXsMvhg+3PZdvRHKb7Hwf/Hbb2ylZOSVabyLnpl5gQULbM9jYuhnCjZlVrUqMGAAKzS0bMnkVo35REZSMSXlvzp5kjMwgyxZqKQqVOAswl5hFSqUMOTdG/z4I4OjfvuNpj9vm/8yZ6Yf7d494KuvuM4b/8+PP+Y9YPRorcj8FT0zcxAjSEIpjvyCcWYGcERdrx6TQPfs0aYUX3HrVkIToL1p8OzZhNvmyAGUKZO0DytfPvNysa5fp1yVK/P/4Us5IiJ43Vosnrec/PQTm4e++Sar6vtTrluwowNAvES+fBzdjh8fnIrM4NAhoEoV+iKWLdN/XndZv56DoerV6YtMKuji8uWEn8mXL2lzYKlS/ltC7b33OHPZto3Xj6+pWpWBK660bkmOr79mHln16jSZ+jpyMq2jzYxeolw5jvyCWZEBjEQbORLo1YuRmz16mC1R4BIRwRm9UU3dQClGCJYuDbRqlVBhlSzJ9I9A4sgRRhV27WqOIgMYVZktm+f+nz/8wJxSANi1i9HMwf7fD2S0MnOCkiVZuiYt8NZbdHS/9x7QpAlvshrnWbMmoSLr1Ikh6iVK0FwdLPTrxwCTwYPNlsRz/PCD7bnRKVorM/9F5607QcmSwLlzdMgHO0qxpl6GDMArr9w/s9A4RoMGNjNtpkz0u5QvH1yKbM0aYOFCKukCBcyWxjNs28bZWLp0DJrRnaL9H63MnKBkST6eOGGqGD6jUCHgu+9oKhs50mxpApNcuZig/NxzwRk0FB/PhPuiRfkYDIgA77zDclnLljHhOxh/u2BDmxmdwFBmx45xdJ0WaNeOYdYDBrC+nln+kEBlyRI+jhsHFClirizeYPp01vScPdu5xGp/ZuFClqn6/nvgySe5aPwfPTNzglKl+HjsmLly+BKlODvLnZv+nqgosyUKLBYvZhJxMCqyO3eYf1WrFgc9wUB0NPDBB6wy0q2b2dJonEErMyfIk4dJqUePmi2Jb3nwQWDyZOadffqp2dIEDteuMZw7WJNsR4xgS6TRo/0jfePWLeDUKfeKgI8dy8HqqFE6DD/Q0MrMCZSiqTEtzcwMnnqKIfpff00TjCZ1/vyTPqVnnzVbEs9z6hQrbrRv7x998CIiaO48fhxo3Ng1hXb5Mv1jLVroYtuBiFZmTpJWlRnAIJCSJYHOnTkK1qTM4sWczdeoYbYknseogO8vDWrXrLYVf4iJSfjaUQYMYPFloyyWJrBwS5kppd5RSu1VSu1RSs1RSoUppXIppVYopQ5bH4Oq/rqhzPygcIrPeeABOvxPnQqeyDVvERfH6vFPPx18jRs3bWLAx3vvMYrRdAYORJed7yBE8U+ZIb2gy853gIEDHd7F3r0sEGCkTmgCD5f/ZkqpQgDeBlBdRCoBCAXQDkB/ACtFpAyAldbXQUPJkswzS9ygMK1Qpw7w4Yf0of3+u9nS+C/h4SytFGwmRiNsPX9+dn42HRHgxg0U+HkMHst9FCVKCI4+9w4K/DyGP4CDo8733mP1ECf0n8bPcHfMmA5AJqVUOgCZAZwD0BLANOv70wC0cvMYfoV9eH5aZeBARui9/vr9NQU1ZPFitlQJtrDuefPojxoyhDN101GKESh9+iDD5TMoenwtFVmfPg5HpvzxB/2bn37KYCdNYOKyMhORswC+AnAKwHkAN0XkLwD5ROS8dZvzAPIm9XmlVHel1Bal1JbLAXRH1MqM1RBmzuTA94030qbJNTWWLGHFiECrsZgSkZGclVepQr+p32AoNHscVGSxsZyVlS7NWqSawMUdM2NOcBZWAkBBAFmUUi87+nkR+VFEqotI9Tx58rgqhs8pXpz/kbSszACgUiWOzhcuBGbMMFsa/+LIEeDAgZRD8iMigGHD3Asj9zWjR9NfOnq09/uiOYVh+7TnnXdSHWVFRABt2rD/2siRHKRpAhd3MimaADguIpcBQCm1AEAdABeVUgVE5LxSqgCASx6Q028IC2OZp7SuzADeL37/Hejdm7MQvwgG8AOMqh+Jldnt28DBg8DPP9si5gKlN96FC1S+rVr5WY1CQ5GNGQMU6gWULgVU6cPXQLIztPBwdjOIiWGATt4k7UeaQMIdZXYKQC2lVGYAkQAaA9gC4C6AzgCGWx8XuSukv1GyZNpLnE6K0FBg2jQ2Y+zSBfj77+CL3HMWEWD+fCr2Zcs4QzOWxE02gcDpWv6//7E6ht/V6FSK3Ur79AF2lAJgZ3LMkSOBIhNhAeF584CJE3nujfVr1zK4SRO4uKzMRGSTUmo+gG0A4gBsB/AjgAcA/KyUeg1UeG09Iag/kZZawaRGiRLAN9+w9M+33wJ9+5otkW+IigIOH06orA4c4Mzr7l1u07s3I+TKlWMib7lywEMP8bOdOrE3Xmion810kmDHDnZbfucdP20FNHAgNdITVsVl+NCsimzPHmDuXCqxI0dY2aNGDWDLFqZQhIX5/2+gSR23CraIyAAAAxKtjgZnaUGLfSuYYCmu6g6vvsreZ/37s3JChQpmS+QZRBitmVhZHTjAShP2Lplixais8uVjdNyYMUDbtgxhTyoOoWBBvh8a6t/nS4Q5hbly+Xkps0Qn+eAhhXnzqMD27aPFoFEjXqPPP8/vExHBWXHDhraZ8dq1NEHarzOT9etZceeJJ/xDHn9GVx9zAftWMDrBkveRiRMZFPLKK7xJpE9vtlSOExtLH6ihqOyX69dt22XKxC7cNWpwZlWuHJcyZVizE2CUX65cbG6aUm2/hg3pW6tThzOen37y6ld0md9/B1avZtX/HDnMliZlIiM5+Hj0Uc4mlQLq1wfGjwdeeIEDDXtq106oIH75BXjxRT4PCWH6SbZsPhP/Pm7d4vcQ4aBn5EgmdQdTLzxPosQP4qqrV68uW7ZsMVsMh4mI4E1oyRJWeNCQhQuB1q2Bzz4DBg0yW5r7uXEjaYV15EjC5qP589sUlf1SpEjKPsH4eH62WTOmLjjCJ58AQ4dSafhbgnVMDKvHp09va1Tpb5w5w4CaefOAzZu5zqji36YNg7UcpXZtYONG2+sSJcwNajp1ihYAezJnZu7i00+zhqQz3y8QUUptFZHqjmzrh5en/6NzzZLm+ec5MxsyhH+2mjV9L4PFwptAUr6sCxds26VLxxlV+fKU21BYDz0EZM/u2rE3bwauXHFOKX32GQdFr7/Okkr+lLQ7fjwV/bJl/qXILlxgkM28eTTDAUDVqvxf5snjWrrD1assVGykHGTIAMyaZa5pLyKCvtaYGA4oBg/mPWfpUpr1Aeb8PfMM/281alD+pMynaQE9M3MBEVY/6N79/lzNtM7Nm8DDD9Mkt307R5Le4O5d4NChhH4s47l9z7WcOamw7JVVuXIcdXvaFPrxx8CXX1KhOWOS27mTN6LWrRmo4A9cvcpgj8ceA5YvN1santMFC6jA1qzhoKVSJc7AXnyRAxMjiGPNGuf3P3gwfYIzZ3Iw5C+KICnFJMKBz9KlHAiFh/N85M4NVK8OrFjB14GS9pESzszMtDJzkYcf5kjQGCFpbKxaxRFlr17sD+UqIhyFJ55lHTjAG45BSAiVk72yMpbcuX3Xa6tyZc6sVq92/rNDhjD8fe5c4KWXPC+bs/TuzU7LO3fS1GgGN26wy/m8ebxBx8fTZ9muHc9R4sAZV5VZVBQDeKpXp4IINK5dYzmuJUs4YzVSDkJCqKSNDgeBiDYz+oBSpXSuWXI0asQQ/W++AZ57LvX6hDExtqoZiU2D9q1msmShgqpfP6HCKl3afKf4yZPA7t3s9+YKH37IgdFbbwENGtD3Zhb791ORde/ue0V2+zbrWs6bxxlhTAyr7nzwARXYI494fnAycyZw6RLw/vue3a+vyJWLfeXat2fPwSeeoOIHGAyTVtAzMxd59122jLhzxz+67PobkZFAtWo0D/XoweaeZcveH+J+4AD9AMafDwAKF046AKNgQf891+PHcyZ68CC/pyscOMCbT5MmDAgx67s+/TQ7ZB8+TB+Ut7l3j365efM4u4iKYmDDSy9xqVHDsXPhyszMYqHCzpyZeWf+en05Q0QELSK//kqLxR9/8DEQcWZmBhExfalWrZoEGmPHigAi58+bLYn/8tNPPEdJLRkzijz8sEjbtiKffioya5bIli0it26ZLbVrNG8uUqaM+/sZNYrnZ8oU9/flCn/+yeOPHOnd40RFiSxaJNK+vUiWLDxmvnwivXqJrFsnEh/v/D4bNODiDIsX89izZzt/PH/nn39Ecubked2yxWxpXAPAFnFQj2gzo4vYRzSaaRLyZ+yjBwGGEvfsyVlWsWJ+VqzWDe7coZ+wZ0/399WnD/1EffrQXOvL0PC4OFocSpWiz8zTxMYyIGHuXH7HmzdpIuvYkTOwBg18f0189RXPcZs2vj2uL6hfnzPsp57iuf3lFz4PVtJ4JT3X0eH5qdOwIX1ZISGMbvzf/6jQSpYMHkUGsCZlTIxn8sRCQoApU2h2fe01msF8xaRJjJL78ksgY0bP7DM+noq+e3egQAHeTH/7jekQf/zBAc+ECVTcvr4m/v2XFT/69g2sJH9nKF+eZseyZXl9Tp5stkTeQ8/MXKR4cT5qZZY8tWvzRhbsOS9LlrBSRL16ntlfyZIMJOnRA/jhBwaFeJubN5nz1qABFY07WCwMF587l9F1Fy8yeKdlS87AmjXznLK05/p1LhERjl1rX3/NnMJu3Twviz9RoACVdtu2/K5//EHfbKNGQfafdNQe6c0lEH1mIiKFCol07my2FBoziY8XyZ9f5MUXPbtfi0WkaVORzJlFDh/27L6T4oMPRJQS2brVtc9bLCKbNom8845I4cL0Q4WFibRpI/LLLyJ373pW3sSEh9v8senSiaxdm/L2x4+LhISI9OvnXbn8iZgYkUaNbOcpUyaeN38GTvjMtJnRDUqW1DOztM62bTSVeboUlVI0CaVPz/Y69tGenubYMRZG7tyZlTQcRYS1A/v3p5/tsccY1Vm1KqtnXLpEP02bNt5Lnjewj2CMi2MtxpQ6W3zzDU26b7/tXbn8ifTp+ZsYGO2HggVtZnSDkiXpL9GkXRYv5k2xeXPP77twYYZYv/IK/ToFC3rHXNuvH290Q4Y4tv2+fbaWKocO0df15JM0U7ZqZU5B4oYN6ZeNiaE8mTLRnNmyJTBqlM3HDdAUOWkS0KFD8Nc2tGfDBrbDSZeOA5EMGYKs9Y2jUzhvLoFqZvz8c07XIyPNlkRjFlWritSt6739Wywijz/O60wpz5uG1q7lvj//POXtDh0S+eILkUqVuH1ICE1WEyaIXL7sOXncITxcZOhQPkZFiQwfzrD/jBmZ/mGYOocN43fYudNceX1JfLxIjRoiBQuKrFxpO0/+DpwwM5quyCSAldnMmTyD+/aZLYnGDM6c4e8/fLh3j9O7t83PoRSViieIjxepVo0+rqR8WsePi4wYQYVtHL9ePeZYBkp+5dmzIh07UvYiRZjPWKAA/ZFpiRkzeA6mTTNbEudwRplpn5kb6PD8tI1Rx++ZZ7x7nPbtGf2nFFXKrFksneUuM2YAW7cCw4fbfFpnz9KfVLs2q0Z8+CHNUl9/zXqY69ax0kmg5FYWLMhyVevWsW5mx47A+fP0qaUV7t1jfcbq1YGXXzZbGu+hlZkbaGWWtlmyhCka3u4UXbs2ixcPGcLCsVevslTYF18wEdkV7t5llf+aNVkU+rvvGJZfpAibhUZFAcOGsf7opk1Mpi5SxLPfy5fUqwd8+y27XYSEsMllnz4sZhzsfPUV+76NGpVyP76Ax9EpnDeXQDUzWiwMne7b12xJNL7m3j36r3r18v2xL18WadeOZqMqVUS2b3d+Hx98wM9Xr07/FyBSoYLIoEEiBw54XGTTWb9eJDRU/gvdf/55fu/cuUUmTnStfFYgcOYM71EvvGC2JK4BbWb0DUrp8Py0yqpVLKZsRnfo3LmBOXPY2fv8eRbiHTjQ1vojOW7eBKZPZ7LsyJFcd+MGTVC7d7P6x2efsY1OMHH+PNC1qy29IT6e52zrVpZWe/11phXYd5kOFj75hKkKX35ptiTeRyszN9HKLG2yZAmrWjRoYJ4MrVoxTL5dO2DQIN6gt21LuM3duwyjf/55IF8+5pJt3szw9cWLGVo/eDAbXQYjCxey9+CpU0w/CAlhibWGDdml+Z9/6IM8d47m3C5d7q8pGqhs3QpMm0Zzqn1qQrCilZmbGMpMzO+ko/ERIlRmTZt6pyyTM+TKxUCO338HLl+mD+zDD5kD9tJLQN68DCDZvJnlsSZNooL74AMGrgRDy5OkuHOHpZtat6Zfc+dOlnQaPDhh92WlmG928CCTv2fPZh3DUaNc90f6AyL0c+bJw9lZmsBRe6Q3l0D1mYmIfPst7fAXLpgticZXbN/O3/ynn8yWxEZ0tMicOSIlS9rC6HPkEHnzTeaSxcfTx1u3rkjevCI3b5otsffYuFGkdGmmMXz0Ec+Noxw6JNKiBc9fuXIif/3lPTm9ya+/8jt8/73ZkrgHtM/Md5QqxUdtakw7LFnCxxYtzJUjLo4lm157jSbE9u1Z3aJZM/rVbt1iAeSaNWlemz+fVSAGD+b6YCMujhGedetyVrV2LTB0KCtdOEqZMky5WLKE+2valLO748e9J7eniY7mzLtixeAvopwAR7WeN5dAnpnt388R0IwZZkui8RWPPSZSs6Y5x46LE1m9WqRHD5E8eXjtZc0q8sorIkuX2mYhN26IdOtmm2GMH89GjaVLcx/BxtGjInXq8Pt27Mjv7y5RUawWkiULiyZ/9pn3CyZ7gpEjeR7+/NNsSdwHugKI74iM5FkcNMhsSTS+4MIFz1bhcIT4eJENG0TefpvVKwCGW7drJ7JwYcrl1JYvZ6dhw/SYPn1glDFyFItFZOpUKvTs2b3TMfr0aXbEBkSKFmUXAIvF88fxBJcuiWTLRlNpMOCMMtNmRjcJC2OxUm1mTBssW0a14O2qHyLAli00FxUvTtPZhAkMXJg3j9XP58xhRGNYWPL7adYMeOMN2+v4+OCplH7tGoNcunRhf66dO2lq9TSFCzMw5J9/gJw52ResSROmMvgbAwYwwOerr8yWxPdoZeYBdHh+2mHJEt7cHnnE8/sWAXbtYvRZmTIMtR8zhseaMYMK7NdfgRdfZFqAozRvzirySjH6Mhgqpa9aBVSuzND74cP5ulgx7x6zfn0OMMaPB7Zv5+/St6//VBHZu5cDnjffZIfpNIejUzhvLoFsZhRhg85ChcyWQuNtoqJEHniA/ipPsn+/yMCB9G0BrFTRtKnI5Mki16555hj2FeUDmagokffe43l66CHXm4m6y5UrvA6Uou9y0iTPVBFZulSkTx/XfqdmzRjBeuWK+3L4C9A+M98yaJDoVjBpgD//5O+8ZIn7+zp6lMqlcmX5rxp+w4YMpb50yf39ByN79og88gjP15tv+kcwxrZtTHcA2GJl0ybX9nPypMjrr9t8myEh7Np99Khjn1+2jJ8bNcq14/srWpn5GKO9wv79Zkui8Sa9e7Me4717rn3+1CmRr75iPUTjplWnjsiYMWxVokkai4X5nGFhnAUtXmy2RAmxWNgOygjO6drVsbzTe/fYkqZJEw5mjGsi8VKunMj77zOKNSbm/v3ExIiULy9SpoxzOXWBgFZmPmbDBp7JpUvNlkTjLSwWkRIlRJ55xrnPnTvHG7ERNm4U9x05kqNxTfKEh4v0789UCEDk6af9uzjBrVsi/foxYjRbNs6SEisfi0UkIkLkjTcYfQmIFC9OM/P8+RwshYby8eefRb75hsoufXpumz27yIsvsi/ZpUs8R88+y/d++82Mb+1dtDLzMRcu8EyOHWu2JBpvsWcPf+MJE1Lf9vJlkR9+oNnQGHFXriwyZIjI4cPelzUYCA9ndXtjAPD++/4bDp+YAwdEmjeX/zoR/P03BzUjRnAGBVBZdeoksmpVQl9bcr7NW7dEFiwQefVVkfz55T/TtL1ZcsMG335PX+CMMktnavRJkJA3L5sbHj1qtiQab2FU/Xj66aTfv34d+O03FvVduZIh8OXKsQr9Sy+l0egyFxHheYuL42ulWIMyUOpIPvQQUzgWLmRkYZMmtsaqdeoAEycyIjWpKiy1a9vqRtqTNSuLRT//PGCxMJryww95rQHc99q13H9aRSszD6BbwQQ/S5Ywl6lQIdu627eBRYuY9/XnnyyhVLIk0K8fFVjlyoFzA/YXIiNZnuvvv1mCS8RW5T5Q2LEDmDKF1fivXqUiiozk92nalN2uM2Vyff8hIbbmrOHhbKQaLCkX7qCVmYdIq8pszRrW+2vUKOkRZTBw9SpvGv/7H1vQL13KGdiyZbyRFCkCvP02W7FUq6YVmKucO8ck8H//ZU3FBg0422jY0P+vrStXqLymTqUyy5CB36VrV+DJJ/ndPviAfeemTGFV/uefd+9aqV2bM7M1awLjHHkdR+2R3lwC3Wcmwm7TmTMHjl3fE/zzj81mHxrKvKhg7Ng7eTK/Y9OmrNMH0G/Ruzf9FMH4nX3N5s0iBQvy/AZKIENsLCMrW7e2BWhUqyYybpzI1atJf2bNGpGHH+a2TZqI7N3rW5kDDegAEN+T1lrBWCz84yYOI37wQUZbTZoU2NF6MTGMTn3lFduNKnduRqGtXh2cxXrNYs4cht0XKyayc6fZ0qTOvn0iH3xgC8TIk4c5Ybt2Ofb52FgGi+XIwSCXd97xTGHkYEQrMxNYsoRnM9ArLDjKRx/x+6ZLx1lZWJjIgAGshmLk2xhVGnr3Fvn9d0Zk+TOxsSIrVrDafK5c8l9PsPTpWV0hqRwfjevEx4v87388z/Xq+WeyuBFd+OefTGivWdN23bdsyVmkq9fF5csi3bszKjFvXvbH07P8hGhlZgL79vFszpxptiTe55tv+F3feINmtsShxBYLQ9lHjRJ56imGIRs3gPr1WXF+40b/mN3Ex7N55Vtv8YYCsGRVx440IRlVPxYuNFvS4OL2bZHnn+e5ffVV/0z2Xbs2YXoAIFKpksjXX4tcvOi542zdastDfOwxmlw1RCszE7h3j2fz88/NlsS7zJnD79m6tePKKCqK+TT9+4tUrWq7MeTMKfLCC8zdOnbMu3LbYySu9u3LmppG3k/btuzQa1/h4913RTJk4M1X4xlOnGBZqpAQkdGj/c/PfPMmq7LkyGG7VpXigMdbslosItOn20yXr77qWYUZqGhlZhKFCol06WK2FN7jr79ocnv8cffqUF66RKX46qsihQvbbhilS/OGsXCh530IFgtHwP36seICQCXVsiVlSU5ZlS3LBFiNZ9iwgTPg7NnZa82f2L9fpGdPzswBkYoVaUJXioMdX7gQbt2iPy59ep6jb75J2+ZtrcxMon59LsHIv//yT165ssj1657br8XCm8iYMSwVZUQLhobS9DJwIG+AsbGu7X/3bvplypSR/0ydTz3FckCpKcyDB/mZceNcO7YmIVOmcABRurT/1DGNixNZtIiRhcYAp1Mnm6nPrG4DBw7QT2so1ZUrfXt8f0ErM5MI1lYwhw4xYqt4ce8XxI2Opq/ik0/obDdK9mTLJtKqlch334kcOZLyPg4epLm3YkX5r9RP48YiEyc61x7j66/5+ePH3fpKaZ64OFvblsaNkw9b9yVXr7I+ZokSlKtQIZHBg/3LtGexUNGWLEkZ27ShiTYtoZWZSQRjK5hz5/iHz52bSsLXXL3Kgquvv87QbcMkWaIEA1Dmz2fPr2PHRIYPF3n0UfnPx/H44yLjx7ueLvHEE3T4a1znxg2RFi34m/Tsab7JbOdOXktGUNLjj/P6MluulIiMpKLNlInLoEGud24INLQyM4lgawVz4wYd9Vmy+EeElcXCWeK4cfR1GSZJ+6ViRbZZOXPGvWNdv06T5EcfeULytMnhwyysmy4dw9rNIjZW5JdfqLiMYJ9u3UR27DBPJlc4dYo5nEal/QUL/C94xtM4o8xCzKo8EoyULMnHYChrFRXFcjx79wILFgA1apgtEUv/GMVZr10D7t7l8wIFgKJF+f7evcCgQSzwOm4ccOgQ1Zyz/PknC90+84zn5E9LrF4NPPYYcPEi8NdfQI8evpfh8mVgyBCgRAmgbVvg1Cngyy+BM2dY7PeRR3wvkzsUKcI6oKtWAQ88ALRuDTRrBuzfb7ZkfoKjWs+bS7DMzM6fl6BoBRMXx5B5gM0DzebKFZEffxRp1Ij+LyPf54svOFMzuH6dofU9etj8DIBI0aIcic+b57jP7OWXaVr1h1y4QOP77zkbK18+df+mN/j3X1ZuyZBB/isbtWhRcP2WsbGsOmRUEXn3XaYUBBvQZkZzsFhownjnHbMlcR2LhcoAYA6QWdy4ITJ1KsPijcTVsmVFPv2UCdmOcOQIb6ytW9saISrF5pgff8w6eUkl68bFsQLIK6949CsFPTExTK0A6Cfz5c01OpoDr1q1ePwsWSjLvn2+k8EMLl3iQE0pkXz5+J8JpioiWpmZSMWK9OcEKkYQy4cf+v7Yt2+LzJ4t8txztlF18eKUZds29/wDsbFMlB40iKWTQkNtN72nn2Y+z759PMb33/O9wYM9992CnatXOXM2Gmn6ahZ07pzIZ5/xRg4wBWPMmLRX6/Dff22KvFYtvg4GtDIzkeeeY1XsQOSHH3hFdO7sO8fyvXuMSGzTxhZhVqgQZ7cbN3pPjps3aXrq2ZMzPsMkmSePLR0gLCzt1Np0h337mDuWIQNnBt7GYmHuYbt2tll7ixYif/wRXLMSZ4mP5/nPl4/XcLdu/lnv0hm0MjORvn052g+0KKNff6U/6umnvR+mHBXFwsMdOtiqLeTNS8Xyzz/m3JCOH6dfrlIlm2ILDWXCrCZ5/viDOYB581LBeJPISBbjNUqiZc/OQc/hw949bqBx8ybz+tKl4zkaM8b1ogNmo5WZiQRiK5g1a0QyZqR54u5d7xwjJobli7p0sfmvcuVizs/ff/vPny08nOfClyWMAhGLhYWkQ0KYvuHNdj8nT7Ku54MP8rqpUIGmYF0vM2X27RN58kn5L2Bq1SqzJXIercxMxJ9awYSH0+9jL8uaNQnL80ydypt38eLOVcdwhLg4/oG6d7fdiLJloxlz2TL/TVQ1q4RRoBAVxbqaACvfe0OpWCzsG9e6NRVmSAgrwKxcGXhWDzOxWFjr1KhH2rZtYPUZ1MrMRPylFcySJTbfj1KsjFGunPxnQgsLYy1E43XGjJ65ecfHi6xfL9Krl60CeJYsIu3bs/dTMFVHSYtcvMgAGoCRpZ42Cd+5Q9+tYe7NlYsBQGmtjJOnuXePJd6MKiJffBEY/0WtzEzE7FYwu3Zx1Jy4D1OJEiJFiiRcZ7+44x+yWFgh5N13bVXww8KYq/bLL94zXWp8y86dLCkWFiYyd65n9330KK8fo+1KlSoikyennbJNvuLECc7OjHvCb7/590xXKzOTKVjQt61g4uNFli61Vf7OlInmn4wZqaQyZWJwQ+PGCRVYvXq8MRnbODMzs1hEtm+nL8NIUE6fXuTZZzkr9feu0hrnWLiQM+yCBT0X9h0fTz/q00/TepAunchLL3Fm78832GBg5UpbIe5mzfy3BJ9WZiZTrx7rwHmbX3/lsYoW5S9ZsCBnV4bva9w4jqQNe3n27IwgfO89W/HXZ5+lycFRRfb55yJ169qOGRrKP8OUKZ5tDaPxDywWkSFD+FvXqOGZrgk3bzJQykiJyJuXJkt362lqnCMmhpGO2bNzIPH++/5XRUQrM5N55RWa27xJeLjNJwaIDBiQsJpFeLit9BPAEGb72ZLFwhsIwP5ejjjxhw+37S8khI0uL1/2+FfT+An37nHwA/DRXZPfgQP0pRrpGDVrsjh3VJRn5NW4xsWLIq+9xvtJ/vzs9ecv+XrOKDNdaNgLlCoFnD3LYr3eYvlyWwHdkBAgY0YgQwbb+7/+ClgsfK4UkCcPkDWr7X2lgM8/B378kYVgGzQALlxI/ngTJgAffZTw8zlyALlze+wrafyIc+d4TcyeDQwdCsycCWTK5Px+4uOBxYtZELdcOV5vzz8PbNrE5eWXee1qzCNvXmDSJP4exYoBnTsD9eoBW7eaLZlzaGXmBUqWpKI5ccJ7x7h0iY+GImvY0PaexcLK2sb7YWEJ37fn9deBRYuAAweA2rWBuXOBYcOAiAi+LwJ88gmrntepw32FhlJxJrXPiIiEn9cEHlu2sEvCvn3AwoUcxCjl3D6uXwe+/hooWxZ47jlgzx7giy+A06eB6dOBmjW9I7vGdWrUAMLDgSlTgKNH+bp7d3YfCASUGMN7E6levbps2bLFbDE8Rng4ULcusHQp0KKF5/cfGQkUL87WFi1bUqnUrm17/7vvgJ49gY8/ZquIxO8nxZYtQNOmvAkBVIKVKwPnz7ONR4ECQJkywO3bwI0bnJUZ7VgMbt0Ctm/n80yZgJUrUz+uxr+YNw/o0gXIlw/4/XdeA86wezcwdixncpGRQP36QO/ebCeUPr03JNZ4g5s3abn59lveQz7/nG2V0qXzrRxKqa0iUt2RbfXMzAt4u6/ZlCmcmY0YwVGzvcI4fhzo149mncGD738/OapXB157zfbaYuFs7eJFKs2yZW39xIoWvV+RAVRyBjExwJo1rn5Dja+xWIDPPgPateO1sHmz44osLo5m7YYN+ZkZM4AOHTiw+ecf9hLTiiywyJ6dM+tdu3g9vP02ULWqn/+nHXWuJbUAyAFgPoADAPYDqA0gF4AVAA5bH3Omtp9gCwDxZiuY2FhGJ9aufX/4ssXC8PusWV3L8g8PZ6h+SAidwaGhdAY783kj6ESXggoc7txhpQ1ApGtXxwMyLl9m9KyRv1ismMiXX3q+kozGXCwWRk4XK8bf+cUX2fXaF8BX0YwApgHoZn2ewarcvgTQ37quP4ARqe0n2JSZiPdawcyYwV9t0aL735swge/98IN7+8+eXSRzZpG//nL+8yVLMvF1wgTXZdD4jpMnWVsxJIS1Fh3J79qyhSXJMmbk9da4MZNvg6n5peZ+7t0TGTiQA97MmVkqz9tVRHyizABkA3AcVr+b3fqDAApYnxcAcDC1fQWjMnv2Wc+3gomPp5KsWPH+0NmTJzkja9TI9YTT1aupyAoWFNmxw/nPh4fbQvd1+xT/Z8MG5nhly8bq9ykRHc1ec7Vry38lyt58U2TvXt/IqvEfjh+3daIvWZIDa28luTujzNzxmZUEcBnAFKXUdqXUJKVUFgD5ROS81YR5HkDepD6slOqulNqilNpyOVDCZZygZEn6zDwZX7N0KbB3L9C/PwM0DEQYdWSxMMTW2cgzAJgzh362QoUYifjII87vY/Zs2/PYWD+3r6dxpk4FnniCvs+NG4HmzZPe7vx5YOBAhmx36MDItm++YerJd98BFSr4UGiNX1C8ODB/PrBiBSOpW7ZkoNvBgyYL5qjWS7wAqA4gDsBj1tdjAHwB4Eai7a6ntq9gnJmNGcORy8WLntmfxcJRcfHi97dL+eknHmvsWNf2++WX/HyDBiLXrrku4xNPyH8J1dpn5p/ExbHSg2EevHr1/m0sFv527duzRJmRWL9smf8k02r8g5gYkdGjObtPn17kgw9EVqzwXNcJ+MjMmB/ACbvX9QEshTYzioitFUxEhGf2t2YN9zd+fML1Z87QNPj4487faOLiWJEBYNdedyox7N3LoJHOnXX7FH/l5k1bGbOePe9vwRMZyZZA1arJf+16+vbVzS81qXPhAoOH7Gu/esLV4Iwyc9nMKCIXAJxWSj1kXdUYwD4AvwPobF3XGcAiV48RyBjh+UePemZ/w4YxU79rV9s6EeCNNxgGP3lyQtNjakRGAm3aAOPGAR98AMya5V4lhqFDmVv21VeOpwNofMfRo0CtWsCff9I8OG6cLVz+9GnmJBYpwhyzyEhuc/YsMHo0ULq0qaJrAoDs2ZmakSePbV10tI9dDY5qvaQWAFUAbAGwC8BvAHICeBDASjA0fyWAXKntJxhnZp5sBbN1K/eVuEXL9OlcP3q0c/u7fJkmS6VY8NVdDh+mafG999zfl8bzrFrFvmC5ctm6DVssnO2/8AJTMHTzS40rnD4t8vHHInnyyH/pGenSea5TO3ShYf/AU61g2ralyefGDdu6c+dEcuYUqVPHuZDoo0dFypShCWDBAvdlE2GR0owZRc6f98z+NJ7jhx94cylfXuTIEeaUTZjASFuj+WW/foxQ02gcwWIRWbtWpE0bDoSUEnnuOfrKDH9rQPnMPLkEqzLzRCuYgwd5sfTvb1tnsXAUHRbGSuSOsnkzQ7Fz5WJYtic4eZI3y169PLM/jWeIiaFfDKCfbMcOzpyN5pePPCIyaZJufqlxnLt32RexcmVeQzlzMpjo2DHvHdMZZebjSltpi5IlbQV/XWXkSPqy+va1rZs3D/jtN+DLL4GHHkrukwlZuhR48UX63ZYvd/xzqTFiBFMB+vXzzP407nPtGn/rlSvpF42KAh59lD7VF15grcS6dV1L4dCkPY4fpw918mTWbq1cGZg4kakamTObLZ0djmo9by7BOjMbOJCzKlez5M+cYbjrW2/Z1l28KPLggyKPPea4eXHCBPpEqlXzrCnw7FmaF19/3XP71LjH/v1MZE2Xjr2pjOaX//ufbn6pcRyLhRWAnnvOVtquTRuaF33pU4WemfkHRiuYkyddmwmNGsVE6Pfft63r1YuV63/6ia1YUkKExWMHDwaeegr4+WdWwPYUX3/NIrP9+3tunxrXmTSJ3RJiY/nbFy3KmX3btrpnmMYxbt9mi55x41hoPE8eRrq+8QajXf0aR7WeN5dgnZmtX8+R8bJlzn/2yhWWDOrY0bbul18kyajGpIiOZsdrQKRbt/sTrd3l0iXWZ+vUybP71ThHfLzI77+LPPSQ/Jff06qVyKZNZkumCSQOHhTp3Zsl8QCR6tVZZNzbtRdTA3pm5h+UKsVHV1rBjBsH3L1rm/VcuQK89RZQrRrzwlLi1i36SlasYB+i//3P8/6R0aOZj/Txx57dr8Yxbtzg7HzcOPo0AKB8eWDJEluOo0aTEhYL8Mcf7D/355/MO2zblj7Vxx4LPJ+qVmZeJF8+JhI7mzh95w6b4j37LFCpEte9/TZvYCtXptwg79w51knbu5d9z7p0cVX65Ll+nTfRtm2BcuU8v39N8uzZw3M/YwZw756tr9zHH7OTszOJ85q0yY0bvDeMH897U4ECwKBBrO+aP7/Z0rmOVmZeRClbwWFnmDSJEWkffcTXixaxEPCgQcDDDyf/ub176Ru7fp0j9GbNXJc9JcaOpW39k0+8s39NQuLi2PV57FhWVAgL4++8cSN/6zlz2FRTo0mJxAOhunXpT2/dGsiQwWzp3EcrMy/jrDKLiWFgxeOPsyTUtWtAjx6sYm8ot6RYu5at6cPC2N330UfdFj1Jbt1i1fSWLR3vRKxxjStXOLD57juWnCpWjKkQBQqwhX327Pyta9QwW1KNvxIXByxezIHQ6tUMBOrQgYFkVauaLZ1n0UYJL+NsK5iZM4EzZ2yK6513eFObOjX51vPz5gFNm/Imt3Gj9xQZAHz/PWcDelbmPbZtYw3OwoV5HZQpAyxcCBw5AsTHA507s/XKv/9qRaZJmitXgOHD6bdv3ZrXzrBhvLf89FPwKTIAOprR2zjTCiYuTqRsWZFHH2Uuh1F5/9NPk97eYhH56ituU79+0u08PMndu6zB1qyZd4+TFomJEZkzh+XJkmp+ee+eSIcOfK99e125Q5M027axen1YGK+VJ54Q+fVXz0cz+wroclb+w+LF4nArGCP0ft48kevXRQoVEqlUiWH2iYmLE3n7bW7ftq1vQmi/+YbHW7fO+8dKK5w/z+T6AgV4bkuXZuHo69dt25w7J1KzJt8fMkQXAtYkJCZGZO5ckbp1eY1kzizyxhsiu3ebLZn7aGXmR+zdy7M8a1bK21ksIlWrsghwXJzIq68y6/7ff+/f9t49kdatud933/VNw8TISBZObtjQ+8cKdiwWDm46dEjY/HLp0vt/yy1bOKjJksVzhaE1wcH58yKDBtkGQiVLiowa5V6DXX/DGWWmA0C8TIkSfEwtCOTvv+krmTiRz3/6iTlm1asn3O7qVeC554CICOZ62dds9CZTpzLsf/p03xwvmIiIYBRi7drAqVN0xm/ZwrD6t95i1Y4yZe7/zLff0leWLx+wYQODgDSaTZt4Df38M6u9NGvG+8ZTT6Xx1AxHtZ43l2CemYlwRtO1a8rbPPEEt7t0SaRIEbbsSGw6PHaMPrWMGWmS9BUxMexTVKuWNnE5Q0wMTcbG7MtYypdnx/Bbt2zbxsXRnLhli8jw4aylCfBxyRLzvoPGP4iKYkWOGjV4XWTNyoodznTNCESgZ2b+RcmSKSdOb9rEsNmvvgI+/ZQdfjdsYJi9wZYtwNNPcyT2999AvXrel9tg1izWl/zuu8CrCuBtLBZGiB0+DBw6xMV4fuwYow/tqV8faN4c2LePUYlnz3K5cOH+bQ127eJvr0l7nDkD/PAD8OOPwOXLLFIwbhzwyitA1qxmS+dfaGXmA1JrBTNsGJAzJ4sRv/8+l1q1bO8vW8aWHrlzM5/Ml1U34uOBoUMZ7v/UU747rj8hwhuJvaI6dAg4eJCvY2Js26ZLx2LO6dMzD+z2bQ5ADNat45IjB1CoEFCwIMPsjeeFCtlKl8XGMi+oYUNff2ONmYjwGhk7lmZmiwV45hmWmWrSRA8ok0MrMx9QsiSz7qOiEs62AFbtWLSI/rHevYGyZVlP0WDSJFvS9NKlvi83M3gwb9hDhwb/n+jmTX7XAweAnTv52xw+zJlTZGTCbUNDk55JhYZyYGIopkKFqJQuX2YifNOmfC+1PlAVKtDP1rAhfW3+RkQES6s1buyf8gUi9+4Bs2dz5rVzJwc877zDBHldbzN1tDLzASm1ghkxgje2y5f5/rp1rOcoAgwcSMXWvDmdvb42K3z3HWUAKIe/3lid4fJlmnW3bqWp79gxKqtr14Do6OQ/pxRvLgULAsWLM6HZXmEZz3Pl8ozSr13bv851ZCRnovv3s4D11Km8Rj//nNYCf5I10Dh+nMUIJk1iQYKHH6ZZsWNHP2t+6edoZeYDjFHVsWMJldnJkxyJtWrFLq59+rBeWmwsi35OnQq8+ipt5slV//AGO3eywsfSpbZ1sbG2iDx/JDYWuHiRiunUKdah27+fN4oLF6isIiOTrsQSGkrTYOHCXEqX5syoeHGbksqf37e/gVncuMHzZiz79vHxxAnbuVPK9jw2ltenv14X/ooIZ7Zjx7LcVEgI7wO9e3MGH+xWEG+glZkPsFdm9nz1FS/iLVu4zZAh9LG0aQP89RdnRZ995rsL++hRHm/OHPp7evSgQo2NZSFSM3w3IhytGoES587Znh89ygHBxYusGZkcoaEMgy9ShEvZshz91qjB52nNkS4CnD+fUGkZy4ULtu0yZuTgq2ZNBquUL8/l6lV2ZoiO5r5mzgTq1GEDR03K3L5Nl8O4cTzfuXOzZFmPHgHQ/NLP0crMB+TPT1+ZvTK7dIlmhTJlOPpdvZo35BYtgN27OVN79VXfyHf+PNuHTJzI2ceHHwL9+tH388or3vPdREbalJO9krJfd+4cfY2pkS4d87GKFeMN+JFH2PvtoYd4w0iLI934eM6oklJaN2/atsuWjUqqeXPOSA2lVbx48t3MV67kdVGzJgtj9+jBYshffJE2z3VqHD5MBTZ1Kv/n1arx+Usv3e9H17iGVmY+IKlWMGPGcGS7bx+TZvPlYwTj1ats39K8ufflunED+PJLVsGPjQVef52pAQUK2LZxxXcTH09lnZKSOnuWM67EpE9v+3NHRSWMBAwNBYoWZTRn+fIcCJQty8dChdJuwmh0NG+WhknQWA4dSjgQyJ+f561jR5vCKl+ev7ezCsj+umjQgEEKQ4ZQoU2cGBwtRdzFYgGWL6cpcfnywG9+6e9oZeYjSpWy5ZrdusXGeFmycNbw7LM002TMSGd6tWreleXePf7Bhg+nQmvfno780qVT/pwIZU9OORmPSeVMhYQAefLQpJchA2+s2bJRodmbCGNjeXMtW9amqIznxYun3Jg02Ll9O+lZ1tGjvHECvEEWL04l9eSTCZVWzpzekStdOgYsFC1KM/W5c8Cvv9oah6Y1Eje/zJ+fLoPu3RMOFDWeJQ3fGnyLkWsmwsglw8zzzjvsDVa8OFuYG+WvvEFsLM2Xn39O02KLFhxNV6nC0f2JE0krJ/vn9+7dv99MmfiHLV2adv906ajM7t7lTNPYx8WLXADORMuUYWi3vdIqVYr7S2sYJa8aNOB5TKyw9u3jOTRIn57n65FH2JjTUFhly5oTAacUZ/VFinCGX78+8yMLFfK9LGaxd6+t+eXduxygfvEF8MILeqbqC5QkFd7lY6pXry5btmwxWwyv8u23jFY8eZIJyNeu0ay4aRMv+kWLgAcf9PxxLRaa/KZMoQwXLvCG88gjVDiGkrpy5f7PZszIkWTevBzVZ8vG2WSGDFRYFy4ACxZwW6VoBoyLs30+Wzb6rOxnV2XKcMme3fPf1d+5e5epAVeuJHzcsIHJsUn9FR94wGZWtV9KlfLfWeqffzKIKWdODtAqVjRbIu8RH29rfrlqFf8z7dvTlBiUPcN8jFJqq4hUT31LPTPzGSVLCACFb76hIgsLE2zcqPDCCxzJuTIbuXMndb/UuXMJFQzAEjm3btHklzkzZ1WFC1PxxcXRz3LnDs0lJ05wSQp7m78I0wo6d7YprTx5gtcvEB9PE+nly0krqKQeEydeG9iHuivFGfPbb1NpFS4ceOewWTN2wG7RgtfEokWccQYTV6/auoCfOsUB4tChQLduvO41vkfPzHzBwIHYdzQjKs78CJky2W5qfR7biK831LovYiw2lrMeeyV19iyVyunTXH/pUtImv3TpOHMSoenQ8KWkRLZsHEXnzMmk38TPk1u3Zw/9MjExPObKlYGbbxQZmbQSSk4xXb2afPfwrFnpC82Th4vxPLnHvXuD5zzac/IkS6AdPcpuCy+9ZLZE7rN9O02Js2dz0NewIWdhzz3nvzPlQEbPzPwJEeDGDRSf+SOAj/5TZK3xC9LH5sLLHQXnLyhcusSR/s2byY/gUyJjRpoAAc6ubt3ibK92bfov8uRJWjHlyOH6n7BuXVuItj9VB7FYeC6TmyUltS6pgQFA0+mDD9oUU6VKKSumBx90PtTaX8+juxQrBqxfz2Tgdu1oEXj33cCbacbG0pw+dixNwpkzM2WlVy/mK2r8Az0z8wUiONRpEB6aNQBAyv9kpRj5FxpKJ3+GDAmXdOm4pE+f8PmdO8COHbbZQo8eTMo2FFwgExXlmBnPUFRXryY/I82SxfEZU+7cVPZpNeTfU0RF8eb/yy80n44alXz+mj9x8SKjNH/4gdaQkiWZRtO1q/ciQzUJ0TMzf0MpFPhuADDLfqVAKfVfwVpDCYnwdXw8zU5379pMh+nTJ71kyMCbubGPkBCGSfujIrNOVB32M12+TEWdFCEhtllT7tz0MRmz0KQUU+7caTNS0mzCwoC5c+lXGjWKM7SZM/33t9i0iabEn3/mf7BpU2DCBJpMA0EJp1W0MvMFIsj62TsohPdwFkXsVyMujjfgb76hP+zvvxmanTdvyiayixeTn31YLDSJLFiQ8szDkdmHETKenPkrJsZxP5PxmFzfrsyZE8r30EMpy58zp541BQohIawUUqQITY1NmgC//+6dCF5XiI6m8ho7Fvj3X0aRdu9OU2Li4uAa/0SbGb2NCJPJxoxBg0KH8c/Z0siSPgp3Y8OgYIFAIbHpMWNGlrdKyXdisdw/w9m0Cdi2jb6w9OnvVy537ya9L3u/kL2ii4xkpKXFwm2aNqUZ1F4xJVcTUSnK4YxJT1cITxvMnw+8/DJ9asuXeze3MjXOnrU1v7x0iYqrVy+aRdNq0rc/4YyZUSszXzBwIHDjBl6/MxqTJlNxtSm9HfOPPIqQEIaxHzyY8COtWjFiypOmmMhI531P9pdH9uzMb3JEMeXKpU0ymuRZv54RgOnTsztDdYduV55BhMc3rBcWCzt5G80v9Wzff3BGmUFETF+qVasmQY/FIt98IwKIZMki0qSJRQYM4GtA5JVXRJSyvQZEsmYV+d//RM6dM0fkdetEMmakXGFhIuHh5sihCU727xcpVoz/h6VLvX+8u3dFJk0SeeQR/r9y5BB57z2Ro0e9f2yNawDYIg7qET0z8yELFwKtW9Oscvw4SxTNn896dgArB+TIwfYvRh1HgCPFl18GGjWi87xRI9+Fb6fmM9No3OHCBc6Kdu4EPviAvipPX98nTjC5efJkFiyoVImzsI4d/TNISmNDz8z8lPBwjgjz5hXJkEGkZ0+uHzzYNht78UWRqCiR1atFSpWyrbeftWXMqGdJmuDh1i2RmjVt13emTO5f3xaLyN9/i7RsKRISIhIaKvLCC/xfWSyekFrjC+DEzExbh31I/vx8vHSJM7Rp0xhA8cknLEgKMKKqaVMW/z18mKWAChVK6LuKjuZsSaMJBrJmZecIA3eu7zt3OAurWJH+rw0b2J/v+HFaQRo2DLykbY1jaGXmQ/Llsz1/8kn+8aZO5ev//Q8YNIjP161jVYjTp+kkP3XKZoo0mDaNIcQaTTDQuLGtsrxSznc1P3wY6NuXA7+ePRk4NWUK/0NDh+ouzmkBrcx8SObM9AkAzC977DEmZxr5Yp99BgwYwFnY4cOsqr9zJ31mgwaxeGv9+tz24EF2+S1YEPjpJ3O+j0bjKWrX5mzs8ceZh+hIyxSLhW1mnnqKxa3Hj6f/LTwc2LIF6NJFd3FOS2hl5mMKFmRFj127mM9y+DCwYoXt/QED2BcqNpZ1GuvVYyI1QEX2zz+sc2eED58/D7z2Gqur//RT8gnJGo2/U7s226nkycPUzORi027eZJGBhx6i8tqxg/+bU6eYzlK7tjYlpkW0MvMxBQpwhrZrF1uo583LfBcDpTgL++QTFr9Nnx5o3pzJywYjR96fw3X2LJVa5sz0xy1e7FrBYo3GTLJlo/943TpbrzyDffuAt96iKfGdd6j0Zs9mdf6BA3UX57SOVmY+Jn9+Kqzdu2lKeeMNmkrsQ/GV4h+6f39Wf8+XjxUJhg7laLVwYaBTJ24bEkL/wIIFbIgowhSA555jmH+lSjRfXrtmytfVaJzmtdd43b79Ngd2w4czmKNiRVof2rShGTE8nOksuouzBoAOzfc1ffowtB4QOXVK5OxZkXTpRN599/5tLRaRfv24bdmyfHzjDZHYWJE7d0Ry5xYpXFhk/XrbZ65dE/n4Y9sxjCU0VOSJJ0TGjBE5ccJnX1ejcYnRoxNev3nzigwZInLpktmSaXwJdGi+/5I/P0OPAZoaCxakWfCnn+6vnagUR6Xvvw8cOgRUq8bq3c8/z/e/+opJ1PadoHPmBIYMYQKqPenScTbYpw9QvDhbug8axAATP8ib12gSYG8iDwlhkvPHH+suzprk0crMxxi5ZgCVGcA/6o0bbIuRGKWAL79kpfGtW4EnnmAtuyeeYHv6atVojkzcXLJFC5oflaLf7dFHbabGIkXoRB80iPlsJUvSB7FmDaMsNRqzadiQBbdDQvjYuLHZEmn8Ha3MfIzhpM6f36bM6talUhk7NulZklKchfXpw2r6zz7LWVa9elRyZ86wvYY9tWuze/GQIcDatSxLdeYMg0eyZweOHeNs7dFHWRj4+++pIPPlY0jzb78l331Zo/E2tWvzWh88mNexLqWmSQ1dm9HH7NxJxVW9Ok0pe/Zw/U8/0fG9enXyCaMiVGhjx9Lx/ddfVHSVKgGbNzPMv2DB1GUQYTjztGmMBrt8mS1gHnuM74eHc6YYFsZqJK1aAc88o008Go3Gt+gWMH7MxYuclTVvzvyyu3dpRomMZJRiw4bAr78m/3kRmiXHjwdef52j1nPnaB58+WVWPXCG2Fj2lJo2jeH8MTFAhQqc9cXFUcbTp2nuqVuXiq1VK5omNRqNxps4o8y0mdHH5M5tC6ePjwf27+f6TJmAbt1o3jt1KvnPK8WZ2ZtvAhMnMmn04Ye5r6lT2ZzTGdKnp9ly/nwmYH/3HWvl/fgj91e+PE2V/frRz/bee+xpVrkyk7u3btUBJBqNxny0MvMxoaFMlDYqeBh+M4AJoQD9VymhFMtgvfEGFVuDBjQHApw1uVoFJFcuKsmNG4EDBxhYsn8/E7i/+w6oUQOYO5f+uZw5mfdWvTo7BvfuzVlibKxrx9ZoNBp30MrMBPLnp1kxY0YGchgUK8Zk54kTgaiolPcREkIF8/rrDA6pVo2mwdOnGchhhP+7ykMPcUZ24gSV1PPPU5G1a0dF+sQTwKZNNGtWrcpeUU2aUFG//DJnerdvuyeDRqPROIpWZiZQoADbwFSsmHBmBrBe49WrVBypERIC/PADA0eGDqW/LW9elgJq1oxBHO4SEsJmiVOnspHitGn0l33+OWdqkyczOOToUZpIW7WiD65tWwaMPP00lfPFi+7LotFoNMmhlZkJ5M9PxVC58v3KrFEjBmAkF6afmJAQ+re6dmUYc7NmXL9+PQsTnz7tObkfeIBltf7+mzO2IUOolF9/nQpu7lzgpZeYArBmDc2m+/YB3btTgdety9SAQ4c8J5NGo9EAWpmZQv78nKlUqkSldumS7T2lODvbto25YY4QEsLZT+fOLEhctixNmCdPMj/H3pTpKYoWZUWGAwfoY3v1VeDPP9mOo0QJYMkSKthjx5iOMHAgTaf9+tGEWaECP795s60Fjkaj0biKVmYmkD8/AyWKF+frxMqmUydWDx83zvF9hobS5NepE2c+kZFULAB9aatWeUT0+1CK+WnjxzMacv589ln75hvOPKtVY+5cjx6MfDxxAvj2W87UvvySny1ShIEnf/7J1ACNRqNxFq3MTMAoaZU7Nx8TK7MHHuCs5pdfqCAcJTSUARkdO9JE+csv9HUVKcK8tlmzPCJ+smTMCLzwArBoEVvSjBlDZWd0AH7uOXbH7t6dQSWXLnEmWbs2H5s3p5+tXTuaLG/e9K68Go0meNDKzAQMZRYby/JRif1mAFu/x8WxsLAzhIYySKN1ayq0nj3pP6tbl1GGw4f7Ji8sb1628Ni6lcr6nXfYtqNtW87K3nqLM8iOHTmbu3yZSdsvvshZZPv2VGzNmzPI5dw578us0WgCGEfL63tzSUstYEREDh1iW4sZM0SaNBFJ7us/9ZRI/vwi0dHOHyM2VqRKFR6nWzeRqCiR9u35+s03ReLi3PsOrhAbK7J8OeUIC6MsDz3E1h4nT9q2i4tjW5v33xcpXdrWBqRmTZGhQ0X27WN7HI1GE9xAt4Dxb4yZmRHRuHdv0onOvXtzm5TKWyVHunTAhg00WU6axOjImTOBDz9kUnbr1r4vJJwuHaMtZ8/m95o0iTO4Tz6h/7BxY84qIyMTRj7u3cvISREGjVSowCCSfv1YR1IHkGg0GtNnZZIGZ2YWi0jmzCLvvScydSpnHQcO3L9dfDxnJrVru36s+fNtM5sxY7hu3DgRpUQee8w/mh0ePSoycKBIyZKUM0sWkVdeEfn7b54De06fFhk/XqRpUzY1BUTy5RN5/XWRJUtEIiPN+Q4ajcbzwImZmemKTNKgMhPhjbtDB5Ft2/gr/Pxz0tsZHXe3bHHtOBaLSIMGIhkycD9jx3L9woU09ZUuLXL4sGv79jQWi8i6dTSLZstGeYsUYefspJT99esis2eLvPSSSNasNkXYpo3IzJnsuq3RaAIXZ5SZNjOahJE4Xb48gzaSCgIB2FssSxbnwvTtUYph8jExLBBsVNxv1YqBFtevM5pw0yYXv4gHUYppBBMn8tzMmcMqKcOHA+XKAbVqsYSX0WQ0Rw4GisydywCSP/5gkMv69XzMm5cltsaN82zyuEaj8T/cVmZKqVCl1Hal1BLr61xKqRVKqcPWx5zuixl8GMosLIxJzskpsxw5mDs2Zw5v2K5QpQpLXp08Sb9Ur16MEKxdmz6nbNlYa/H33139Np4nUyaG6P/xh62p6N27jM4sUIApAL//bitsnDGjLfLx7Fkmcr//Pp/37s0k72rVgC++YHSl6Er/Gk1Q4YmZWR8A++1e9wewUkTKAFhpfa1JRIECVGZA0mWt7OnVi4WDJ01y/XiDB1NxZsnCWopvvskyWGXLstJIpUosJvzDD64fw1sUKEDFtGsXK6O8+SbrT7Zsyfy1vn253lBQISFMxh42jFX/DxwARoygwvvsM57v0qXZzuaff1zvMqDRaPwIR+2RSS0ACoMKqxGAJdZ1BwEUsD4vAOBgavtJiz6zL76gjycqiqHpgMjNm8lv36gR/Uexsa4f0zjO8uUiLVrw+cSJfO/OHZGnn+a6jz7y/9D3mBiR338XeeEFmz+wUiWRL78UOXs2+c+dOycyYQLTHozP5c4t0rWryKJFInfv+u47aDSalIGvAkAAzAdQDUBDO2V2I9E215P5bHcAWwBsKVq0qJdPif8xcSLP/qlTIosX8/mGDclvv3Aht/n1V9ePee+eSNGizD+7c4c3dEBk8mS+HxvLqEBA5OWXXctvM4OrV0W++06kVi3KHhIi0qwZg0Pu3Uv+c7duMfCmQweR7Nn52UyZRFq1YpTplSs++woajSYJfKLMADwD4Dvrc6eVmf2SFmdmhgLbtIkJw4DI998nv31sLBVRw4buHXfOHJsCi4zkTV8pkSlT+L7FIjJ4MLdp3Fjkxg33judrDhwQ+eQTzmIBRkW+9prIP/+kPNuMjhZZsUKkZ0+RQoVsSrFBA0aUHjvmq2+g0WgMfKXMhgE4A+AEgAsA7gGYqc2MjvHvvzz7ixbxJps9OytzpMTw4fzMrl2uH9diYd5a/vycmURGijz5JBXatGm27aZOZR7Xww8ztyvQiI8XWblSpHNnhusDIiVKiAwYwLy2lLBY+Pv87380XRp5eo88ws9v2+b/ZliNJhjwmZnxv50knJmNBNDf+rw/gC9T+3xaVGanT/PsT5jA1/XqcUmJK1eYG9a9u3vH3riRx/7kE76+d49ltZRiiS2Dv/5i/lbhwiK7d7t3TDO5fZuKunFjfkeA53riRMdmnocPi3z1lUj9+rbPFy0q8vbbIqtWuefH1Gg0yWO2MnvQGhRy2PqYK7XPp0VlFhPDsz9oEF+/9RZNYqmN+F99ldVD3E0I7tCBitGoiXj3LoNMQkJEZs2ybbd9u0iBApw5rlrl3jH9gZMnWd/xoYd4/sPCRNq1E/njD8eU0sWLNNE++6ytvmTOnCKdOtGfeeeO97+DRpNW8Lkyc3dJi8pMhAriscdEwsNFfviBv8aJEyl/xqgY8vXX7h375EnejNu3t627e5c+uZAQ+tbst61QQSR9egZVBAMWC/2Vb71FZQRQab//vuOz0Dt3RBYsYOktYx9hYVR0kyZR8Wk0GtfRyiwACA+X/3wxYWE0NwIMN0+NunVZDsvdyveffMJjRkTY1t25I/L441Roc+fa1l+7xvUAw9+DyWcUFcVZ1XPP2eo9PvqoyDffOF67MjaWM9c+fUSKFeM+lKI5c+RI/ykZptEEElqZBQBGzpexGDlegwen/tm5c7nt4sXuyXD7NgNBatVKqJxu36Z/KDQ0Yc3IqCjWQQREevUyp42Mt7l4kQWZq1bl90yXjjOt+fP5/R3BYqF5dsAABo0Yv3HFihxA/PtvcA0GNBpvoZVZABAezpymkBAuAJN4GzdO/bMxMTSJNWvmvhyTJ/PYic2Ht29zVhEayhu5QXw8TXEA87FSyuMKdHbvFvngA55rwzf25psMoHFGGR0/zlmeYcIFGP7/1lsMsgmUfD6NxtdoZRYghIczGGHDBvqojICCTp1YqSIlBg2SZFvHOENcHJOoixS5XzHduiVSpw5nJwsWJHxvzBia0WrXFrl82T0Z/J2UmoqeOuXcvq5cYWTl889zMAPQd9q+vci8eSlXgdFo0hpamQUoH35IBZE+vcgDD9A3ldyo/fx5bte7t/vHXb2aV8KQIfe/d/MmzZDp0on89lvC9379lTf3MmVSz90KFm7cYHBH/fryn1+sUSMqqNu3ndvX3bvMM+zalSW1jNn5U0/Rh5ragEajCXa0MgtQfv6Zv8hvv4k884xtBrB8edLbd+zIPLBbt9w/dqtWVKDnz9//3o0bjLpMn/7+AJX160Vy5RLJm1dk82b35QgknGkqmhpxcaxS8u67tv0BHEgMH+7+DFyjCUS0MgtQDhzgL2KUllq6lLMeQKRlS5FffqFZMjyc7xvJz+PGuX/sw4eprJ59NuExDG7cEKlZk9skDjw5cECkeHHmvy1Z4r4sgYazTUUd2d/u3SxGXa2aTbE99BBn7xERzitLT2OYyBNfJxqNJ9HKLECJi6PZ7t13beuiojgyN/wrRih/eDhvetWri5Qr55nouBdekP9MZ5ky3X+jun6dx8uQgYrWnvPneeMNCbFVNUmL3LtH/2fz5rZgj8ceYyHkq1dd2+epUxywNGliSx3In1/kjTdEli1zPMrSU4SH2zoOZMigFZrGezijzHSnaT8iNJR9xex7m2XMCHz4IdCnj21ddDSwZg07M/fuzX5df//t/vHLluWjCDtTr1mT8P0cOYC//rL1Plu+3PZe/vzcvlkz4I03gE8/TZsNMJNrKvrWW+zL1qZNwqaijlCkCJuSrlgBXLoEzJwJ1K8PzJoFtGgB5MkDvPQSG7jeuOG1r4b4eOC334AOHXh9APweia8TjcYUHNV63lz0zMxG1670PyUmPNwWSRcaahsNR0WJ5MnDhF93CQ+31R5MamZmcPUqk4ozZrzfnxcbS3MbQP+RDjvnrHnbNpG+fflbAXzs00dk61bXZ9WRkZwhv/66SL588l9e3JNPiowf77kC0bduMXrV8OXlz2+bdRpWAo3GG0CbGQOX0aP5q1y4cP974eE05YWF0eRn8MknVEKeaFPSsSOV5erVKW935QoTgsPCmCtlj8Ui8vnn/B5PPqnDze0xmoq2aXN/U1F3ohfj43l99Otn87MCNAsPHiwycyajVQ3Fk5rPKzycOXbt29v8gHXq0G8bG0vfapYsWpFpvItWZgHMypX8VVasSPp9ozbj6NG2dadPUwG9/777xzeagKbUKNTg8mWRypWp0P7++/73p0zhTOGRR1Lu/pxWSaqpaPPmqTcVTQ2LRWTfPpFhw+ivs680o5RI2bK21yEhnGU3aGBbHn3UNkMH6KvbtCnhMRo2pHLTaLyJM8pM+8z8jIcf5qO938yeRx8F6tYFxo8HLBauK1yYPqzJk4F799w7fu3afAwPT33b3LmBlSuBMmWAZ58FVq1K+H6XLsCSJcDRo0CtWsDeve7JFmzkygW8+SYQEUG/50cf8Rx16EAfZLduwLp1zvselQLKlwf69wc2buSjgQhw+bLttcVyv5/txg3bMUNCgEaNgJo1E25z5Ah/d43GX9DKzM/Ik4eBAskpMwDo1Ys3E/sAjN69gevXgdmz3Tt+vnxAqVKOKTPAptBKlQKeeeb+YIBmzYB//mGgQL16wNq17skXrDz0EDB4MHDiBM/n888Dc+cCjz/OcztwIHDsmGv7fu45BqaEhvJx+PCEr2fN4u9mLLNm2d7PmBFo2DDh/u7dY3BL6dLufGONxsM4OoXz5qLNjAlp2pSmnuQwajM+9ZRtncVCk1/lyu6H6XfqxIACZ/Zz8SLbxGTOLLJ27f3vnzghUr48/UT21fg1yXP7tsj06Qmbitav73hTUXsS+8gc8Zkl9/7u3ZTFvk2QRuMNoH1mgc377zNSMKVmkUZtxkOHbOt+/JHrklImzvD999yPsyWqLlygwsqShdUsEnP1qq0M1Fdf6crxznDqlHtNRT2J4Vf991/fHleT9nBGmWkzox9SuTJzyQ4fTn6b7t2B9OnpOzPo2BHImRMYO9a949epw0dHTY0G+fLRb1akCPDUU8CGDQnfz5WLeWpt2wLvvw/07cvcJU3qFClCn9r+/cCmTcCrrwJ//snzXLQo8MEHwJ49vpHlyBE+ajOjxp/QyswPqVyZjyn5zfLnp1KYMgW4c4frMmcGXnsNWLiQPg1XqVgRyJrVeWVmyLVqFVCoENC8+f37CAujL+jdd4FvvwVefBGIjHRd1rSGUgzGGD8eOH8e+PVXoEYN4JtvGDxUrRowZkzCIA9HiIgAhg7lo/F62DDba3sOH6avNEcOd7+NRuNBHJ3CeXPRZsaEREUx1P7jj1PeLiKC5p7vvrOtO3aM/pVPPnFPhiefZEi9q5w9y3ynrFkTdrK2Z/RoylqnDvPWNK5z6ZLrTUXDw21J0EbovuGjS58+YYNWEXYJqFXLe99FozGANjMGNhkzAuXKpTwzA4DHHgOqVwfGjbOFUpcowajCH38EoqJcl6FOHWD3buDWLdc+X7AgsHo1TY/NmtE0lpi+fYGffwa2buXxjh93Xd60Tp48wNtv81zu3g288w6wZQvLZxUowHJamzYlHeY/Y4YtzUOsofvGdrGxnD2XK0fT8OrVNDNqE6PG39DKzE+pXJk3pZRQimH6+/YlzPHq3Zs3pJ9/dv34derwBrd5s+v7KFSIN788eYCmTYF//71/mzZtWFfy8mXmom3Z4vrxNKRSJeDLL4FTp5i+0bw5zdG1ajH/bOhQ4PRpbnvyJDBvHq+lkJD7Q/czZuSgo1gx+mIbNeJ+t28Hpk1jrUiNxh9QktRQzcdUr15dtui7WAKGD6fD/8YNIHv25LeLimJwQN26LAILcFRdvjyQLZvryujmTQaTDBwIfPaZa/swOH0aaNAAuHaNiqt69fu32b+fwQxXrlAJt2jh3jE1Cbl1C/jlF2D6dOb9KcVixceO8b3vv6dia9iQifMREcw5M14D9M1OmcIZYI4cvDYNH97TT3MWlzEjFZ7xGV/y66/AsmVMNjfj+BrPo5TaKiJJ3DGSwFF7pDcX7TO7n6VL6bNYty71bT/+mD6P48dt68aO5ec3bnRdhocfFmnWzPXP23PiBHue5cjB4rpJce4c8+tCQ5lLpfEOR4+KfPaZra1QWBiLQq9cmXqftEWLbNfV1q2swZm4ZJYZxYfXr7cdPyTk/q7omsAE2mcW+KRW1sqeHj04Qv7+e9u6zp0ZkehOmH6dOhyhG/4UdyhWjCbH7NmBJk1opkpMgQKsENKkCfD668CAAWmzjYy3KVmS1WIiIxnS//LLnNU3bgwULw588glw8GDSnzXC8suUAapWZaufjRtpRTCIimK7Gl/y8ce25xYL8MILNMGfPetbOTQm4qjW8+aiZ2b3Y7EwErBmTcdGuS+8IJIrV8ICtb17Mxrt/HnXZJg2jSPdt9/23Ej72DGRokUp644dSW8TE8NWOIBIly58rXGOlCp4GLN2+yawRlPRp55Kuanom2/yt0vqeJky2aIgK1dmQrcvulH/9JP81xopJIQFB557jhGdGTPy+nWnI4HGPODEzEz7zPyUiAhb8nKmTKzXl5IfYO1a+jcmT2ZCLcDRdblywOefcwTtLL/8wkg2gMEBjzxCP5y7REYCO3cyYfqRR4AHHrh/GxH6cE6epO+uQgUgXTr3j50WuHUL2LGD5zB9ejYIbdqUvtV16xjt+swzwIIFDPJIzPnzrPE5bRqDkDJkYCHpzp2Zw3b7dtLRqYafzWLhNWc08HTk+nWV1av53Z54gtf4+vU2P9/x46x3OW0az0OPHmx0mz+/5+XQeAdnfGZamfkpw4bZTCchIfxT2ptyEiNCxRAaCmzbRrMjwLD4PXtYwDZ9eudkGDqUJieDEiVYbcITREbabriVKyet0ADeWA8d4vuVKjHAQJMyp06lnOYQFsaiz8WLs+OCsRQpwkdjwCLC32j6dBYfvnyZ12KZMlR2jz5qu84S0707MHEin4eGAl98kfL16wqHDjFCM39+Jucnl8R99Cj/PzNmUDG/+SbQrx/TRjT+jQ4ACQIc7fpsz4QJ9weNLF7Mda4U9zW6W4eGOi6DMxw+LFKwILsu79mT/HbLlrHeY7Fi7NOlSRnD5GeY3MaP55IjB89js2Zs2ml0p068ZM3KGptNm4q8+iqDRb7/non4RoCF0VR05MikTXiuXL/OcOWKSOnSvHYcbUp7+DADXUJCWBD7gw+YbK7xX6DNjMFB1aoMf541yzETzd27HFk3bcrcIYCmvLJlmcS8bp3zMiQVou1JDh9m2H58PE1GFSokvd3WrQz/jokBFi1iWLkmeex/typV+LhnD8Pyq1WzbRcTA5w7x/Jnp0/z0ViM1xcu3B+IkzEjZ2VRUXwsWZLHePpptqwpUoRJ/VeuMM2ke3fPfbfoaF7jmzYxv9IwxzvKoUOcKc6eTRNor15MCM+d23MyajyDnpkFCUbnX2d47z06vs+csa37+muOkLdv96BwHuTAAZH8+TlTSGnmdewYq8ZnzHh/iSVN0sTHi7Rty1nSwoWu7SMmRuTkSZERI3gd9e4t0rcvg44qV+ZMLqkZnrF4cmZmsYh07sz9zp7t3r727xfp0IHn5oEHRD76KAjKqiVuRRHgrSmgW8AEB64os6NH+ef89FPbumvXaFZ59VVPSudZ9u+nMsuXj8+T48oVkbp1+R1HjfKdfIGKYRocOdL9fY0ezX1dvnz/e9HRjIZs0YKDDXtlFhLCqEZPMGQI9zlokGf2JyKyd6/ISy/xmsqalefMPoIzYBgwQKRPH5sCs1j4esAA82RyE63MggRXlJmIyDPPiOTNm7DAbPfu9H/588hz3z7KXaCAyMGDyW937x5nBQBnCKkl+qZFwsNF2rThOerWzTMD9J49RbJnT31ft29zMGX41gAWrXalqag9P//MfXXs6J0Jx549nMUCItmy0Vd4/brnj+MVDMUF2BRa4tcBiFZmQYKryuzPP/nLzphhW7drF9eNGOEp6bzD3r106hcsmLDxaGLi4mz/1TZtRCIjfSai3xMezvxCY1aUVKNUV2jalIEjzsjRr59Ijx4Jm4q2b88ctLg4x/e1cSM/W7eu93/rXbtsg6Xs2UUGDnRPCfsMewVmLAGsyES0MgsaXFVm8fG8eTz2WML1DRsyItCZm4gZ7N4tkju3SKFCjEBLiVGjeBXXqxegpiEPs2uXSIUK4hUTX8mS7G7tChaLyKZNIm+9JZIzJ2UrUIBd1XfvTvmzJ07Q/FyypG+jD3fsEGnVirLmyCHyxRciN2/67vguYbEkVGYBrMhEtDILGlxVZiK2Kg+bN9vWzZ/Pda4GAviSXbtEHnxQpHBhkSNHUt523jyRDBlEypVLWJ8yLWFvIsucmekUSnku+CI6morxf/9zf19RUSK//mqr0gGwD9s339yvrG7eZI3Q7NnNS8vYto2yAqx+MmSIyK1b5siSInpmppWZv+KOMrt5kxFanTrZ1sXGihQpwuaKgcCOHVRoRYowsCUl1q7l6Dl//uQLGQcj+/bZghceeMAWvJBSOStXmDePdwtPKDN7jKai1apx/+nSUXHMny/y999sFBoaKrJihWeP6wpbttAfDfC6HDaMMvqiZFeqaJ+ZVmb+jDvKTESkVy/OWC5etK0bOpS/+t697krnG7Zv52i4aNHUZ11793K7LFnokwlm9u+n70kpfl9vhpWHh9tmUOnTe+/GvXs3E5kLFEg4ufDmMV1h82ZGbdrLGBrKlIFvvhGZOZN+661bRU6dSlgv1avoaEatzPwVd5XZgQP8hQcPtq27dImh02++6a50vmPrVvpZihWj/yQlzp4VqVKFN5fJk30ink85eFDk5ZdtVSw+/DDpUHlPMnSorZoH4DkfXHLExdlyyQxF4e1jukKPHgkVmv05SrxkycIWSNWrs5hzp04s9Dx0KKM8Fy5kG5sDBzizdjlCNw3nmenSrUHMQw+xUsL337MWXfr07Prcrh3r7Q0blnLjT3+halW2FGnShFUm1q5NvkZkwYKsctGmDfDaa6xi8dlnydcQDBSOHGHVipkzWX3j3XfZviVvXu8fu2FD1nOMiuKt2dvnMjQUeOMNVrGJiWE9xYYNvXtMV3jlFRYxjo7mb7JiBQt7X7nCOpaJH43nly6xO/zly8C9e0nvOzQUePBB/l9z53bsMWNG3P/jBPqF7wS6nJUfU7kyy1nNm+d6KanFi4HnnmP35rZtuW7rVnZ7Hj0a6NvXU9J6ny1bqNAefJClmooUSX7b2FiWUJo6lV0EfvjB+ULL/sCxY1RiM2ZQ/rfeMqdIbkQEu4TPmMEu5Hv28Abq7WN6s5SaJ3BXxnv3qOCSU3yJH69eTb7HX9asjim+jRvZ9f3pp/33vBroqvlBwIYNrGwOcFS8apVrF158PKucFynCGY1BnTr8gxw8yErogcK//1Kh5cnDm0jhwslvKwIMHMh2JM2bs6VNctX5/Y0TJ1jpfepUtr55803/aF+yezdrOz73HM9nGhr4+wXx8WysmtLML/G6qKik9+XN1jyeQtdmDAIaN/acz+Crr7ifnTtt62bP5rply9yX1dds3MiyQ6VLJ6xBmRwTJ/IcVq3qeqNSX3HihMjrr9saS/buTT+gPzFsmHikNqLGN9y5w+vq33/Z7NYbOYjeAjoAJLBZvpzOZKNzrru5Qlevch/dutnWRUczjL12bT8JLXaS8HAqtDJlRBYtSv07LF3KgInixVOu/WgWJ0+KvPEGI/cyZGDpKEcUtRnExorUqsWgHH9TtJqUCQ+31c4MDfX//70zykybGf2MkycZ8FCoELv6btzoGZ9B9+4MHjhzBsiVi+s6deI6pehw7tsXePhhIHNm25Ily/2vM2VKukOxrwkPp8kxMpLfISwsZbPJli30E8TGAr//bjPjmsmZM2yCOmkSX3frxiaWKfkD/YFDh9ha5okngCVLtLkxkIiIYPBQeDiwfTub+vor2mcWoERH8wZ76BBvvGXKeG7fu3czoGTkSPZuAuiH+eEH1/aXMWPSii4lJejMtpkyOebLa9sWmD+fzx3pyH3sGPDUUxw0zJoFvPCCa9/fXc6dYzTpjz8CFgsjLz/+2HOdvH3Bt98CffpQEb/2mtnSaJzh+nXeXx55hIE9/joY0cosQDGUy8KFQKtWnt9/w4a8iR85wplVRATQqJEt/PmXX9gc8+5dRlndu5fweWqvU3ovNtZ5ecPCUld8d+5wlgXwD9m7N79DSgo0Kgro0oXNHUeP5g3ZV5w/z2aVEybQmd+lC/DJJ0Dx4r6TwVNYLEDjxhx47d4dmN8hLTNuHP8vixYxoMcf0cosAJk+HejcmWHXI0Z45xjz53MmY3/x+ir8OTbWM0oxqW1v3+ajq4SFMToyOaXpziwzUyaaihcv5qxw0SKei86dqcRKlvTcOTaDEydomq5RgyP8QIqMTevExtJaEx/PVIsMGcyW6H60Mgswdu4EatXismIFQ7G9QVwcUKIEEztXrPDOMcwiPh549ll+r3nz+CdNTUnevg0sXUq/QfHi9FVGRyevQO/e5WzEHVq0oHmuVCmPfG2/YNIk4PXX+b169zZbGo0zLFtGP7K/5pxqZRZA3LjBBOZ793hT9XYy7NChnBHs2weUL+/dY/mamzeBmjXpD9iyxTH/kwjw9dd0iNevD/z2my1AJqltjRmmozPJP/8EVq/m55UChgxJ2acXiIjwhrhmDbBjB1C2rNkSaRxFhDmYmzfT/fDgg2ZLlBCdZxYgxMfb2mCsX++bY166ZAv9Dkb272eX4KpVRe7edfxzc+bwvJQvn3r9R2cID2daRGio59qx+CNnzzJUv1Yt/++Xp0nI7t1MAerd22xJ7gdOhOZrC7eJjBzJ4IWvvgLq1vXNMY3ajNOmAbdu+eaYvqRcOUYpbt9O05ejhod27TiLOneO5t7t2z0jT+3aTBf44gv/r7bgDgULMqBg40Zez5rAoVIl/le++w44cMBsadzAUa3nzSUtzsxWruRo6KWXfF/Y+t9/mTT57be+Pa4vGTyY33HkSOc+t2cP+6c98ADbeGgcx2IReeEFznB37TJbGo0zXLxIi8bTT5stSUKgk6b9m7NngUcfZdHPzZvNqRdYuzaLje7fH5wRaCLAiy8CCxbQyd2smeOfPXuWgRr79jG4oXNn78kZbFy+DFSsyJqZGzf6Z4ScJmm+/JL1P//6C3jySbOlIc74zILwNubfxMQwPP7ePeDXX80rfNu7N5Ozgy2q0UApYMoU3ljbtaNz21EKFQLWrWO6QpcuTMT2gzFfQJAnDxPBt28HevRgYnhEhG+OHRHh2+MFG336MNr53XcZ+RxwODqF8+aSlsyMb79N89e8eebKER0tki+f/5kVPM2xY+xUXaGCyK1bzn02OppNFAEW/42N9Y6MwYhRKFsp7wW+3LtHc+b8+WyUaTTHDOZAG2/zyy88hz/8YLYkBDoAxD+ZO5e5OH370gRmJhkysAHismXA0aPmyuJNSpRgZZODB9lM0Zk8sQwZGCjzySfAxIlAy5asOKJJnYoV+SjC3L01a1zbT2wscPgw8wFHj2Y/tyZNmHaROTPzCdu0YeUcY/YcFeX68dI6L7zAFJVPP2WqSyChfWY+Yt8+5kBVqcK8I39oFHnuHFCsGPD228y1CmbGjOEgYsAA9jhzlgkTeCOtWpWFdX3dHDPQiIhgEeLoaL5++23+BklhsbDg8uHDNH0fOmR7fuwYE+INcuRgB/UyZZjPZjwazSaN0mxr1gRv5Ki32bKFFV28WY3IUXTStJ9x+zYvjuvX6UsoWNBsiWwYIelnzrAUU7AiAnTtypnWggXA8887v48lS4CXXqIiW77cv5ODJ0wAjh/nbNKsm3pEBH2yq1dTubRsSZ9a3rxUYIbiOnIkYQPJzJnvV1bG8wcfTL4o7po1nLW1a8duEBrX6dyZlqT9+80tuaaVmR9hH1W3ciWDCvwJo6P1Dz/Q7BjMREUBDRpwlrxxo80U5gybNwPPPMOb8e+/s2O3v2HfpTxjRioTM2cp8fG2/4BBunQs6WWvqIznBQu6XsW9Th1G565f7xnZ0ypnz/K3aNGCZnqz0NGMfsQ337DA77Bh/qfIAP75H32UCa9+MK7xKmFhvKE+8ABnCdeuOb+PmjU548iZkxXjFy70vJzuMmOG7Xl0NJPIzSQ0lCXbDEJCaO49cMBWNOCNN2iWLFTIvXYkderQTGaYNzWuUagQzYzz5zOyNxDQysyLrFvHmn+tWvHRHzHapuzZA6xda7Y03qdQIaZEnDoFtG/vWghyqVJsbFilCh3m48Z5XEy3OHiQj0px+eknBk+YGbbesKGtqWvGjBwIeIM6dajIPFXBJS3z/vv8v7zzjvsFtn2Co2GP3lyCMTT//HmRAgVEypQRuXHDbGlS5t49kQcfFGnd2mxJfMfEiQxBfv991/dx965Iy5bczwcfsNam2Rw+zBD1V14RGTpUZNEiXoOcd7M6x5Il5sgWHk6ZvBk2f+4cv+fXX3vvGGmJ6dN5PqdNM+f4cCI033RFJkGozGJjRR5/nPkugVLW58MPWV7r5EmzJfEdb73Ff8DMma7vIy7Otp927USiojwnnyu89RYV1rlztnUDBtiUmbHkySPSqJFInz4ikyaJbNokcueOWVJ7lhIlWFZL4z7x8SLVq4sUKmTO9eETZQagCIDVAPYD2Augj3V9LgArABy2PuZMbV/Bpsw++IBndsYMsyVxnBMnqMw++shsSXxHTAwHHWFhIlu2uL4fi0VkxAj+5g0aiFy/7ikJnePyZQ6gXn014frwcH5Hpajo+vThNjVqcHtDwSklUrq0yPPPi3z2GZORDx4MvCr4HTuK5M/v+5qnwcq6dbw+Bg70/bF9pcwKAKhqfZ4VwCEAFQB8CaC/dX1/ACNS21cwKbNff+VZffNNsyVxnuefF8meXWTQoLRTQeHSJZGiRUUKFxa5cMG9fc2aJZI+vUjFiubMcD//nNfenj33v5eciS8uTuTQIV63AwdyRlOmjK2ahlFRo1o1kS5dREaNElmxwv1z5U3Gj6fcx4+bLUnw0LatSObMImfO+Pa4ppgZASwC8CSAgwAKiE3hHUzts8GizA4eFMmaVaRmTfPNTa4wbFjCG1haUWjbtvH71qvHElbusGoVq48XLCiyY4dn5HOEyEiaDlu08Mz+7t5ld4WffhJ55x2Wp8qbN2lTZd++IpMni2ze7FwPOW+xfTvlmzXLbEmCh2PHOKt/5RXfHtfnygxAcQCnAGQDcCPRe9eT+Ux3AFsAbClatKh3z4gPuHNHpFIlBlIEqt/pkUdsN6rQUI7k0wqzZ/N79+jh/r527eJML2tWzmJ8wY8/Uv5Vq7x7nIsXRf7+W2T0aJGuXelPSWyqLFOGwUQDBtBUeeiQb02VsbFs4ROsDWjN4sMP+Rv/+6/vjumMMnM7aVop9QCAtQCGiMgCpdQNEclh9/51EcmZ0j4CPWlaBHj5ZWDOHFbT8Jf2Cc7w66+scZcuHb9PhgzB3UwyKT78kG0wJkwAund3b19nzjDhdP9+hsZ36uQZGZPCYgEqVGAFly1b3MvTcoX4eJad2r0b2LWLj7t3s7KHcXvJlIlJ6g8/zKVyZT7mzesdmZo0YR7htm3e2X9a5NYtW3L7P//45jpzJmna3RlZegB/AnjXbl2aMzMaNvovvjBbEte4eFEkd26Osv/5x/vh0/5KXJxI8+b0e61f7/7+btygGQ4QGTLEewEJv//OY8ye7Z39u4phqpw8mabIpEyVefNyfd++NGn++69nTJWffsqAptu33d+XxsaECfzdfvnFN8eDL2ZmSikFYBqAayLS1279SABXRWS4Uqo/gFwi0i+lfQXyzGzjRuDxx4GmTVnNINAaXYoArVsDf/zBUWyFCmZLZC7Xr7PKx+3bnOUULuze/mJigFdfZRWON95ggnW6dJ6R1aBBA+DECc6E/KGAdWpcvGibvRnL3r1AZCTfVwooXTrhDO7hh1kjMDTUsWMsXw489RStC40aee+7pDXi41kx6M4dloULC/Pu8XwyMwNQD4AA2AVgh3VpAeBBACvB0PyVoDILypnZpUv0jRQvLnLtmtnSuMaMGRxpjRxptiT+w9699LlUr86EcnexWJjyAIg884xn83U2beJ+R43y3D7NIC6OAVTz59PX1rp10lGVNWowrWD0aPruLl5Men/Xrwe2tcSfWbGC53bECO8fC770mXmCQJyZxccDzZuzZFV4OFuDBBpnzgCVKnFZu9bxUW9a4PffWb+xUydW2veEf+D774FevYBq1ViB3xP+opdeop/29Gkga1b39+dv3L3LGYD9LG7XLuDyZds2+fLZZm/GbK5CBfro0qcHpk5NW75fX/Dss7xnHDniPb8n4EOfmaeWQJyZffIJRyeTJ5stiWtYLCLNmjF35PBhs6XxT4y8LU/OehYt4gyjZElG+bnDsWP0C/Xr5xnZAokLFzhDGDWKUZXVqjEx3JjFhYTYnmfMmDZ9wN7kwAGRdOlE3njDu8eBLmflXRYv5pl77TWzJXEdw5E7frzZkvgv8fE0d4WEiPz1l+f2GxHBgJsHH+RzV3n7bd5QfJ3I6q/YmyqNwBsjXSAtpZn4irff5n/DmyX7nFFm2szoJMeO0UxUogT7RmXKZLZEznPsGE0xtWvTRBVoQSu+5M4dnqezZ4F//2XFfE9w+DADFM6eZRPEli2d+/y1a0DRoqzaP22aZ2QKJiIiWJnf6Dyd1tJMfMG1awzUqV6d9xFvhOrrfmZeIjKSNw+AeVmBqMgsFnZcDg0FJk/Wiiw1HngA+O03Pm/VisrNE5QpQ19r5cqMJh0/3rnPT5hAf9J773lGnmCjdm0qsC++0IosMfPmAUOHut8OKFcu9qVbsQLo0sW89kIGHg4SDl5EgJ49gR076LwvUcJsiVzj22+Z8DhlCkf2mtQpVQr4+WegWTO2k//lF88MAvLmBVatYl+1Xr0YxDF0aOr7jo7m79i0KZWhJmlq19ZKLDGLFwPt2vF5SAjwyCNAtmyu7+/mTT5On87/yKpV5p1zPS53kMmTqQA+/RR4+mmzpXGNAweAjz5iJFLnzmZLE1g0aQKMHMlO1UOGeG6/WbJwnz16ACNGMHoytS7Js2cDFy6weaJG4wyjR9ueWyzAjRvu7c9QZgAQGwusWePe/tzCUeeaNxd/DwDZsoURUU8+GXjtMAxiY1kAOVcuNg7VOI/FIvLyywwqWLTI8/s2Cj0/8UTybWQsFpEKFUQqV9YtTjTOERkpkiMHgzZCQz1TTDw8nPvx1P4SAycCQLSZMRWuXWPNwrx5OSIO1FysL78ENm+mvTx/frOlCUyUAn78kTPcl18GNm0Cypf33L7792fFkVdfBerXB5YtA4oUSbjd8uXMu5o+3fc1GDWBzZw5nIl9+y19vw0bum8SNHyTa9Z4Zn/uoKMZU8BiAZ55Bvj7b2D9epY5CkR27gRq1GCgwdy5ZksT+Jw5wwiubNmo0HKmWEbbeVau5G+VNSvLjD38sO29xo2BgwcZkZohg2ePqwleRFiGKj6eSeeBMhDS0YweYvBg3kzGjAlcRRYTA7zyCiOPnI2Y0yRN4cKMZj1xAujQgTcIT9K4MSvLAEC9enSqA6yduWoV0KePVmQa51i7loPavn0DR5E5i1ZmyfDnn8DAgXTI9+hhtjSuERHBdjS7dgETJwIPPmi2RMFD3bosGrx8OfDxx57ff+XK/P2KFGHZtE8+ofkxc2b329MECv/8AwwbZn7IdzAwZgz//x06mC2J99A+syQ4eZI/eqVKwA8/BN5IRoTK6803aSoNCQFy5zZbquCje3dg+3b6I6tUYYi9JylShObtBg0Ysg/QZ7tvX/CEnIsAV69ylnviBHD8OB+3b7cpsQwZgNWrgTp1TBQ0gDl2DFi0iIOuQMyNdRStzBIRHQ20bQvExdGUlDmz2RI5zrFjwMyZwIwZLABqIEIHbbDcAP2JMWOAPXuA114DypWjX8KT5MjBZO1du/g6Pt7cXB5nEWHQgaGk7BWWsSRORM+ZM2FrkZgYnoPBg2kpCeYbsjcYN46DoLfeMlsS76KVWSL69mXZooULWaXB37l+ncmKM2awvJZSjCp68UVg1CjmfmTIwHUaz5MhAzB/PgNCWrXitePpKuJ16yZ8vXw58M47vh1oRUQkH7F282bSisp4futWwu2zZmXRgVKl6B8sXpyvixcHihWjAjfKUUVH80acMyf7wX38Mc3+PXty3/4QRefP3L7NHNkXXwQKFjRbGu+ilZkd06fTrNivH29M/kpMDMO2Z8xgNZKYGLa8GDYM6NjRFs79zDP6z+4L8uVjyat69Tir//tvzzbJ3LmTjz17csYyahQbTi5eDOTJ47njJEdEBPDEE7zOQkNZ0i0mxqa0rl9PuH2WLDbl1KABH+0VVo4cqZvuE4d816pFH9ro0TS5jhhBE7oIz4kuWZU0U6dyMNGnj9mS+ABHE9K8ufhD0vSOHWwh0bAhE4z9DYuFCYlvvsnEZ6PlfN++Ilu36gRaf2DmTP4uPXt6bp/R0SKFCrEKvMHChbxWS5f2bvue6Ggmhz/8sK0CPSCSPj0Tt1u0EHnrLZEvvxT55ReRf/8VuXzZ+9fi4cMitWvrqvipER/Pa6R2bbMlcR3oFjDOcf26SKlSIgUKsE+SP3HkiMjAgbwoAd7E2rUTWbbMP5VuWuf99/k7TZrkmf1Nn879LVuWcH14OFvI5M4tsnGjZ44lwhvgmjUir78ukjMnj21UjVCKlXA2bPDc8VwlPFwkQwb5r3eZP8jkbxitqubONVsS19HKzAni40VatmRfqPXrTRMjAdeuifzwg0idOraR5xNPiPz0k8jNm2ZLp0mJuDiRpk05e3H3BmuxsGxVhQpJz3YOHmSTz0yZ3CuvZbGIbN8u8sEHIoUL85rLkkWkY0cq0ZgYKo+hQ/2ryWV4uMhTTwX+DdtbNG7M3zMmxmxJXEcrMycYPpxn4ZtvTBNBRESiokQWLGAzSGPEWaEC6/WdOmWubBrnuHaNM/38+d1rnPnXX7wOfvop+W0uXhSpUYOzk+++c27/R4+KDB4sUr48j5Muncgzz4jMmSNy547rcvuS2Fh+/zx5RK5cMVsa/2HXLv6mw4aZLYl7aGXmICtX8ibw4ovm+Jy0Hyx42bNH5IEHeKONjHRtH02bUiFGRaW83Z07VEKASP/+tDYkx8WLImPHJvQ51a8v8v33gasMdu6kIu7UyWxJ/Idu3Thjd/o3TXzTMfkmpJWZA5w5w9FcuXIit2759tiJ/WCZMom0b6/9YMHGwoX8fTt3dv6esHMnP+toYENsrEj37vxMx44M3jC4dYu+t+bNWd0coPly+HCREyeck8tf+fRTSdK3mBa5fJm+9e7dnfzggAEiffrYLlaLha8HDPCofM6glVkqREdzZJoli8i+fb455tWrHP3a+8EaNRKZMkX7wYKZAQP4ez/9tHP+pqeeorl5+XLHP2OxiAwZwuM9+qjISy/Rb5IpE9cVKyby0Uciu3c7+y38n6gomkuLFPH94NTfMK6BvXud+JChuACbQkv82gScUWZpsmp+nz5sgzBvHpMJvUV0tC0fbOlSWz5Yp04J88E0wcuGDcw/A5hbVaVK6p19r1wB9u7l80yZnM+h+uwz4IsvbK9btwbefZf78ESHbH8lIoIJ5j17AmPHmi2NOcTGMpevYkXgr7+c/LAIs/HHjLGt69OHyX0m1fTTVfNTYO5cKrK+fb2jyESA8HDWRSxQgDeSDRtYSmbrVpY+6t9fK7K0wj//2J6LONbZ1768U0yM8917M2Wy3XtCQlidpG7d4FZkAJV1797sDrF+vdnSmMP8+cC5c7y/OY1SCVtRA6YqMqdxdArnzcVXZsa9e2larFvX8+Gqhh+sVCntB9PYcKUTr7vde73d/defuX2b5tSHHnI98CaQqVlTpGzZlIOAksXetGgsJpoYRbTPLElu3eIFnjeve+HS9mg/mMYRXMnRcjevyx/zwnzFn3/y//jJJ2ZL4lsiIvi9x41z4cPaZ+YZvO0zEwFeeolV8FeudK/orr0fbMkS2qgrVqQfrEMHbT7UaPyBLl2AWbNY+LlKFbOl8Q3t2rEI9ZkzwAMPuLCDgQNpBzdMi4YPLUcOvmcCzvjM0oQyGz2aDvARI1hE2FlE6FyeMYNBI9evs7hs+/bs4lylSuCYlTWatMC1a0D58uwKvmkTkC7IS6qfOcPAj759ga++cmNHIglvZolf+xhnlFmQ/8RsP//BB6yC/8EHzn326FEqsJkz+TxTJu6nUyd2cA72P4hGE6jkysVAkLZt2WXAlUFsIDF+PPVOr15u7iix4gqgUXpQz8wuXACqVmVLii1bgOzZU//MtWucfc2YwdmYUmx/0akTW19kzepxMTUajRcQ4X/2jz/Y3DQQ+hO6wr17dG80bEhXSjChZ2Zgp+iXXqIJePnylBVZdDTzwIx8MMMPNnw488EKF/aZ2BqNxkMoxS7LFSoAr7/ODt3BmJ4wcyYH4S6F4wcRQavMPv6YOT4zZgCVK9//fnJ+sF69OAvTfjCNJvApWBD4+mugWzdg4kR2qw4mRJjj/OijtuT8tEpQKrMFC4CRI5m4/PLLCd87coQjGXs/2PPPU4E1aaL9YBpNsPHqq8Ds2fSbPf10cFla/v4b2LcPmDZND76Dzmd26BArHpQvz5lZxozA1avAzz8n9IM1akQF1rq19oNpNMHOsWNApUpA48bA778Hz43/6adZWejkSd7rgo00W87q7l06fDNk4Mxr6VLOugoUYDmpW7foBzt1iiOazp21ItNo0gIlSwKDBzM3dN48s6XxDIcOMef1zTeDU5E5S9AY1cLDqbD27gWeegp47DHtB9NoNDb69KEie/ttuhRy5zZbIvf49lsO3Hv0MFsS/yAolFlEBFC/PmCx8PXKlZyhaT+YRqMxCA0FJk9mus4779DtEKjcuAFMncrCDfnymS2NfxAUZsY1a2yKDGBV+lmzgObNtSLTaDQ2KlUCPvqIbohly8yWxnUmT6ZbpU8fsyXxH4JCmTVsCISFMYckUyagWTOzJdJoNP7Kxx8z96xHD+D2bbOlcZ64OPZre/xxhuRrSFAos9q1mRA5eLDzjQw1Gk3aImNGzmxOn2Z1n4gIsyVyjt9/Z/SinpUlJGiMcLVrayWm0WgcQ4SWnK1b2bjUKBperx7L3/kzY8awqHDLlmZL4l8ExcxMo9FonGHNGio0gI/z5tHHnisX3RaDBwMbN9Kk509s38782V69GNCisaGVmUajSXMYfvbQUPrZV6wA/vyTprtbt4BPP6Wl58EH2Slj7Fhg/36bAjSLMWM4c3ztNXPl8EeCxsyo0Wg0jlK7Nv3ra9ZQsRkuiqZN+XjlCrB6NYsr/P03sGgR1xcsyHSfxo25FCrkO5kvXgTmzGHR5Bw5fHfcQCHoyllpNBqNpzl2jMrPWK5c4fry5ancmjQBGjRwrM2UqwwaxIbPBw8CZct67zj+hO40rdFoNF7CYmF/NGPW9s8/QGQkTZY1atiUW61aniszFR0NFC3KurNLl3pmn4GAVmYajUbjI6KjGSxiKLfNm6nwMmViLpih3CpXdr2f2vTprCX711/scp9W0MpMo9FoTOLmTfriVq6kctu/n+tz57b52po0AUqUcGx/IkC1alSae/akrfqyutO0RqPRmET27MwBM/LAzp61KbaVK21V+0uWtAWTNGqUfOHjdesYkj9hQtpSZM6iZ2YajUbjI0SAAwdsJsk1a5gKALA0laHc6tcHMmfm+hde4HanT9vWpRW0mVGj0WgCgLg4YMsWm3ILDwdiY9napU4d9mKcOxfo0IHFkdMaWplpNBpNAHL3Ls2KK1cyt+3wYa7PmJF5b2mtZF+a7TSt0Wg0gUyWLCyrNXIk0LWrzUcWF0dToyZ5tDLTaDQaP8S+5FaGDHytSR4dzajRaDR+SHIltzRJo5WZRqPR+Cm6tZXjaDOjRqPRaAIercw0Go1GE/BoZabRaDSagEcrM41Go9EEPFqZaTQajSbg0cpMo9FoNAGPVmYajUajCXi8psyUUs2VUgeVUkeUUv29dRyNRqPRaLyizJRSoQDGA3gKQAUA7ZVSFbxxLI1Go9FovDUzqwngiIgcE5EYAHMBtPTSsTQajUaTxvGWMisE4LTd6zPWdf+hlOqulNqilNpy+fJlL4mh0Wg0mrSAt5RZUs29EzROE5EfRaS6iFTPkyePl8TQaDQaTVrAW8rsDIAidq8LAzjnpWNpNBqNJo3jlU7TSql0AA4BaAzgLIB/AXQQkb3JbH8ZwEmPC+Kf5AZwxWwhggB9Ht1Hn0PPoM+j+yR3DouJiEOmO6+0gBGROKVULwB/AggF8FNyisy6fZqxMyqltjjaBlyTPPo8uo8+h55Bn0f38cQ59Fo/MxFZBmCZt/av0Wg0Go2BrgCi0Wg0moBHKzPf86PZAgQJ+jy6jz6HnkGfR/dx+xx6JQBEo9FoNBpfomdmGo1Gowl4tDLTaDQaTcCjlZmXUEoVUUqtVkrtV0rtVUr1sa7PpZRaoZQ6bH3MabasgYBSKlQptV0ptcT6Wp9HJ1FK5VBKzVdKHbBel7X1eXQOpdQ71v/zHqXUHKVUmD6HqaOU+kkpdUkptcduXbLnTSn1kbXjykGlVDNHjqGVmfeIA/CeiJQHUAtAT2vngP4AVopIGQArra81qdMHwH671/o8Os8YAMtFpByAR8Dzqc+jgyilCgF4G0B1EakE5tC2gz6HjjAVQPNE65I8b9b7ZDsAFa2f+c7aiSVFtDLzEiJyXkS2WZ/fBm8chcDuAdOsm00D0MoUAQMIpVRhAE8DmGS3Wp9HJ1BKZQPwOIDJACAiMSJyA/o8Oks6AJmsVY4yg2X69DlMBRH5B8C1RKuTO28tAcwVkWgROQ7gCNiJJUW0MvMBSqniAB4FsAlAPhE5D1DhAchromiBwjcA+gGw2K3T59E5SgK4DGCK1Vw7SSmVBfo8OoyInAXwFYBTAM4DuCkif0GfQ1dJ7ryl2nUlKbQy8zJKqQcA/Aqgr4jcMlueQEMp9QyASyKy1WxZApx0AKoC+F5EHgVwF9oc5hRWn05LACUAFASQRSn1srlSBSWpdl1JCq3MvIhSKj2oyGaJyALr6otKqQLW9wsAuGSWfAFCXQDPKaVOgE1eGymlZkKfR2c5A+CMiGyyvp4PKjd9Hh2nCYDjInJZRGIBLABQB/ocukpy582lritamXkJpZQC/RP7RWSU3Vu/A+hsfd4ZwCJfyxZIiMhHIlJYRIqDTuFVIvIy9Hl0ChG5AOC0Uuoh66rGAPZBn0dnOAWgllIqs/X/3Rj0hetz6BrJnbffAbRTSmVUSpUAUAbA5tR2piuAeAmlVD0A6wDshs3X8zHoN/sZQFHwz9FWRBI7RjVJoJRqCOB9EXlGKfUg9Hl0CqVUFTCIJgOAYwC6ggNafR4dRCk1CMBLYLTydgDdADwAfQ5TRCk1B0BDsNXLRQADAPyGZM6bUuoTAK+C57mviPyR6jG0MtNoNBpNoKPNjBqNRqMJeLQy02g0Gk3Ao5WZRqPRaAIercw0Go1GE/BoZabRaDSagEcrM41Go9EEPFqZaTQajSbg+T/m3hR+z2zewAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('-----\\nOverall objective funtion value -', vrp_obj_val)\n",
    "for r in range(len(vrp_routes)):\n",
    "    for n in range(len(vrp_routes[r])-1):\n",
    "        i = vrp_routes[r][n]\n",
    "        j = vrp_routes[r][n+1]\n",
    "        plt.plot([vrp_X[i], vrp_X[j]], [vrp_Y[i], vrp_Y[j]], color='blue')\n",
    "plt.scatter(vrp_X[:num_cust], vrp_Y[:num_cust], color='blue', marker='.')\n",
    "plt.scatter(vrp_X[num_cust:], vrp_Y[num_cust:], color='red', marker='x', label='Depot')\n",
    "plt.legend()\n",
    "plt.title('Network Graph of Single-Depot Vehicle Routing')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31f1bc8",
   "metadata": {},
   "source": [
    "___\n",
    "### Constrained Vehicle Routing Problem with Bin Packing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0deef121",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra vehicles required \n",
    "no_extra = 2\n",
    "\n",
    "nos = []\n",
    "for i in range(len(x)):\n",
    "    nos.append(apx2[i])\n",
    "veh_capacities = []\n",
    "for i, n in enumerate(nos):\n",
    "    if n>0:\n",
    "        for t in range(int(n+no_extra)):\n",
    "            veh_capacities.append(A[i])\n",
    "#veh_capacities\n",
    "vehicles = [i+1 for i in range(len(veh_capacities))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c30c1b04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changed value of parameter timelimit to 7200.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 180519 rows, 182204 columns and 1418208 nonzeros\n",
      "Model fingerprint: 0x7f716e62\n",
      "Variable types: 100 continuous, 182104 integer (182104 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+02]\n",
      "  Objective range  [1e+00, 1e+02]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 10200 rows and 10200 columns\n",
      "Presolve time: 1.28s\n",
      "Presolved: 170319 rows, 172004 columns, 1362108 nonzeros\n",
      "Variable types: 100 continuous, 171904 integer (171904 binary)\n",
      "\n",
      "Deterministic concurrent LP optimizer: primal and dual simplex\n",
      "Showing first log only...\n",
      "\n",
      "Concurrent spin time: 0.00s\n",
      "\n",
      "Solved with dual simplex\n",
      "\n",
      "Root relaxation: objective 4.960003e+02, 331 iterations, 0.63 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  496.00032    0  200          -  496.00032      -     -    4s\n",
      "     0     0  496.00032    0  206          -  496.00032      -     -   25s\n",
      "     0     0  496.00032    0  206          -  496.00032      -     -   69s\n",
      "     0     0  496.00032    0  202          -  496.00032      -     -   71s\n",
      "     0     0  496.00032    0  202          -  496.00032      -     -  137s\n",
      "     0     0  496.00032    0  211          -  496.00032      -     -  139s\n",
      "     0     0  496.00032    0  211          -  496.00032      -     -  155s\n",
      "     0     0  496.00032    0  204          -  496.00032      -     -  157s\n",
      "     0     0  496.00032    0  204          -  496.00032      -     -  170s\n",
      "     0     0  496.00032    0  211          -  496.00032      -     -  173s\n",
      "     0     0  496.00032    0  211          -  496.00032      -     -  178s\n",
      "     0     2  505.12761    0  211          -  505.12761      -     -  250s\n",
      "    15    20  505.12761    4  211          -  505.12761      -  78.3  256s\n",
      "    31    33  505.12761    6  211          -  505.12761      -  60.7  260s\n",
      "    58    59  505.12761   13  213          -  505.12761      -  45.8  265s\n",
      "    80    80  505.12761   18  209          -  505.12761      -  40.9  270s\n",
      "   105   100  521.36402   22  208          -  505.12761      -  41.2  275s\n",
      "   129   124  526.76581   26  204          -  505.12761      -  39.6  280s\n",
      "   171   150  535.78895   33  201          -  505.12761      -  36.4  286s\n",
      "   203   177  537.98598   40  204          -  505.12761      -  34.8  291s\n",
      "   230   206  536.59989   46  196          -  505.12761      -  33.5  295s\n",
      "   298   258  545.16759   58  195          -  505.12761      -  30.7  301s\n",
      "   347   291  556.45352   64  185          -  505.12761      -  30.0  306s\n",
      "   407   331  556.84368   71  185          -  505.12761      -  28.5  310s\n",
      "   472   366  562.13160   83  179          -  505.12761      -  26.6  316s\n",
      "   533   404  568.49818   93  178          -  505.12761      -  24.8  321s\n",
      "   614   451 infeasible  108               -  505.12761      -  22.6  326s\n",
      "   697   490  586.09901  125  175          -  505.12761      -  21.9  333s\n",
      "   743   511  592.25474  134  173          -  505.12761      -  21.2  336s\n",
      "   787   537  604.91551  145  170          -  505.12761      -  20.6  340s\n",
      "   871   571  644.57033  165  171          -  505.12761      -  19.9  347s\n",
      "   912   595  688.63515  179  175          -  505.12761      -  19.5  351s\n",
      "   953   617  693.95314  185  172          -  505.12761      -  19.5  356s\n",
      "   990   635  697.73712  191  161          -  505.12761      -  19.8  360s\n",
      "  1022   658  702.47431  196  165          -  505.12761      -  20.2  366s\n",
      "  1062   688  722.24975  205  166          -  505.12761      -  20.2  370s\n",
      "  1111   718  723.27325  210  173          -  505.12761      -  20.1  376s\n",
      "  1159   746  725.26458  213  167          -  505.12761      -  20.5  381s\n",
      "  1217   777  741.22671  226  165          -  505.12761      -  20.1  387s\n",
      "  1268   799  747.52808  235  164          -  505.12761      -  20.2  392s\n",
      "  1308   834  777.20016  248  163          -  505.12761      -  20.3  398s\n",
      "  1369   859  785.45470  258  161          -  505.12761      -  20.1  404s\n",
      "  1412   889  791.78543  266  156          -  505.12761      -  20.3  410s\n",
      "  1463   921  807.05121  282  165          -  505.12761      -  20.3  417s\n",
      "  1509   951  812.80311  291  158          -  505.12761      -  20.4  423s\n",
      "  1560   979  815.02827  297  156          -  505.12761      -  20.6  430s\n",
      "  1607  1012  822.13099  305  143          -  505.12761      -  21.0  437s\n",
      "  1654  1065  831.76554  312  144          -  505.12761      -  21.3  444s\n",
      "  1742  1106  841.22484  331  137          -  505.12761      -  20.8  452s\n",
      "  1818  1149  859.24022  353  129          -  505.12761      -  20.7  459s\n",
      "  1903  1206  888.29571  379  130          -  505.12761      -  20.7  467s\n",
      "  1997  1268  919.45030  412  130          -  505.12761      -  20.6  475s\n",
      "  2091  1318  970.99914  444  133          -  505.12761      -  20.4  484s\n",
      "  2176  1365  994.75054  460  131          -  505.12761      -  20.6  492s\n",
      "  2265  1438 1001.50550  478  133          -  505.12761      -  20.7  502s\n",
      "  2366  1509 1031.47038  497  129          -  505.12761      -  21.2  512s\n",
      "  2473  1574 1046.94157  523  128          -  505.12761      -  21.6  521s\n",
      "  2570  1640 1075.10125  535  134          -  505.12761      -  21.9  531s\n",
      "  2663  1707 1078.81808  546  139          -  505.12761      -  22.2  542s\n",
      "  2765  1780 1087.29728  558  131          -  505.12761      -  22.8  553s\n",
      "  2865  1850 1089.77862  566  133          -  505.12761      -  23.4  565s\n",
      "  2984  1920 1152.11935  586  128          -  505.12761      -  23.7  578s\n",
      "  3109  1985 1192.37269  603  128          -  505.12761      -  24.2  591s\n",
      "  3248  2081 1237.78074  638  127          -  505.12761      -  24.5  604s\n",
      "  3399  2158 1297.70978  672  125          -  505.12761      -  24.8  619s\n",
      "  3533  2224 1339.16916  691  121          -  505.12761      -  25.0  633s\n",
      "  3663  2350 1352.60710  723  125          -  505.12761      -  25.3  649s\n",
      "  3858  2439  505.12761   20  197          -  505.12761      -  25.6  664s\n",
      "  4010  2586  505.64491   27  187          -  505.12761      -  26.4  679s\n",
      "  4195  2587  695.56175  305  211          -  505.12761      -  26.9  691s\n",
      "  4197  2588  759.19846  323  200          -  505.12761      -  26.9  795s\n",
      "  4198  2589  522.46194   54  200          -  505.12761      -  26.9  887s\n",
      "  4200  2590 1213.38866  602  200          -  505.12761      -  26.8  984s\n",
      "  4202  2592  689.42967  274  200          -  505.12761      -  26.8  989s\n",
      "  4203  2592  605.39997  147  200          -  505.12761      -  26.8  991s\n",
      "  4204  2593  563.31306  207  200          -  505.22377      -  26.8 1088s\n",
      "  4205  2597  505.22377   14  200          -  505.22377      -   1.4 1147s\n",
      "  4207  2600  505.22377   15  200          -  505.22377      -   1.4 1152s\n",
      "  4219  2605  505.22377   17  200          -  505.22377      -   1.4 1155s\n",
      "  4239  2615  519.56027   19  199          -  507.99975      -   1.6 1160s\n",
      "  4263  2627  530.22757   22  201          -  507.99975      -   1.8 1165s\n",
      "  4284  2636  530.22757   24  201          -  507.99975      -   2.0 1170s\n",
      "  4309  2645  526.67527   27  206          -  507.99975      -   2.1 1184s\n",
      "  4313  2650  526.69230   27  207          -  507.99975      -   2.1 1185s\n",
      "  4339  2662  526.67527   30  203          -  507.99975      -   2.2 1190s\n",
      "  4386  2683  526.67527   35  203          -  507.99975      -   2.4 1196s\n",
      "  4411  2694  526.85632   37  196          -  507.99975      -   2.6 1200s\n",
      "  4455  2713  526.67527   40  203          -  507.99975      -   2.8 1206s\n",
      "  4485  2726  526.67527   43  203          -  507.99975      -   2.9 1211s\n",
      "  4517  2736  535.88928   45  203          -  507.99975      -   3.1 1216s\n",
      "  4554  2757  534.07295   46  185          -  507.99975      -   3.2 1221s\n",
      "  4612  2779  534.26639   49  188          -  507.99975      -   3.5 1228s\n",
      "  4647  2794  534.26639   50  188          -  507.99975      -   3.8 1232s\n",
      "  4682  2813  534.27569   52  190          -  507.99975      -   4.1 1235s\n",
      "  4755  2839  541.45447   56  189          -  507.99975      -   4.6 1242s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  4791  2854  534.71408   59  184          -  507.99975      -   4.8 1245s\n",
      "  4881  2886  535.11426   63  184          -  507.99975      -   5.3 1252s\n",
      "  4928  2901  535.27468   66  180          -  507.99975      -   5.5 1256s\n",
      "  4981  2915  536.35900   71  181          -  507.99975      -   5.6 1260s\n",
      "  5074  2947  583.47912   87  175          -  507.99975      -   6.1 1268s\n",
      "  5129  2954  597.55837   95  166          -  507.99975      -   6.3 1272s\n",
      "  5183  2975  608.96494  105  166          -  507.99975      -   6.5 1276s\n",
      "  5255  2985  612.83919  113  166          -  507.99975      -   6.8 1280s\n",
      "  5312  2998  622.92699  122  163          -  507.99975      -   6.9 1285s\n",
      "  5456  3024  637.72436  145  154          -  507.99975      -   7.3 1294s\n",
      "  5542  3032  646.39355  163  157          -  507.99975      -   7.4 1299s\n",
      "  5622  3044  655.24377  174  157          -  507.99975      -   7.5 1304s\n",
      "  5715  3089  667.46225  187  154          -  507.99975      -   7.5 1309s\n",
      "  5819  3110  669.59101  199  149          -  507.99975      -   7.7 1315s\n",
      "  5926  3141  672.58031  218  154          -  507.99975      -   7.8 1321s\n",
      "  6032  3171  690.93826  236  150          -  507.99975      -   7.9 1327s\n",
      "  6131  3195 infeasible  246               -  507.99975      -   8.1 1333s\n",
      "  6228  3210 infeasible  262               -  507.99975      -   8.3 1339s\n",
      "  6317  3250  708.58948  272  150          -  507.99975      -   8.5 1346s\n",
      "  6435  3295  716.66140  282  148          -  507.99975      -   8.8 1352s\n",
      "  6574  3343  722.30384  305  151          -  507.99975      -   8.8 1359s\n",
      "  6701  3415  724.69594  318  137          -  507.99975      -   9.1 1366s\n",
      "  6837  3475  726.20530  324  138          -  507.99975      -   9.2 1374s\n",
      "  6994  3496  727.36662  331  135          -  507.99975      -   9.3 1381s\n",
      "  7124  3548  758.40570  346  135          -  507.99975      -   9.7 1390s\n",
      "  7259  3614  794.86746  360  136          -  507.99975      -   9.8 1398s\n",
      "  7413  3638  809.93957  374  126          -  507.99975      -  10.0 1407s\n",
      "  7557  3715  813.72609  381  118          -  507.99975      -  10.1 1416s\n",
      "  7728  3752  816.73002  390  116          -  507.99975      -  10.3 1426s\n",
      "  7859  3805  821.06689  400  111          -  507.99975      -  10.6 1435s\n",
      "  7991  3871  828.41461  415  111          -  507.99975      -  10.8 1444s\n",
      "  8143  3965  849.61848  435  111          -  507.99975      -  11.1 1455s\n",
      "  8342  4062  865.40649  459  108          -  507.99975      -  11.3 1466s\n",
      "  8586  4163  886.72883  485  109          -  507.99975      -  11.5 1477s\n",
      "  8811  4235  907.41367  504  101          -  507.99975      -  11.8 1489s\n",
      "  8996  4343  914.36446  522  105          -  507.99975      -  12.2 1501s\n",
      "  9191  4432 infeasible  532               -  507.99975      -  12.5 1514s\n",
      "  9383  4522 infeasible  548               -  507.99975      -  12.8 1526s\n",
      "  9612  4596 infeasible  575               -  507.99975      -  13.0 1539s\n",
      "  9848  4714 1071.20660  604   86          -  508.05665      -  13.0 1552s\n",
      " 10163  4857  527.55064   78  179          -  508.05665      -  13.0 1564s\n",
      " 10520  5038  571.14320  133  179          -  508.05665      -  13.0 1585s\n",
      " 10888  5089  587.03340  183  174          -  508.05665      -  12.9 1593s\n",
      " 11144  5202  627.80383  228  169          -  508.05665      -  12.9 1602s\n",
      " 11410  5288  650.75184  275  169          -  508.05665      -  12.9 1611s\n",
      " 11658  5357  705.83513  320  169          -  508.05665      -  13.0 1620s\n",
      " 11850  5442  731.67769  347  160          -  508.05665      -  13.1 1631s\n",
      " 12061  5487  753.68006  365  160          -  508.05665      -  13.4 1643s\n",
      " 12219  5706  764.48694  371  153          -  508.05665      -  13.7 1653s\n",
      " 12500  5932  959.36337  395  151          -  508.05819      -  13.9 1663s\n",
      " 12799  6213  542.12622   63  172          -  508.05819      -  14.0 1674s\n",
      " 13105  6451  608.95516  140  164          -  508.05819      -  14.1 1683s\n",
      " 13390  6703  643.75611  211  154          -  508.05819      -  14.2 1693s\n",
      " 13671  6894  704.65679  278  144          -  508.05819      -  14.3 1705s\n",
      " 13929  7079  752.80223  303  149          -  508.05819      -  14.5 1717s\n",
      " 14186  7261  839.27168  337  146          -  508.05819      -  14.6 1729s\n",
      " 14420  7470  516.16259   62  181          -  508.05819      -  14.8 1741s\n",
      " 14672  7652  554.05403  123  174          -  508.05819      -  15.0 1753s\n",
      " 14940  7885  588.21076  175  166          -  508.05819      -  15.1 1764s\n",
      " 15259  8155  646.62322  252  164          -  508.05819      -  15.0 1773s\n",
      " 15562  8404  679.80154  332  160          -  508.05819      -  15.0 1783s\n",
      " 15875  8646  721.80759  391  165          -  508.05819      -  15.0 1792s\n",
      " 16220  8879  769.98691  445  169          -  508.05819      -  14.9 1804s\n",
      " 16557  9085  854.38303  492  165          -  508.05819      -  15.0 1816s\n",
      " 16887  9330 infeasible  530               -  508.05819      -  15.1 1826s\n",
      " 17179  9547  528.44104   91  178          -  508.05819      -  15.1 1836s\n",
      " 17492  9792  551.43290  132  170          -  508.05819      -  15.2 1847s\n",
      " 17835 10042  604.14058  190  170          -  508.05819      -  15.2 1858s\n",
      " 18151 10268  655.51181  262  170          -  508.05819      -  15.2 1869s\n",
      " 18445 10465  703.96078  314  165          -  508.05819      -  15.2 1880s\n",
      " 18705 10651  756.79083  377  158          -  508.05819      -  15.3 1891s\n",
      " 18991 10880  791.94657  458  158          -  508.05819      -  15.3 1902s\n",
      " 19311 11145  842.05192  523  164          -  508.05819      -  15.4 1912s\n",
      " 19620 11389  904.44305  569  166          -  508.05819      -  15.5 1923s\n",
      " 19906 11639  923.64753  612  165          -  508.05819      -  15.5 1933s\n",
      " 20241 11953 1038.38726  642  159          -  508.05819      -  15.6 1942s\n",
      " 20595 12241  527.69350   95  179          -  508.05819      -  15.6 1951s\n",
      " 20937 12242  508.82428   30  211          -  508.05819      -  15.5 2183s\n",
      " 20939 12243  640.58728  128  200          -  508.05819      -  15.5 2294s\n",
      " 20940 12244  578.50465  141  200          -  508.05819      -  15.5 2391s\n",
      " 20942 12245  558.99913  125  211          -  508.05819      -  15.5 2442s\n",
      " 20943 12246  628.37873  181  211          -  508.05819      -  15.5 2451s\n",
      " 20944 12247  621.54279  182  205          -  508.05819      -  15.5 2461s\n",
      " 20945 12247  771.66730  310  205          -  508.05819      -  15.5 2470s\n",
      " 20946 12248  527.84283   84  207          -  508.05819      -  15.5 2513s\n",
      " 20947 12249  770.89860  439  207          -  508.05819      -  15.5 2520s\n",
      " 20948 12249  721.60803  382  207          -  508.05819      -  15.5 2568s\n",
      " 20949 12250  544.17389   76  207          -  508.05819      -  15.5 2577s\n",
      " 20950 12251  748.26523  427  223          -  508.05819      -  15.5 2598s\n",
      " 20952 12252  641.39195  254  217          -  518.83424      -  15.5 2607s\n",
      " 20953 12253 1041.33374  794  207          -  526.67527      -  15.5 2627s\n",
      " 20954 12253  698.83188  396  209          -  526.67527      -  15.5 2633s\n",
      " 20955 12254  551.33045   55  208          -  526.67527      -  15.5 2668s\n",
      " 20956 12255  526.67527   55  204          -  526.67527      -  15.5 2674s\n",
      " 20957 12255  898.84282  568  208          -  526.67527      -  15.5 2715s\n",
      " 20958 12256  560.28335   71  212          -  526.67527      -  15.5 2722s\n",
      " 20959 12257  612.03943  251  210          -  526.67527      -  15.5 2759s\n",
      " 20960 12257  794.55255  426  218          -  526.67527      -  15.5 2765s\n",
      " 20961 12258  669.26631  294  209          -  526.67527      -  15.5 2786s\n",
      " 20962 12259  680.12568  337  209          -  526.67527      -  15.5 2791s\n",
      "H20962 11645                    8431.2808340  526.67527  93.8%  15.5 2834s\n",
      "H20962 11062                    3477.4462577  526.67527  84.9%  15.5 2920s\n",
      "H20963 10511                    3336.9414326  526.67527  84.2%  16.3 2947s\n",
      " 20964 10512  526.67527   27  207 3336.94143  526.67527  84.2%  16.4 2957s\n",
      " 20966 10514  526.67527   28  207 3336.94143  526.67527  84.2%  16.4 2964s\n",
      " 20969 10515  526.67527   29  207 3336.94143  526.67527  84.2%  16.4 2972s\n",
      " 20977 10519  526.85632   30  201 3336.94143  526.67527  84.2%  16.4 2976s\n",
      " 20985 10524  526.85632   31  201 3336.94143  526.67527  84.2%  16.4 2980s\n",
      "H20989 10000                    3296.8658053  526.67527  84.0%  16.4 3013s\n",
      "H20993  9501                    3217.5138104  526.67527  83.6%  16.4 3119s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H20994  9027                    3205.4701901  526.67527  83.6%  16.4 3119s\n",
      " 20997  9031  526.67527   33  207 3205.47019  526.67527  83.6%  16.4 3127s\n",
      " 21012  9039  526.85632   35  201 3205.47019  526.78396  83.6%  16.4 3132s\n",
      " 21019  9044  526.85632   36  201 3205.47019  526.78396  83.6%  16.4 3135s\n",
      " 21035  9056  535.83896   38  202 3205.47019  526.78396  83.6%  16.4 3140s\n",
      " 21055  9070  543.86997   40  197 3205.47019  526.78396  83.6%  16.5 3146s\n",
      " 21067  9070  543.91594   42  194 3205.47019  526.78396  83.6%  16.5 3159s\n",
      " 21071  9074  544.19449   42  200 3205.47019  526.78396  83.6%  16.5 3163s\n",
      " 21082  9082  544.57330   43  192 3205.47019  526.78396  83.6%  16.5 3166s\n",
      " 21108  9087  547.13087   47  190 3205.47019  526.78396  83.6%  16.5 3172s\n",
      " 21118  9095  547.24964   48  194 3205.47019  526.78396  83.6%  16.5 3176s\n",
      " 21145  9109  547.45674   51  191 3205.47019  526.78396  83.6%  16.5 3183s\n",
      " 21161  9117  547.63758   53  191 3205.47019  526.78396  83.6%  16.5 3187s\n",
      " 21180  9121  555.16308   57  185 3205.47019  526.78396  83.6%  16.5 3191s\n",
      " 21217  9133  555.98891   62  180 3205.47019  526.78396  83.6%  16.6 3197s\n",
      " 21237  9138  556.28789   65  184 3205.47019  526.78396  83.6%  16.6 3202s\n",
      " 21260  9141  557.13419   70  181 3205.47019  526.78396  83.6%  16.6 3206s\n",
      " 21280  9146  557.63504   74  181 3205.47019  526.78396  83.6%  16.6 3210s\n",
      "H21322  8701                    2962.0106723  526.78396  82.2%  16.6 3219s\n",
      " 21343  8708  576.76046   84  179 2962.01067  526.78396  82.2%  16.6 3224s\n",
      " 21359  8719  558.30562   86  177 2962.01067  526.78396  82.2%  16.6 3229s\n",
      " 21384  8724  558.44708   90  181 2962.01067  526.78396  82.2%  16.6 3235s\n",
      "H21388  8294                    2653.5875114  526.78396  80.1%  16.6 3235s\n",
      "H21393  7885                    2652.3095494  526.78396  80.1%  16.6 3235s\n",
      "H21398  7497                    2611.8465899  526.78396  79.8%  16.6 3235s\n",
      "H21403  7129                    2567.0580552  526.78396  79.5%  16.6 3235s\n",
      " 21405  7147  577.06442   92  178 2567.05806  526.78396  79.5%  16.6 3240s\n",
      " 21431  7156  558.78184   96  183 2567.05806  526.78396  79.5%  16.6 3246s\n",
      " 21458  7178  579.62129  100  180 2567.05806  526.78396  79.5%  16.7 3251s\n",
      "H21492  6822                    2532.4474813  526.78396  79.2%  16.7 3320s\n",
      "H21494  6489                    2476.7974239  526.78396  78.7%  16.7 3320s\n",
      "H21495  6174                    2470.2698239  526.78396  78.7%  16.7 3320s\n",
      " 21496  6194  579.63734  106  181 2470.26982  526.78396  78.7%  16.7 3327s\n",
      " 21527  6208  579.75097  111  179 2470.26982  526.78396  78.7%  16.7 3333s\n",
      " 21557  6218  580.17833  115  178 2470.26982  526.78396  78.7%  16.7 3339s\n",
      " 21587  6232  579.82595  119  174 2470.26982  526.78396  78.7%  16.7 3345s\n",
      " 21617  6247  579.82595  124  173 2470.26982  526.78396  78.7%  16.8 3352s\n",
      " 21647  6270  579.82595  129  173 2470.26982  526.78396  78.7%  16.8 3359s\n",
      " 21684  6281  588.35194  134  172 2470.26982  526.78396  78.7%  16.8 3367s\n",
      " 21718  6297  588.35194  138  172 2470.26982  526.78396  78.7%  16.9 3375s\n",
      " 21756  6309  588.49686  144  167 2470.26982  526.78396  78.7%  16.9 3384s\n",
      " 21792  6328  592.36029  148  171 2470.26982  526.78396  78.7%  17.0 3392s\n",
      " 21829  6339  592.60068  152  167 2470.26982  526.78396  78.7%  17.0 3400s\n",
      " 21871  6351  598.33753  157  165 2470.26982  526.78396  78.7%  17.1 3409s\n",
      " 21914  6365  615.50525  164  162 2470.26982  526.78396  78.7%  17.1 3418s\n",
      " 21969  6381  636.55728  174  158 2470.26982  526.78396  78.7%  17.2 3427s\n",
      " 22029  6403  647.09663  182  152 2470.26982  526.78396  78.7%  17.2 3437s\n",
      " 22090  6424  671.18180  191  149 2470.26982  526.78396  78.7%  17.3 3448s\n",
      " 22149  6447  692.53813  202  147 2470.26982  526.78396  78.7%  17.3 3460s\n",
      " 22213  6481  703.82836  213  142 2470.26982  526.78396  78.7%  17.4 3471s\n",
      " 22312  6510  729.46265  237  136 2470.26982  526.78396  78.7%  17.5 3483s\n",
      " 22424  6528  749.51924  258  136 2470.26982  526.78396  78.7%  17.6 3495s\n",
      "H22534  6196                    2372.3363684  526.78396  77.8%  17.7 3586s\n",
      "H22535  5911                    2340.6388231  526.78396  77.5%  17.7 3586s\n",
      "H22536  5641                    2230.0285069  526.78396  76.4%  17.7 3586s\n",
      " 22538  5706  777.62644  276  130 2230.02851  526.78396  76.4%  17.7 3598s\n",
      " 22644  5736  792.72989  290  128 2230.02851  526.78396  76.4%  17.7 3611s\n",
      " 22741  5773  802.54463  301  131 2230.02851  526.78396  76.4%  17.8 3626s\n",
      " 22884  5797  860.52700  343  116 2230.02851  526.78396  76.4%  17.8 3641s\n",
      " 23027  5815  884.54961  382  120 2230.02851  526.78396  76.4%  17.8 3655s\n",
      " 23155  5853  912.82352  413  110 2230.02851  526.78396  76.4%  17.8 3671s\n",
      " 23284  5891  928.32098  431  108 2230.02851  526.78396  76.4%  17.8 3686s\n",
      " 23410  5926  929.50918  443  117 2230.02851  526.78396  76.4%  17.8 3702s\n",
      " 23539  5965  948.64378  457  114 2230.02851  526.78396  76.4%  17.9 3718s\n",
      " 23640  6013  949.98020  467  103 2230.02851  526.78396  76.4%  17.9 3735s\n",
      " 23752  6060  964.88424  491   99 2230.02851  526.78396  76.4%  18.1 3752s\n",
      " 23890  6102  994.38586  519   97 2230.02851  526.78396  76.4%  18.3 3769s\n",
      " 23951  6081 1109.86630  526   74 2230.02851  526.78396  76.4%  18.3 3770s\n",
      " 24015  6163 1012.22057  544   97 2230.02851  526.78396  76.4%  18.4 3788s\n",
      " 24149  6215 1028.06669  565   99 2230.02851  526.78396  76.4%  18.6 3805s\n",
      " 24279  6269 1043.53330  580   91 2230.02851  526.78396  76.4%  18.7 3822s\n",
      " 24405  6321 1073.99743  607   83 2230.02851  526.78396  76.4%  18.8 3841s\n",
      " 24606  6319 infeasible  667      2230.02851  526.78396  76.4%  18.8 3858s\n",
      " 24746  6365 1111.34030  712   82 2230.02851  526.78396  76.4%  18.9 3877s\n",
      " 24889  6450 1179.34491  736   81 2230.02851  526.78396  76.4%  19.0 3897s\n",
      " 25041  6507 infeasible  757      2230.02851  526.78396  76.4%  19.1 3918s\n",
      " 25173  6573 1231.66107  782   63 2230.02851  526.78396  76.4%  19.1 3940s\n",
      " 25300  6633 1233.65746  798   66 2230.02851  526.78396  76.4%  19.2 3962s\n",
      "H25443  6332                    2186.6835224  526.78396  75.9%  19.3 4087s\n",
      "H25445  6087                    2128.2218093  526.78396  75.2%  19.3 4087s\n",
      " 25447  6206 1271.62845  825   65 2128.22181  526.78396  75.2%  19.3 4109s\n",
      " 25612  6296 1303.64920  864   76 2128.22181  526.78396  75.2%  19.4 4133s\n",
      "H25797  6170                    1979.5459123  526.78396  73.4%  19.5 4160s\n",
      "H26006  5884                    1962.2648235  526.81122  73.2%  19.7 4280s\n",
      "H26007  5675                    1935.8575226  526.81122  72.8%  19.7 4280s\n",
      "H26008  5476                    1925.3051240  526.81122  72.6%  19.7 4280s\n",
      "H26009  5287                    1844.2239752  526.81122  71.4%  19.7 4280s\n",
      " 26010  5479  549.62568   57  212 1844.22398  526.81122  71.4%  19.7 4307s\n",
      " 26243  5607  557.18970  105  191 1844.22398  526.81122  71.4%  19.7 4334s\n",
      " 26476  5776  578.09007  155  191 1844.22398  526.81122  71.4%  19.7 4362s\n",
      " 26782  5943  588.32402  199  176 1844.22398  526.81122  71.4%  19.7 4391s\n",
      " 27100  6079  602.63516  241  177 1844.22398  526.81122  71.4%  19.7 4421s\n",
      " 27398  6231  613.96307  277  176 1844.22398  526.81122  71.4%  19.7 4450s\n",
      " 27677  6388  615.77859  297  176 1844.22398  526.81122  71.4%  19.6 4481s\n",
      " 27956  6514  624.04408  319  180 1844.22398  526.81122  71.4%  19.6 4516s\n",
      " 28215  6626  629.64739  343  192 1844.22398  526.81122  71.4%  19.6 4554s\n",
      "H28463  6368                    1833.8810838  526.81122  71.3%  19.6 4686s\n",
      "H28464  6197                    1808.5174490  526.81122  70.9%  19.6 4686s\n",
      " 28467  6410  632.37746  363  186 1808.51745  526.81122  70.9%  19.6 4723s\n",
      " 28698  6560  636.08352  385  175 1808.51745  526.81122  70.9%  19.7 4761s\n",
      " 28968  6680  641.77063  422  177 1808.51745  526.81122  70.9%  19.8 4798s\n",
      " 29207  6833  646.39540  443  209 1808.51745  526.81122  70.9%  19.8 4839s\n",
      " 29453  6966  649.20911  466  235 1808.51745  526.81122  70.9%  19.8 4875s\n",
      " 29677  7118  653.16827  495  228 1808.51745  526.81122  70.9%  19.9 4913s\n",
      " 29912  7228  669.36109  534  217 1808.51745  526.81122  70.9%  20.0 4971s\n",
      " 30115  7418  669.93009  536  221 1808.51745  526.81122  70.9%  20.0 5011s\n",
      " 30394  7593  679.94295  574  217 1808.51745  526.81122  70.9%  20.0 5051s\n",
      " 30704  7749  685.18753  608  219 1808.51745  526.81122  70.9%  20.0 5088s\n",
      " 30968  7982  687.21205  630  208 1808.51745  526.81122  70.9%  20.0 5123s\n",
      " 31235  8201  700.29398  666  235 1808.51745  526.81122  70.9%  20.0 5178s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 31533  8386  702.96118  720  232 1808.51745  526.81122  70.9%  20.0 5220s\n",
      " 31805  8575  760.25239  754  225 1808.51745  526.81122  70.9%  20.1 5259s\n",
      "H32087  8572                    1787.7616340  526.81122  70.5%  20.1 5447s\n",
      "H32088  8572                    1786.3149287  526.81122  70.5%  20.1 5447s\n",
      " 32091  8772  767.92948  784  212 1786.31493  526.81122  70.5%  20.1 5495s\n",
      " 32380  8989  772.22364  821  251 1786.31493  526.81122  70.5%  20.2 5562s\n",
      " 32623  9218  790.72433  866  187 1786.31493  526.81122  70.5%  20.2 5606s\n",
      " 32883  9456  801.88578  904  268 1786.31493  526.81122  70.5%  20.2 5647s\n",
      " 33165  9743  814.11223  945  205 1786.31493  526.81122  70.5%  20.3 5689s\n",
      " 33483  9982  818.58831  987  170 1786.31493  526.81122  70.5%  20.4 5728s\n",
      " 33779 10236  820.80700 1013  189 1786.31493  526.81122  70.5%  20.4 5768s\n",
      " 34089 10474  823.35705 1050  162 1786.31493  526.81122  70.5%  20.4 5803s\n",
      "H34399 10150                    1677.0192362  526.81122  68.6%  20.5 5922s\n",
      "H34415  9998                    1640.5061689  526.81122  67.9%  20.5 5922s\n",
      "H34431  9930                    1609.3553513  526.81122  67.3%  20.5 5922s\n",
      " 34463  9965  825.78397 1100  169 1609.35535  526.81122  67.3%  20.5 6126s\n",
      "H34473  9800                    1560.4319563  526.81122  66.2%  20.5 6126s\n",
      " 34507 10036  826.23898 1111  218 1560.43196  526.81122  66.2%  20.5 6159s\n",
      " 34815 10269  828.61858 1143  206 1560.43196  526.81122  66.2%  20.5 6206s\n",
      "H35065  9754                    1504.4000564  526.81122  65.0%  20.6 6347s\n",
      "H35131  9452                    1457.5486740  526.81122  63.9%  20.6 6347s\n",
      " 35132  9726  829.36219 1177  208 1457.54867  526.81122  63.9%  20.6 6395s\n",
      " 35421  9979  831.14047 1228  210 1457.54867  526.81122  63.9%  20.6 6440s\n",
      " 35689 10265  833.15079 1273  213 1457.54867  526.81122  63.9%  20.8 6487s\n",
      " 35992 10489  836.15685 1313  195 1457.54867  526.81122  63.9%  20.8 6536s\n",
      " 36250 10746  839.32262 1339  186 1457.54867  526.81122  63.9%  21.0 6589s\n",
      " 36546 11051  849.88420 1367  250 1457.54867  526.81122  63.9%  21.0 6633s\n",
      " 36857 11310  858.08268 1403  232 1457.54867  526.81122  63.9%  21.1 6677s\n",
      " 37132 11397  862.05988 1440  207 1457.54867  526.81122  63.9%  21.1 6861s\n",
      "H37148 10989                    1372.4694437  526.81122  61.6%  21.1 6861s\n",
      " 37231 11246  863.05253 1457  240 1372.46944  526.81122  61.6%  21.2 6911s\n",
      " 37556 11496  866.02798 1514  238 1372.46944  526.81122  61.6%  21.3 6966s\n",
      " 37840 11730  867.77370 1541  198 1372.46944  526.81122  61.6%  21.4 6999s\n",
      " 38143 11958  868.24104 1568  162 1372.46944  526.81122  61.6%  21.4 7035s\n",
      " 38444 12147  928.27105 1593  192 1372.46944  526.81122  61.6%  21.6 7068s\n",
      " 38749 12344  935.05126 1622  184 1372.46944  526.81122  61.6%  21.6 7099s\n",
      " 39056 12556  943.31363 1653  198 1372.46944  526.81122  61.6%  21.7 7132s\n",
      " 39370 12808  950.57027 1691  196 1372.46944  526.81122  61.6%  21.7 7164s\n",
      " 39675 13061  957.84796 1720  180 1372.46944  526.81122  61.6%  21.7 7200s\n",
      "\n",
      "Cutting planes:\n",
      "  Gomory: 173\n",
      "  MIR: 1\n",
      "  Flow cover: 7\n",
      "  Zero half: 46\n",
      "  RLT: 91\n",
      "\n",
      "Explored 39970 nodes (991899 simplex iterations) in 7200.58 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 10: 1372.47 1457.55 1504.4 ... 1808.52\n",
      "\n",
      "Time limit reached\n",
      "Best objective 1.372469443688e+03, best bound 5.268112248488e+02, gap 61.6158%\n"
     ]
    }
   ],
   "source": [
    "veh_capacity = veh_capacities\n",
    "veh_depot = [0 for i in range(len(vehicles))]\n",
    "time_limit = 7200\n",
    "vrp_routes, vrp_veh_routes, vrp_node_routes, vrp_X, vrp_Y, vrp_depot, vrp_obj_val = MDCVRP_BP(cust_x, cust_y, cust_demand_item, \n",
    "                                                                                              depot_x, depot_y, vehicles, \n",
    "                                                                                              veh_depot, veh_capacity, \n",
    "                                                                                              time_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20ffff72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "Overall objective funtion value - 1372\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('-----\\nOverall objective funtion value -', vrp_obj_val)\n",
    "for r in range(len(vrp_routes)):\n",
    "    for n in range(len(vrp_routes[r])-1):\n",
    "        i = vrp_routes[r][n]\n",
    "        j = vrp_routes[r][n+1]\n",
    "        plt.plot([vrp_X[i], vrp_X[j]], [vrp_Y[i], vrp_Y[j]], color='blue')\n",
    "plt.scatter(vrp_X[:num_cust], vrp_Y[:num_cust], color='blue', marker='.')\n",
    "plt.scatter(vrp_X[num_cust:], vrp_Y[num_cust:], color='red', marker='x', label='Depot')\n",
    "plt.legend()\n",
    "plt.title('Network Graph of Single-Depot Vehicle Routing')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cb365c",
   "metadata": {},
   "source": [
    "#### ____\n",
    "### Multi-Depot Constrianed Vehicle Routing Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5456f3dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changed value of parameter timelimit to 7200.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 95663 rows, 96508 columns and 750816 nonzeros\n",
      "Model fingerprint: 0x8f599fc5\n",
      "Variable types: 100 continuous, 96408 integer (96408 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+02]\n",
      "  Objective range  [1e+00, 1e+02]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 5400 rows and 5400 columns\n",
      "Presolve time: 0.70s\n",
      "Presolved: 90263 rows, 91108 columns, 721116 nonzeros\n",
      "Variable types: 100 continuous, 91008 integer (91008 binary)\n",
      "\n",
      "Deterministic concurrent LP optimizer: primal and dual simplex\n",
      "Showing first log only...\n",
      "\n",
      "Concurrent spin time: 0.01s\n",
      "\n",
      "Solved with dual simplex\n",
      "\n",
      "Root relaxation: objective 4.926287e+02, 596 iterations, 0.57 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  492.62871    0  201          -  492.62871      -     -    2s\n",
      "     0     0  492.62871    0  199          -  492.62871      -     -    4s\n",
      "     0     0  492.62871    0  199          -  492.62871      -     -    5s\n",
      "     0     0  492.62871    0  198          -  492.62871      -     -    7s\n",
      "     0     0  492.62871    0  198          -  492.62871      -     -   29s\n",
      "     0     0  492.62871    0  208          -  492.62871      -     -   31s\n",
      "     0     0  492.62871    0  202          -  492.62871      -     -   32s\n",
      "     0     0  492.62871    0  205          -  492.62871      -     -   33s\n",
      "     0     0  492.62871    0  205          -  492.62871      -     -   36s\n",
      "     0     2  501.54948    0  205          -  501.54948      -     -   68s\n",
      "    15    18  501.54948    5  205          -  501.54948      -  58.8   70s\n",
      "    80    83  501.54948   23  209          -  501.54948      -  19.5   76s\n",
      "   135   142  501.54948   30  195          -  501.54948      -  17.7   81s\n",
      "   191   182  516.68284   46  186          -  501.54948      -  17.8   85s\n",
      "   279   250  520.41897   70  177          -  501.54948      -  19.8   91s\n",
      "   347   305  526.45530   88  161          -  501.54948      -  21.4   95s\n",
      "   417   348  548.52956  105  164          -  501.54948      -  22.1  100s\n",
      "   478   415  646.03682  122  147          -  501.54948      -  24.4  105s\n",
      "   572   479  656.07264  142  141          -  501.54948      -  25.0  112s\n",
      "   618   517  673.50765  145  140          -  501.54948      -  25.2  115s\n",
      "   720   566  685.20139  165  141          -  501.54948      -  25.1  121s\n",
      "   746   596  689.66224  174  142          -  501.54948      -  26.2  125s\n",
      "   843   653  802.30646  207  120          -  501.54948      -  26.4  132s\n",
      "   883   687  809.88247  217  120          -  501.54948      -  26.9  135s\n",
      "   987   730  834.94399  228  109          -  501.54948      -  27.9  143s\n",
      "  1014   773  843.82665  236  119          -  501.54948      -  28.4  147s\n",
      "  1072   830  900.71332  249  110          -  501.54948      -  28.3  153s\n",
      "  1155   868 infeasible  265               -  501.54948      -  28.0  157s\n",
      "  1213   907  949.56926  284   83          -  501.54948      -  28.0  162s\n",
      "  1267   941  987.35682  298   77          -  501.54948      -  28.3  167s\n",
      "  1333   985 1019.09606  310   71          -  501.54948      -  28.8  172s\n",
      "  1413  1050 1102.87634  330   63          -  501.54948      -  28.5  177s\n",
      "  1506  1119 1180.74691  348   55          -  501.54948      -  28.2  183s\n",
      "  1609  1181 1272.91699  366   43          -  501.54948      -  27.6  188s\n",
      "  1703  1249 1354.27224  389   23          -  501.54948      -  27.6  194s\n",
      "  1809  1304 1788.95180  416    3          -  501.54948      -  27.0  200s\n",
      "* 1810  1302             416    1802.0223832  501.54948  72.2%  26.9  200s\n",
      "* 1893  1289             364    1496.4715334  501.54948  66.5%  26.3  200s\n",
      "H 1908  1289                    1488.8136399  501.54948  66.3%  26.6  269s\n",
      "H 1909  1289                    1487.6811311  501.54948  66.3%  26.6  269s\n",
      "H 1909  1282                    1387.7566404  501.54948  63.9%  26.6  269s\n",
      "H 1911  1282                    1386.9727301  501.54948  63.8%  26.6  269s\n",
      "  1912  1337  501.54948   20  198 1386.97273  501.54948  63.8%  26.7  275s\n",
      "H 1981  1340                    1379.7861273  501.54948  63.7%  26.7  336s\n",
      "H 1982  1338                    1354.9893637  501.54948  63.0%  26.7  336s\n",
      "  1985  1386  501.54948   41  194 1354.98936  501.54948  63.0%  26.8  342s\n",
      "  2054  1480  501.54948   54  198 1354.98936  501.54948  63.0%  26.5  349s\n",
      "  2148  1569  501.82123   69  200 1354.98936  501.54948  63.0%  26.0  356s\n",
      "  2238  1635  502.08043   84  200 1354.98936  501.54948  63.0%  25.5  364s\n",
      "  2305  1690  502.53587   96  198 1354.98936  501.54948  63.0%  25.4  371s\n",
      "  2368  1742  502.89348  108  197 1354.98936  501.54948  63.0%  25.6  379s\n",
      "  2432  1790  623.73368  116  175 1354.98936  501.54948  63.0%  26.1  388s\n",
      "  2496  1873  624.43132  123  176 1354.98936  501.54948  63.0%  26.4  397s\n",
      "  2581  1874  576.43219  113  205 1354.98936  501.54948  63.0%  26.2  406s\n",
      "H 2582  1781                    1352.3803504  501.54948  62.9%  26.2  415s\n",
      "  2584  1782  578.33442  133  198 1352.38035  501.54948  62.9%  26.1  425s\n",
      "H 2587  1694                    1336.8722048  501.54948  62.5%  26.1  464s\n",
      "  2589  1697  501.54948   10  207 1336.87220  501.54948  62.5%  27.8  491s\n",
      "  2602  1704  501.54948   13  208 1336.87220  501.54948  62.5%  27.7  495s\n",
      "  2639  1725  501.54948   18  207 1336.87220  501.54948  62.5%  27.7  500s\n",
      "H 2665  1656                    1333.3462204  501.54948  62.4%  27.5  553s\n",
      "H 2666  1575                    1305.0178330  501.54948  61.6%  27.5  553s\n",
      "  2676  1585  536.17554   23  206 1305.01783  501.54948  61.6%  27.5  555s\n",
      "  2729  1617  538.71205   31  199 1305.01783  501.54948  61.6%  27.4  560s\n",
      "  2798  1658  538.55473   42  208 1305.01783  501.54948  61.6%  27.1  565s\n",
      "  2847  1691  544.16480   48  209 1305.01783  501.54948  61.6%  27.1  570s\n",
      "  2892  1714  545.50283   54  199 1305.01783  501.54948  61.6%  27.2  575s\n",
      "  2943  1737  555.87905   61  192 1305.01783  501.54948  61.6%  27.3  580s\n",
      "  3001  1777  560.71430   68  185 1305.01783  501.54948  61.6%  27.4  585s\n",
      "  3070  1805  568.16023   76  190 1305.01783  501.54948  61.6%  27.5  591s\n",
      "  3100  1820  574.55676   80  192 1305.01783  501.54948  61.6%  27.8  595s\n",
      "  3132  1836  580.41074   83  188 1305.01783  501.54948  61.6%  28.2  600s\n",
      "  3176  1871  541.73761   87  199 1305.01783  501.54948  61.6%  28.5  605s\n",
      "  3221  1903  541.95417   91  201 1305.01783  501.54948  61.6%  28.7  610s\n",
      "  3280  1947  594.06749   94  193 1305.01783  501.54948  61.6%  29.3  615s\n",
      "  3353  1990  594.14611   98  187 1305.01783  501.54948  61.6%  29.6  621s\n",
      "  3429  2047  542.41047  104  198 1305.01783  501.54948  61.6%  29.9  627s\n",
      "  3468  2076  542.53432  107  195 1305.01783  501.54948  61.6%  30.0  631s\n",
      "  3564  2147  542.68770  112  195 1305.01783  501.54948  61.6%  30.2  637s\n",
      "  3614  2187  542.75109  114  197 1305.01783  501.54948  61.6%  30.4  640s\n",
      "H 3711  2119                    1304.1583079  501.54948  61.5%  31.1  687s\n",
      "H 3712  2046                    1257.9081710  501.54948  60.1%  31.1  687s\n",
      "  3715  2079  612.24120  121  178 1257.90817  501.54948  60.1%  31.3  691s\n",
      "  3749  2122  611.24258  124  180 1257.90817  501.54948  60.1%  31.7  695s\n",
      "  3848  2174  617.68689  131  175 1257.90817  501.54948  60.1%  32.3  704s\n",
      "  3890  2186  551.02600  134  192 1257.90817  501.54948  60.1%  32.7  707s\n",
      "  3916  2229  551.03551  136  198 1257.90817  501.54948  60.1%  32.9  712s\n",
      "  3968  2258  623.72006  140  179 1257.90817  501.54948  60.1%  33.3  717s\n",
      "  4014  2300  624.19685  144  185 1257.90817  501.54948  60.1%  33.7  722s\n",
      "  4072  2335  551.36374  149  193 1257.90817  501.54948  60.1%  34.1  727s\n",
      "  4127  2375  551.61745  153  191 1257.90817  501.54948  60.1%  34.6  732s\n",
      "  4185  2406  650.73087  157  174 1257.90817  501.54948  60.1%  35.2  738s\n",
      "  4239  2467  661.12378  161  180 1257.90817  501.54948  60.1%  36.1  744s\n",
      "  4321  2516  680.02959  177  167 1257.90817  501.54948  60.1%  36.3  750s\n",
      "  4398  2570  681.97265  187  161 1257.90817  501.54948  60.1%  36.6  756s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  4479  2650  707.92960  195  155 1257.90817  501.54948  60.1%  36.9  763s\n",
      "  4593  2726  723.18375  221  142 1257.90817  501.54948  60.1%  37.0  770s\n",
      "  4709  2790  724.71257  237  142 1257.90817  501.54948  60.1%  36.9  777s\n",
      "  4817  2811  729.27894  250  138 1257.90817  501.54948  60.1%  37.0  786s\n",
      "  4894  2854  752.31059  256  148 1257.90817  501.54948  60.1%  37.1  794s\n",
      "  4984  2867  810.26467  263  139 1257.90817  501.54948  60.1%  37.3  802s\n",
      "  5042  2916  808.92903  267  160 1257.90817  501.54948  60.1%  37.7  811s\n",
      "  5130  2952  731.82705  272  153 1257.90817  501.54948  60.1%  38.0  820s\n",
      "  5228  3019  836.77307  278  138 1257.90817  501.54948  60.1%  38.8  829s\n",
      "  5340  2995  782.07355  284  142 1257.90817  501.54948  60.1%  39.1  839s\n",
      "  5360  2997  732.63965  287  150 1257.90817  501.54948  60.1%  39.5  851s\n",
      "  5372  3010  732.67707  288  150 1257.90817  501.54948  60.1%  39.8  863s\n",
      "  5404  3019  732.73220  291  150 1257.90817  501.54948  60.1%  40.1  879s\n",
      "  5428  3033  732.76141  293  155 1257.90817  501.54948  60.1%  40.8  888s\n",
      "  5452  3049  732.77072  296  145 1257.90817  501.54948  60.1%  41.2  897s\n",
      "  5478  3057  732.78706  299  152 1257.90817  501.54948  60.1%  41.2  925s\n",
      "  5497  3070  732.79610  300  152 1257.90817  501.54948  60.1%  43.1  937s\n",
      "  5521  3087  733.01202  301  150 1257.90817  501.54948  60.1%  43.3  946s\n",
      "  5549  3120  827.46106  303  137 1257.90817  501.54948  60.1%  43.4  955s\n",
      "  5598  3215  733.31623  305  146 1257.90817  501.54948  60.1%  43.3  967s\n",
      "  5764  3339  734.05878  312  147 1257.90817  501.54948  60.1%  43.6  981s\n",
      "  5951  3404  734.49790  319  148 1257.90817  501.54948  60.1%  43.5  996s\n",
      "  6087  3508  859.96315  325  138 1257.90817  501.54948  60.1%  43.7 1010s\n",
      "  6249  3531  869.47902  333  143 1257.90817  501.54948  60.1%  43.7 1031s\n",
      "  6334  3535  893.34866  343  136 1257.90817  501.54948  60.1%  44.6 1044s\n",
      "  6367  3725 1013.19469  343  132 1257.90817  501.54948  60.1%  44.8 1060s\n",
      "  6599  3808 1157.83741  371  126 1257.90817  501.54948  60.1%  44.5 1076s\n",
      "  6821  3932 1080.08836  398  133 1257.90817  501.54948  60.1%  43.8 1119s\n",
      "H 6977  3932                    1232.3190807  501.54948  59.3%  43.7 1119s\n",
      "  7036  3930 1207.01648  423  136 1232.31908  501.54948  59.3%  43.4 1174s\n",
      "H 7037  3930                    1232.2743462  501.54948  59.3%  43.4 1174s\n",
      "H 7039  3930                    1226.8185316  501.54948  59.1%  43.4 1174s\n",
      "  7040  4000 1207.09658  423  134 1226.81853  501.54948  59.1%  43.4 1188s\n",
      "  7161  4126  516.15178   16  213 1226.81853  501.54948  59.1%  43.4 1205s\n",
      "  7321  4346  516.60520   51  214 1226.81853  501.54948  59.1%  43.0 1225s\n",
      "  7604  4568  516.83972  111  215 1226.81853  501.54948  59.1%  42.1 1246s\n",
      "  7871  4580  517.50051  165  226 1226.81853  501.54948  59.1%  41.6 1303s\n",
      "H 7873  4577                    1222.6528813  501.54948  59.0%  41.6 1303s\n",
      "  7883  4671  517.50178  168  212 1222.65288  501.54948  59.0%  41.6 1319s\n",
      "  7978  4977  517.72790  185  218 1222.65288  501.54948  59.0%  41.4 1343s\n",
      "  8294  5269  518.16243  231  218 1222.65288  501.54948  59.0%  40.6 1366s\n",
      "  8595  5509  519.35776  282  216 1222.65288  501.54948  59.0%  39.9 1391s\n",
      "  8837  5643  647.85848  327  193 1222.65288  501.54948  59.0%  39.4 1411s\n",
      "  8975  5846  650.57084  351  195 1222.65288  501.54948  59.0%  39.2 1435s\n",
      "  9189  6031  658.42438  385  183 1222.65288  501.54948  59.0%  39.0 1461s\n",
      "  9459  6230  674.36564  426  187 1222.65288  501.54948  59.0%  38.6 1486s\n",
      "  9670  6540  679.75155  450  187 1222.65288  501.54948  59.0%  38.2 1515s\n",
      " 10020  6889  696.71287  503  187 1222.65288  501.54948  59.0%  37.7 1543s\n",
      " 10370  7234  704.13996  553  192 1222.65288  501.54948  59.0%  37.3 1589s\n",
      " 10726  7782  713.50162  613  189 1222.65288  501.54948  59.0%  37.2 1629s\n",
      " 11328  8001  792.01781  737  164 1222.65288  501.54948  59.0%  36.9 1657s\n",
      " 11578  8408  910.33248  795  145 1222.65288  501.54948  59.0%  36.8 1689s\n",
      " 12068  8721  921.92735  850  158 1222.65288  501.54948  59.0%  36.3 1717s\n",
      " 12384  9105  931.98908  889  148 1222.65288  501.54948  59.0%  36.3 1752s\n",
      " 12835  9542  541.85422  145  196 1222.65288  501.54948  59.0%  36.0 1784s\n",
      " 13276  9546  704.00198  299  184 1222.65288  501.54948  59.0%  35.7 1867s\n",
      "H13277  9387                    1203.6259034  501.54948  58.3%  35.7 1867s\n",
      " 13280  9544  704.02557  300  170 1203.62590  501.54948  58.3%  35.7 1894s\n",
      "H13438  9872                    1201.7387274  501.54948  58.3%  35.5 1925s\n",
      "H13505  9858                    1194.2597291  501.54948  58.0%  35.5 1925s\n",
      "H13589  9782                    1174.2263441  501.54948  57.3%  35.5 1925s\n",
      "H13780  9650                    1132.1088097  501.54948  55.7%  35.4 1998s\n",
      " 13784 10056  707.08312  404  186 1132.10881  501.54948  55.7%  35.4 2031s\n",
      " 14224 10457  827.10158  469  167 1132.10881  501.54948  55.7%  35.4 2066s\n",
      " 14631 10696  835.45278  551  173 1132.10881  501.54948  55.7%  35.3 2098s\n",
      " 14907 11151  841.90932  606  182 1132.10881  501.54948  55.7%  35.3 2137s\n",
      " 15404 11517  853.32908  683  154 1132.10881  501.54948  55.7%  35.2 2170s\n",
      " 15770 11762  863.88930  753  169 1132.10881  501.54948  55.7%  35.5 2202s\n",
      " 16016 12091  878.56820  804  164 1132.10881  501.54948  55.7%  35.8 2236s\n",
      " 16346 12424  900.93586  905  183 1132.10881  501.54948  55.7%  36.2 2269s\n",
      " 16684 12556  912.13643  991  172 1132.10881  501.54948  55.7%  36.3 2331s\n",
      " 16820 12939  913.84861 1028  179 1132.10881  501.54948  55.7%  36.4 2368s\n",
      " 17221 13192  940.06982 1143  152 1132.10881  501.54948  55.7%  36.8 2403s\n",
      " 17476 13402  951.94650 1233  145 1132.10881  501.54948  55.7%  36.7 2436s\n",
      " 17690 13781  972.61975 1312  156 1132.10881  501.54948  55.7%  36.7 2472s\n",
      " 18122 14064  501.54948  118  207 1132.10881  501.54948  55.7%  37.0 2509s\n",
      " 18472 14632  667.24342  219  171 1132.10881  501.54948  55.7%  37.1 2546s\n",
      " 19093 15113  699.70642  392  188 1132.10881  501.54948  55.7%  36.8 2585s\n",
      " 19606 15361  829.81933  464  153 1132.10881  501.54948  55.7%  36.7 2674s\n",
      "H19669 14812                    1064.5015304  501.54948  52.9%  36.6 2675s\n",
      "H19862 14640                    1056.0701129  501.54948  52.5%  36.5 2767s\n",
      "H19863 14543                    1042.9696221  501.54948  51.9%  36.5 2767s\n",
      " 19871 14718  863.67434  529  148 1042.96962  501.54948  51.9%  36.5 2799s\n",
      " 20083 15271  902.70438  567  167 1042.96962  501.54948  51.9%  36.5 2837s\n",
      " 20713 15802  616.34196  130  173 1042.96962  501.54948  51.9%  36.2 3113s\n",
      " 21376 16336  658.96733  152  200 1042.96962  501.54948  51.9%  35.9 3134s\n",
      " 21938 16757  701.14970  288  189 1042.96962  501.54948  51.9%  35.8 3153s\n",
      " 22366 17205  860.06553  422  181 1042.96962  501.54948  51.9%  35.8 3173s\n",
      " 22900 17536  770.51243  209  154 1042.96962  501.54948  51.9%  35.6 3192s\n",
      " 23279 17832  949.00897  292  127 1042.96962  501.54948  51.9%  35.6 3212s\n",
      " 23613 17963  677.83226  202  184 1042.96962  501.54948  51.9%  35.5 3229s\n",
      " 23780 18290  679.17531  242  194 1042.96962  501.54948  51.9%  35.5 3250s\n",
      " 24111 18570  682.98071  289  208 1042.96962  501.54948  51.9%  35.5 3270s\n",
      " 24397 18836  799.36440  328  168 1042.96962  501.54948  51.9%  35.5 3290s\n",
      " 24678 19198  809.51001  357  203 1042.96962  501.54948  51.9%  35.5 3313s\n",
      " 25081 19598  824.24828  425  213 1042.96962  501.54948  51.9%  35.5 3338s\n",
      " 25536 20026  860.80634  530  212 1042.96962  501.54948  51.9%  35.5 3359s\n",
      " 26012 20346  886.82454  592  183 1042.96962  501.54948  51.9%  35.4 3378s\n",
      " 26483 20749  896.33377  647  178 1042.96962  501.54948  51.9%  35.4 3397s\n",
      " 26915 21062  920.18899  703  169 1042.96962  501.54948  51.9%  35.4 3417s\n",
      " 27247 21450  950.25180  762  185 1042.96962  501.54948  51.9%  35.5 3441s\n",
      " 27665 21544  973.95694  814  186 1042.96962  501.54948  51.9%  35.6 3462s\n",
      " 27849 21805  980.01365  849  199 1042.96962  501.54948  51.9%  35.7 3484s\n",
      " 28224 21944 1015.24222  915  193 1042.96962  501.54948  51.9%  35.8 3503s\n",
      " 28407 22323  502.13852   94  200 1042.96962  501.54948  51.9%  36.0 3526s\n",
      " 28834 22679  667.55638  224  186 1042.96962  501.54948  51.9%  36.2 3554s\n",
      " 29224 23048  670.34371  374  213 1042.96962  501.54948  51.9%  36.1 3574s\n",
      " 29641 23430  945.98030  519  141 1042.96962  501.54948  51.9%  36.1 3597s\n",
      " 30045 23845  952.89558  607  164 1042.96962  501.54948  51.9%  36.1 3618s\n",
      " 30494 24288  957.90568  725  141 1042.96962  501.54948  51.9%  36.0 3643s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      " 30961 24842  962.55711  810  156 1042.96962  501.54948  51.9%  35.8 3692s\n",
      "H31537 24625                    1027.6405982  501.54948  51.2%  35.7 3812s\n",
      " 31541 24772  967.20505  957  144 1027.64060  501.54948  51.2%  35.7 3840s\n",
      " 31721 25219  969.48556 1007  143 1027.64060  501.54948  51.2%  35.7 3876s\n",
      " 32210 25767  973.54132 1100  140 1027.64060  501.54948  51.2%  35.7 3911s\n",
      " 32821 26233  717.05143  239  137 1027.64060  501.54948  51.2%  35.6 3940s\n",
      " 33311 26672  640.22013  130  200 1027.64060  501.54948  51.2%  35.5 4032s\n",
      " 33847 27167  656.72417  231  170 1027.64060  501.54948  51.2%  35.5 4063s\n",
      " 34362 27616  759.91657  333  161 1027.64060  501.54948  51.2%  35.3 4093s\n",
      " 34816 27963  767.81646  436  164 1027.64060  501.54948  51.2%  35.3 4124s\n",
      " 35197 28382  775.23082  535  160 1027.64060  501.54948  51.2%  35.3 4150s\n",
      " 35715 28690  988.52567  247  141 1027.64060  501.54948  51.2%  35.3 4176s\n",
      " 36065 29065  937.52004  275  138 1027.64060  501.54948  51.2%  35.2 4202s\n",
      " 36618 29523  943.54184  440  135 1027.64060  501.54948  51.2%  35.1 4230s\n",
      " 37141 30101  680.64369  231  174 1027.64060  501.54948  51.2%  34.9 4254s\n",
      " 37762 30575  820.46036  404  167 1027.64060  501.54948  51.2%  34.7 4276s\n",
      "H38279 30655                    1013.4686456  501.54948  50.5%  34.6 4296s\n",
      "H38806 30403                    1008.9928865  501.54948  50.3%  34.5 4419s\n",
      "H38807 30310                    1006.4943528  501.54948  50.2%  34.5 4419s\n",
      " 38812 30402  884.45490  575  151 1006.49435  501.54948  50.2%  34.5 4439s\n",
      " 38913 30850  898.20231  611  160 1006.49435  501.54948  50.2%  34.5 4461s\n",
      " 39389 31269  943.52149  675  159 1006.49435  501.54948  50.2%  34.4 4485s\n",
      " 39857 31751  617.80557  136  181 1006.49435  501.54948  50.2%  34.4 4510s\n",
      " 40397 32339  501.54948  139  215 1006.49435  501.54948  50.2%  34.3 4534s\n",
      " 40996 32763  726.92411  312  195 1006.49435  501.54948  50.2%  34.2 4558s\n",
      " 41516 33419  821.85069  485  168 1006.49435  501.54948  50.2%  34.1 4604s\n",
      " 42208 33652  899.36281  658  154 1006.49435  501.54948  50.2%  33.9 4698s\n",
      " 42464 34192  951.29186  716  150 1006.49435  501.54948  50.2%  33.9 4727s\n",
      " 43039 34711  680.34185  189  179 1006.49435  501.54948  50.2%  33.8 4754s\n",
      " 43597 35101  757.90709  362  162 1006.49435  501.54948  50.2%  33.7 4777s\n",
      " 44046 35535  931.66269  477  155 1006.49435  501.54948  50.2%  33.6 4801s\n",
      " 44534 35894  945.53174  610  151 1006.49435  501.54948  50.2%  33.5 4822s\n",
      " 44923 36211  504.90962  144  210 1006.49435  501.54948  50.2%  33.4 4845s\n",
      " 45284 36470  666.25075  269  176 1006.49435  501.54948  50.2%  33.3 4869s\n",
      " 45550 36836  668.35046  335  181 1006.49435  501.54948  50.2%  33.3 4892s\n",
      " 45930 37187  673.50352  422  173 1006.49435  501.54948  50.2%  33.2 4917s\n",
      " 46295 37561  676.44054  495  163 1006.49435  501.54948  50.2%  33.1 4942s\n",
      " 46742 37899  665.53808  163  196 1006.49435  501.54948  50.2%  33.0 4962s\n",
      " 47123 38391  733.48722  251  150 1006.49435  501.54948  50.2%  33.0 5072s\n",
      " 47660 38669  849.52356  200  148 1006.49435  501.54948  50.2%  32.8 5092s\n",
      " 47954 39091  876.56693  318  130 1006.49435  501.54948  50.2%  32.8 5116s\n",
      " 48435 39591  916.44817  251  138 1006.49435  501.54948  50.2%  32.7 5142s\n",
      " 49016 40037  827.33611  232  146 1006.49435  501.54948  50.2%  32.6 5167s\n",
      " 49530 40459  503.37273  190  202 1006.49435  501.54948  50.2%  32.6 5184s\n",
      " 49977 40938  778.02016  362  171 1006.49435  501.54948  50.2%  32.6 5296s\n",
      "H50292 40919                    1005.9900137  501.54948  50.1%  32.6 5296s\n",
      " 50459 41250  812.61585  434  161 1005.99001  501.54948  50.1%  32.5 5315s\n",
      " 50791 41650  815.50166  528  187 1005.99001  501.54948  50.1%  32.4 5337s\n",
      " 51196 42021  981.52623  636  164 1005.99001  501.54948  50.1%  32.4 5361s\n",
      " 51637 42418  501.54948  178  209 1005.99001  501.54948  50.1%  32.5 5385s\n",
      " 52104 42869  684.22147  235  174 1005.99001  501.54948  50.1%  32.5 5430s\n",
      " 52713 43096  694.81361  309  166 1005.99001  501.54948  50.1%  32.7 5452s\n",
      " 52957 43348  695.13296  335  163 1005.99001  501.54948  50.1%  32.7 5478s\n",
      " 53220 43612  728.63904  371  150 1005.99001  501.54948  50.1%  32.8 5507s\n",
      " 53589 43956  856.01198  389  151 1005.99001  501.54948  50.1%  32.9 5540s\n",
      " 54075 44343  729.84085  415  175 1005.99001  501.54948  50.1%  33.1 5570s\n",
      "H54467 44424                    1005.3792595  501.54948  50.1%  33.4 5657s\n",
      "H54509 44341                    1003.5337370  501.54948  50.0%  33.4 5657s\n",
      "H54510 44324                    1003.1113897  501.54948  50.0%  33.4 5657s\n",
      " 54589 44595  891.03643  458  139 1003.11139  501.54948  50.0%  33.4 5683s\n",
      " 54916 45012  893.08968  509  137 1003.11139  501.54948  50.0%  33.7 5719s\n",
      " 55393 45500  894.58505  557  157 1003.11139  501.54948  50.0%  34.1 5755s\n",
      " 55983 46019 infeasible  609      1003.11139  501.54948  50.0%  34.2 5789s\n",
      " 56607 46443  907.71940  662  142 1003.11139  501.54948  50.0%  34.3 5815s\n",
      " 57222 46929  910.12033  718  141 1003.11139  501.54948  50.0%  34.4 5837s\n",
      " 57827 47401  912.49887  761  138 1003.11139  501.54948  50.0%  34.4 5861s\n",
      " 58404 47882     cutoff  810      1003.11139  501.54948  50.0%  34.5 5885s\n",
      " 59008 48343  918.94044  880  144 1003.11139  501.54948  50.0%  34.5 5908s\n",
      " 59640 48704     cutoff  938      1003.11139  501.54948  50.0%  34.6 5932s\n",
      " 60122 49167     cutoff 1009      1003.11139  501.54948  50.0%  34.6 5956s\n",
      " 60704 49611  655.62718  202  200 1003.11139  501.54948  50.0%  34.6 5979s\n",
      " 61176 50192 infeasible  320      1003.11139  501.54948  50.0%  34.6 6001s\n",
      " 61809 50614  765.28602  341  175 1003.11139  501.54948  50.0%  34.5 6024s\n",
      " 62280 51112     cutoff  436      1003.11139  501.54948  50.0%  34.5 6047s\n",
      " 62882 51246     cutoff  540      1003.11139  501.54948  50.0%  34.4 6195s\n",
      "H62921 51074                     998.6460262  501.54948  49.8%  34.4 6195s\n",
      " 63042 51695  785.66115  577  170  998.64603  501.54948  49.8%  34.4 6233s\n",
      " 63734 52022  746.60643  289  179  998.64603  501.54948  49.8%  34.4 6326s\n",
      "H64065 51995                     997.9437110  501.54948  49.7%  34.3 6326s\n",
      " 64066 52586  759.43078  427  176  997.94371  501.54948  49.7%  34.3 6353s\n",
      " 64685 53108  806.39723  600  186  997.94371  501.54948  49.7%  34.2 6378s\n",
      " 65268 53646  826.88368  756  178  997.94371  501.54948  49.7%  34.2 6405s\n",
      " 65823 54131  888.43692  871  176  997.94371  501.54948  49.7%  34.2 6433s\n",
      " 66309 54494  917.66506  959  164  997.94371  501.54948  49.7%  34.1 6459s\n",
      " 66734 55034  604.02222  103  179  997.94371  501.54948  49.7%  34.0 6481s\n",
      " 67310 55462  614.54336  212  174  997.94371  501.54948  49.7%  34.0 6503s\n",
      " 67802 55906  911.42066  314  144  997.94371  501.54948  49.7%  34.0 6522s\n",
      " 68372 56462  501.54948   95  201  997.94371  501.54948  49.7%  33.9 6543s\n",
      " 69039 57010  748.33415  154  146  997.94371  501.54948  49.7%  33.8 6562s\n",
      " 69700 57628  681.89099  199  192  997.94371  501.54948  49.7%  33.8 6580s\n",
      " 70342 58250  905.82923  351  166  997.94371  501.54948  49.7%  33.7 6598s\n",
      " 70981 58569  907.07103  524  162  997.94371  501.54948  49.7%  33.6 6618s\n",
      " 71326 58620  669.75416  152  169  997.94371  501.54948  49.7%  33.5 6631s\n",
      " 71396 58701  694.48778  162  166  997.94371  501.54948  49.7%  33.5 6641s\n",
      " 71502 58740  695.01524  173  171  997.94371  501.54948  49.7%  33.5 6651s\n",
      " 71548 58767  695.52032  184  162  997.94371  501.54948  49.7%  33.5 6665s\n",
      " 71588 58817  697.09583  192  166  997.94371  501.54948  49.7%  33.5 6798s\n",
      "H71603 58705                     995.9588519  501.54948  49.6%  33.5 6798s\n",
      " 71653 58987  699.91071  207  170  995.95885  501.54948  49.6%  33.5 6818s\n",
      " 72094 59274  663.39001  164  196  995.95885  501.54948  49.6%  33.5 6838s\n",
      " 72505 59740  689.11553  246  190  995.95885  501.54948  49.6%  33.5 6858s\n",
      " 72986 60001  722.41438  342  191  995.95885  501.54948  49.6%  33.5 6876s\n",
      " 73260 60574  762.55873  412  183  995.95885  501.54948  49.6%  33.6 6918s\n",
      " 73886 60880  834.07570  561  180  995.95885  501.54948  49.6%  33.5 6937s\n",
      " 74194 61327  893.48561  617  173  995.95885  501.54948  49.6%  33.5 6960s\n",
      " 74660 61717  917.18014  707  165  995.95885  501.54948  49.6%  33.5 6984s\n",
      " 75065 62074  932.19320  790  167  995.95885  501.54948  49.6%  33.5 7009s\n",
      " 75484 62076 infeasible  864       995.95885  501.54948  49.6%  33.6 7200s\n",
      "H75485 62006                     994.3885671  501.54948  49.6%  33.6 7200s\n",
      "H75486 62005                     994.3326573  501.54948  49.6%  33.6 7200s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H75487 61773                     990.8112212  501.54948  49.4%  33.6 7200s\n",
      "\n",
      "Cutting planes:\n",
      "  Gomory: 20\n",
      "  MIR: 57\n",
      "  Flow cover: 46\n",
      "  Zero half: 8\n",
      "  RLT: 2\n",
      "\n",
      "Explored 75489 nodes (2540902 simplex iterations) in 7200.67 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 10: 990.811 994.333 994.389 ... 1005.99\n",
      "\n",
      "Time limit reached\n",
      "Best objective 9.908112212483e+02, best bound 5.015494753705e+02, gap 49.3799%\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(4)\n",
    "num_veh = 9\n",
    "vehicles = [i for i in range(1, num_veh + 1)]\n",
    "veh_depot = np.random.randint(0, num_depot, size=num_veh)\n",
    "veh_capacity = [400 for i in range(num_veh)]\n",
    "\n",
    "time_limit = 7200\n",
    "routes, veh_routes, node_routes, X, Y, depot, obj_val =  MDCVRP(cust_x, cust_y, cust_demand, depot_x, \n",
    "                                                                depot_y, vehicles, veh_depot, veh_capacity, time_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "675137be",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "Overall objective funtion value - 991\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('-----\\nOverall objective funtion value -', obj_val)\n",
    "\n",
    "X, Y = np.array(X), np.array(Y)\n",
    "plt.scatter(X[depot],Y[depot],color='red', marker='D', label='Depot')\n",
    "\n",
    "for r in range(len(routes)):\n",
    "    color = colors[node_routes[r]]\n",
    "    for n in range(len(routes[r])-1):\n",
    "        i=routes[r][n]\n",
    "        j=routes[r][n+1]\n",
    "        plt.plot([X[i],X[j]],[Y[i],Y[j]], color=color, marker='.')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Network Graph of Multi-Depot Vehicle Routing')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33efdd83",
   "metadata": {},
   "source": [
    "____\n",
    "### Multi-Depot Constrianed Vehicle Routing Problem with Bin Packing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6afe292",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra vehicles required \n",
    "no_extra = 2\n",
    "nos = []\n",
    "for i in range(len(x)):\n",
    "    nos.append(apx2[i])\n",
    "veh_capacities = []\n",
    "for i, n in enumerate(nos):\n",
    "    if n>0:\n",
    "        for t in range(int(n+no_extra)):\n",
    "            veh_capacities.append(A[i])\n",
    "            \n",
    "#veh_capacities\n",
    "vehicles = [i+1 for i in range(len(veh_capacities))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d82ecf76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changed value of parameter timelimit to 7200.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 180519 rows, 182204 columns and 1418208 nonzeros\n",
      "Model fingerprint: 0xcdb70511\n",
      "Variable types: 100 continuous, 182104 integer (182104 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+02]\n",
      "  Objective range  [1e+00, 1e+02]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 10200 rows and 10200 columns\n",
      "Presolve time: 1.39s\n",
      "Presolved: 170319 rows, 172004 columns, 1362108 nonzeros\n",
      "Variable types: 100 continuous, 171904 integer (171904 binary)\n",
      "\n",
      "Deterministic concurrent LP optimizer: primal and dual simplex\n",
      "Showing first log only...\n",
      "\n",
      "Concurrent spin time: 0.00s\n",
      "\n",
      "Solved with dual simplex\n",
      "\n",
      "Root relaxation: objective 4.926287e+02, 387 iterations, 0.61 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  492.62871    0  200          -  492.62871      -     -    4s\n",
      "     0     0  492.62871    0  201          -  492.62871      -     -    6s\n",
      "     0     0  492.62871    0  201          -  492.62871      -     -    8s\n",
      "     0     0  492.62871    0  209          -  492.62871      -     -   10s\n",
      "     0     0  492.62871    0  209          -  492.62871      -     -   11s\n",
      "     0     0  492.62871    0  210          -  492.62871      -     -   14s\n",
      "     0     0  492.62871    0  210          -  492.62871      -     -   15s\n",
      "     0     0  492.62871    0  199          -  492.62871      -     -   19s\n",
      "     0     0  492.62871    0  199          -  492.62871      -     -   73s\n",
      "     0     0  492.62871    0  204          -  492.62871      -     -   76s\n",
      "     0     0  492.62871    0  204          -  492.62871      -     -   81s\n",
      "H    0     0                    8459.0179467  492.62871  94.2%     -   88s\n",
      "H    0     0                    3772.1762387  492.62871  86.9%     -  128s\n",
      "     0     2  492.62871    0  204 3772.17624  492.62871  86.9%     -  131s\n",
      "    11    15  492.62871    4  214 3772.17624  492.62871  86.9%   108  135s\n",
      "H   27    31                    3295.8971606  492.62871  85.1%  47.2  137s\n",
      "H   29    31                    3060.5796096  492.62871  83.9%  44.0  137s\n",
      "    53    60  492.62871   14  217 3060.57961  492.62871  83.9%  25.2  140s\n",
      "H   60    64                    2966.8572297  492.62871  83.4%  23.5  202s\n",
      "    86    96  492.62871   23  219 2966.85723  492.62871  83.4%  18.3  205s\n",
      "   133   150  492.62871   37  214 2966.85723  492.62871  83.4%  13.3  210s\n",
      "   174   197  492.62871   45  214 2966.85723  492.62871  83.4%  10.9  216s\n",
      "   222   244  516.15178   59  214 2966.85723  492.62871  83.4%  11.3  223s\n",
      "   248   281  516.15178   65  214 2966.85723  492.62871  83.4%  11.8  227s\n",
      "   285   311  516.22939   74  211 2966.85723  492.62871  83.4%  11.2  231s\n",
      "   318   375  530.60203   78  210 2966.85723  492.62871  83.4%  11.3  236s\n",
      "   388   416  529.76766  100  215 2966.85723  492.62871  83.4%  11.2  241s\n",
      "H  442   419                    2511.6161008  492.62871  80.4%  12.3  297s\n",
      "   446   454  544.95092  112  204 2511.61610  492.62871  80.4%  12.4  303s\n",
      "   492   485  554.96345  122  195 2511.61610  492.62871  80.4%  12.9  308s\n",
      "   540   521  562.96567  132  193 2511.61610  492.62871  80.4%  13.9  313s\n",
      "   587   554  580.51393  148  193 2511.61610  492.62871  80.4%  13.9  317s\n",
      "   627   597  584.70782  157  184 2511.61610  492.62871  80.4%  14.5  323s\n",
      "   683   632  597.70155  164  177 2511.61610  492.62871  80.4%  14.8  328s\n",
      "   745   669  611.25039  173  174 2511.61610  492.62871  80.4%  14.8  333s\n",
      "   798   705  614.10352  187  166 2511.61610  492.62871  80.4%  14.9  339s\n",
      "   851   736  621.12578  195  162 2511.61610  492.62871  80.4%  15.1  347s\n",
      "   909   781  638.09057  209  162 2511.61610  492.62871  80.4%  15.2  353s\n",
      "   979   821  645.34700  221  154 2511.61610  492.62871  80.4%  15.3  360s\n",
      "  1047   857  670.20404  243  146 2511.61610  492.62871  80.4%  15.5  366s\n",
      "  1091   891  684.09333  249  144 2511.61610  492.62871  80.4%  15.8  372s\n",
      "  1152   926  684.76989  252  131 2511.61610  492.62871  80.4%  15.9  379s\n",
      "  1213   976  687.61537  260  126 2511.61610  492.62871  80.4%  16.2  386s\n",
      "  1278  1024  711.58492  275  128 2511.61610  492.62871  80.4%  16.3  393s\n",
      "  1364  1076  727.01406  287  125 2511.61610  492.62871  80.4%  16.2  401s\n",
      "  1469  1127  739.69342  302  127 2511.61610  492.62871  80.4%  15.8  410s\n",
      "  1573  1172 infeasible  328      2511.61610  492.62871  80.4%  15.6  418s\n",
      "  1651  1221  808.08285  346  126 2511.61610  492.62871  80.4%  15.9  427s\n",
      "  1746  1283  819.87162  359  115 2511.61610  492.62871  80.4%  15.7  437s\n",
      "  1850  1342  828.08315  386  125 2511.61610  492.62871  80.4%  15.5  447s\n",
      "  1935  1412  864.68710  417  118 2511.61610  492.62871  80.4%  15.6  494s\n",
      "H 1975  1412                    2497.6234537  492.62871  80.3%  15.4  494s\n",
      "  2044  1479  910.67829  458  124 2497.62345  492.62871  80.3%  15.4  505s\n",
      "  2157  1556  933.41280  499  122 2497.62345  492.62871  80.3%  15.3  517s\n",
      "  2280  1607  985.04875  542  114 2497.62345  492.62871  80.3%  15.5  528s\n",
      "  2371  1649 1053.27074  561  103 2497.62345  492.62871  80.3%  15.5  541s\n",
      "  2487  1709 1058.94991  574  104 2497.62345  492.62871  80.3%  15.4  555s\n",
      "  2611  1803 1064.45074  588  101 2497.62345  492.62871  80.3%  15.4  570s\n",
      "  2730  1899 1074.64566  603  105 2497.62345  492.62871  80.3%  15.5  584s\n",
      "  2884  1945 1075.59821  610  109 2497.62345  492.62871  80.3%  15.3  675s\n",
      "H 2889  1945                    2479.8859738  492.62871  80.1%  15.3  675s\n",
      "H 2914  1945                    2459.1994969  492.62871  80.0%  15.4  675s\n",
      "H 2939  1945                    2420.8655471  492.62871  79.7%  15.3  675s\n",
      "H 2970  1945                    2397.6100806  492.62871  79.5%  15.2  675s\n",
      "  2971  2035 1080.47751  616  107 2397.61008  492.62871  79.5%  15.2  691s\n",
      "  3095  2141 1137.31310  642  106 2397.61008  492.62871  79.5%  15.5  708s\n",
      "  3226  2222 1144.71056  673  111 2397.61008  492.62871  79.5%  15.6  726s\n",
      "  3398  2315 1160.20918  688   99 2397.61008  492.62871  79.5%  15.3  744s\n",
      "  3534  2411 1163.53633  710   92 2397.61008  492.62871  79.5%  15.5  763s\n",
      "  3693  2514 1212.94041  750  104 2397.61008  492.62871  79.5%  15.7  781s\n",
      "  3829  2626 1230.06435  788  103 2397.61008  492.62871  79.5%  15.8  801s\n",
      "  3989  2738 1254.98859  856  105 2397.61008  492.62871  79.5%  16.0  821s\n",
      "  4136  2847 1279.37269  907  102 2397.61008  492.62871  79.5%  16.2  844s\n",
      "  4302  2965  492.77830   31  212 2397.61008  492.62871  79.5%  16.3  866s\n",
      "  4433  3073  526.23693   63  182 2397.61008  492.62871  79.5%  16.5  889s\n",
      "  4573  3191  554.37103   93  175 2397.61008  492.62871  79.5%  16.9  912s\n",
      "  4725  3329  579.32763  116  165 2397.61008  492.62871  79.5%  17.1  938s\n",
      "H 4908  3332                    2365.8617451  492.62871  79.2%  17.3 1014s\n",
      "H 4910  3332                    2290.6748406  492.62871  78.5%  17.3 1014s\n",
      "H 4912  3489                    2284.8118891  492.62871  78.4%  17.3 1039s\n",
      "H 4974  3489                    2265.6075208  492.62871  78.3%  17.3 1039s\n",
      "  5099  3684  588.42783  181  165 2265.60752  492.62871  78.3%  17.4 1066s\n",
      "H 5324  3684                    2259.7445692  492.62871  78.2%  17.4 1067s\n",
      "  5325  3685  589.30531  142  204 2259.74457  492.62871  78.2%  17.4 1080s\n",
      "H 5326  3501                    2204.2041198  492.62871  77.7%  17.4 1086s\n",
      "H 5327  3326                    2064.8546773  492.62871  76.1%  17.4 1092s\n",
      "H 5327  3160                    2051.5601850  492.62871  76.0%  17.4 1107s\n",
      "  5329  3162  516.23814   41  205 2051.56019  492.62871  76.0%  17.3 1112s\n",
      "H 5329  3004                    2003.5546054  492.62871  75.4%  17.3 1175s\n",
      "  5332  3006  989.17534  530  203 2003.55461  522.36790  73.9%  17.3 1239s\n",
      "H 5332  2855                    1966.1845612  522.36790  73.4%  17.3 1241s\n",
      "  5334  2856 1280.96003  889  217 1966.18456  522.36790  73.4%  17.3 1283s\n",
      "  5335  2857  522.36790   12  218 1966.18456  522.36790  73.4%  17.3 1287s\n",
      "H 5335  2714                    1877.8598609  522.36790  72.2%  17.3 1389s\n",
      "  5337  2715  537.47023   75  213 1877.85986  522.36790  72.2%  17.3 1392s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H 5337  2579                    1848.5865433  522.36790  71.7%  17.3 1429s\n",
      "  5338  2580  789.61667  319  204 1848.58654  522.36790  71.7%  17.3 1430s\n",
      "  5340  2581  552.15070   97  209 1848.58654  522.36790  71.7%  17.3 1438s\n",
      "  5341  2582  522.36790   23  211 1848.58654  522.36790  71.7%  17.3 1441s\n",
      "  5342  2582 1016.01659  939  219 1848.58654  522.36790  71.7%  17.3 1448s\n",
      "  5343  2583  587.86516  120  239 1848.58654  522.36790  71.7%  17.3 1451s\n",
      "  5344  2584  618.45006  186  202 1848.58654  522.36790  71.7%  17.3 1456s\n",
      "  5345  2584  606.46334  164  202 1848.58654  522.36790  71.7%  17.3 1460s\n",
      "  5346  2585  659.42205  209  200 1848.58654  522.36790  71.7%  17.3 1466s\n",
      "  5348  2586  749.62792  289  199 1848.58654  522.36790  71.7%  17.3 1474s\n",
      "  5349  2587  771.54289  280  212 1848.58654  522.36790  71.7%  17.3 1477s\n",
      "  5350  2588 1094.03020 1218  203 1848.58654  522.36790  71.7%  17.3 1483s\n",
      "  5351  2588 1335.56731  983  203 1848.58654  522.36790  71.7%  17.3 1488s\n",
      "  5352  2589  555.81236  101  202 1848.58654  522.36790  71.7%  17.3 1493s\n",
      "  5353  2590  587.30289  119  197 1848.58654  522.36790  71.7%  17.3 1496s\n",
      "H 5356  2463                    1831.5175970  522.36790  71.5%  22.6 1504s\n",
      "  5357  2463 1247.43836  827  202 1831.51760  522.36790  71.5%  22.6 1505s\n",
      "H 5357  2340                    1816.6864562  522.36790  71.2%  22.6 1511s\n",
      "H 5357  2223                    1776.4134585  522.36790  70.6%  22.6 1511s\n",
      "H 5357  2111                    1762.1815154  522.36790  70.4%  22.6 1537s\n",
      "H 5358  2007                    1745.5748489  522.36790  70.1%  22.6 1537s\n",
      "H 5359  1907                    1734.9915238  522.36790  69.9%  22.6 1542s\n",
      "H 5359  1812                    1734.9624363  522.36790  69.9%  22.6 1564s\n",
      "  5361  1813  522.49795   70  224 1734.96244  522.36790  69.9%  22.6 1567s\n",
      "  5362  1814 1313.52287  916  230 1734.96244  522.36790  69.9%  22.6 1575s\n",
      "  5364  1815  682.52083  234  212 1734.96244  522.36790  69.9%  22.6 1590s\n",
      "  5366  1816  611.98026  183  214 1734.96244  522.36790  69.9%  22.6 1599s\n",
      "  5367  1817  595.65304  153  240 1734.96244  522.36790  69.9%  22.6 1602s\n",
      "  5368  1818  754.19636  256  210 1734.96244  522.36790  69.9%  22.5 1609s\n",
      "H 5368  1727                    1626.0518930  522.36790  67.9%  22.5 1612s\n",
      "  5370  1728  817.48132  368  209 1626.05189  522.36790  67.9%  22.5 1622s\n",
      "  5371  1729  522.36790    6  214 1626.05189  522.36790  67.9%  22.5 1625s\n",
      "  5372  1729  592.00695  166  221 1626.05189  522.36790  67.9%  22.5 1632s\n",
      "  5373  1730 1142.70308  659  213 1626.05189  522.42141  67.9%  22.5 1635s\n",
      "  5374  1731  906.38286  460  197 1626.05189  529.78794  67.4%  22.5 1644s\n",
      "  5375  1731 1062.33811 1062  197 1626.05189  529.78794  67.4%  22.5 1647s\n",
      "H 5375  1644                    1621.6575621  529.78794  67.3%  22.5 1655s\n",
      "  5377  1646  977.56652  486  202 1621.65756  529.78794  67.3%  22.5 1660s\n",
      "H 5377  1563                    1613.2734560  529.81575  67.2%  22.5 1771s\n",
      "  5380  1565 1321.79480  977  238 1613.27346  534.58386  66.9%  22.5 1782s\n",
      "  5381  1565 1199.72406  827  263 1613.27346  536.00326  66.8%  22.5 1786s\n",
      "  5382  1566  831.44283  488  241 1613.27346  537.46108  66.7%  22.5 1793s\n",
      "H 5382  1487                    1544.0514213  537.46108  65.2%  22.5 1794s\n",
      "  5383  1488  638.64638  202  269 1544.05142  538.10013  65.2%  22.5 1799s\n",
      "  5384  1488  648.68599  207  255 1544.05142  538.29800  65.1%  22.5 1840s\n",
      "H 5384  1413                    1534.1903646  538.29800  64.9%  22.5 1840s\n",
      "  5386  1415 1088.37530 1198  265 1534.19036  540.03100  64.8%  22.5 1852s\n",
      "  5387  1415 1079.07277  672  273 1534.19036  540.25042  64.8%  22.5 1865s\n",
      "  5388  1416 1072.82258  598  262 1534.19036  541.15915  64.7%  22.5 1915s\n",
      "  5389  1417  989.69081  497  303 1534.19036  541.64818  64.7%  22.5 1921s\n",
      "  5390  1417 1139.18994  770  277 1534.19036  544.20113  64.5%  22.5 1926s\n",
      "  5391  1418  545.29212   84  289 1534.19036  545.29212  64.5%  22.5 1930s\n",
      "H 5391  1347                    1529.0254841  546.77094  64.2%  22.5 1938s\n",
      "  5393  1348  848.36236  383  311 1529.02548  548.07549  64.2%  22.4 1943s\n",
      "  5394  1349  549.19018   54  300 1529.02548  549.19018  64.1%  22.4 1967s\n",
      "  5395  1350  793.09488  323  331 1529.02548  550.13634  64.0%  22.4 1971s\n",
      "H 5395  1282                    1506.4938081  550.13634  63.5%  22.4 1978s\n",
      "H 5395  1217                    1505.8343343  551.14448  63.4%  22.4 2004s\n",
      "  5397  1218 1169.08187  705  350 1505.83433  551.33489  63.4%  22.4 2005s\n",
      "H 5398  1157                    1504.5770207  551.42366  63.4%  22.4 2006s\n",
      "  5403  1160 1031.77463  987  399 1504.57702  552.14092  63.3%  22.4 2012s\n",
      "  5404  1161  800.98159  339  369 1504.57702  553.36479  63.2%  22.4 2019s\n",
      "  5405  1161  992.13907  811  379 1504.57702  553.65855  63.2%  22.4 2020s\n",
      "  5408  1163  556.55879   23  390 1504.57702  556.55879  63.0%  22.4 2026s\n",
      "  5409  1164 1162.19184  771  369 1504.57702  558.10686  62.9%  22.4 2092s\n",
      "  5413  1167  614.24001  186  455 1504.57702  558.38421  62.9%  22.4 2095s\n",
      "  5415  1168  559.17322  104  420 1504.57702  559.17322  62.8%  22.4 2107s\n",
      "  5419  1171  611.91018  188  460 1504.57702  559.74926  62.8%  22.3 2110s\n",
      "  5421  1172  872.55955  444  459 1504.57702  559.80272  62.8%  22.3 2115s\n",
      "  5422  1173 1234.98496  796  430 1504.57702  559.86422  62.8%  22.3 2152s\n",
      "H 5422  1113                    1490.0792801  559.86422  62.4%  22.3 2152s\n",
      "  5423  1113  577.33887  137  461 1490.07928  559.86422  62.4%  22.3 2156s\n",
      "  5424  1114 1287.58859  890  416 1490.07928  559.94582  62.4%  22.3 2164s\n",
      "  5425  1115  589.30531  142  447 1490.07928  560.03575  62.4%  22.3 2165s\n",
      "  5429  1117  560.27805   41  467 1490.07928  560.27805  62.4%  22.3 2172s\n",
      "  5430  1118 1028.76073  929  453 1490.07928  560.40926  62.4%  22.3 2205s\n",
      "  5431  1119 1230.18489  790  473 1490.07928  560.40926  62.4%  22.3 2210s\n",
      "  5432  1119  989.17534  530  450 1490.07928  560.86262  62.4%  22.3 2217s\n",
      "  5435  1121  561.00481   12  461 1490.07928  561.00481  62.4%  22.3 2223s\n",
      "  5436  1122  952.40386  696  424 1490.07928  561.45950  62.3%  22.3 2232s\n",
      "  5440  1125  561.63450   97  476 1490.07928  561.63450  62.3%  22.2 2235s\n",
      "  5442  1126 1016.01659  939  435 1490.07928  561.67745  62.3%  22.2 2248s\n",
      "  5443  1127  587.86516  120  469 1490.07928  561.67745  62.3%  22.2 2252s\n",
      "  5444  1127  618.45006  186  444 1490.07928  561.67745  62.3%  22.2 2260s\n",
      "  5450  1131 1094.03020 1218  510 1490.07928  561.75750  62.3%  22.2 2265s\n",
      "  5452  1133  561.81152  101  476 1490.07928  561.81152  62.3%  22.2 2277s\n",
      "H 5455  1077                    1488.0222235  561.82135  62.2%  22.2 2280s\n",
      "  5457  1078 1247.43836  827  461 1488.02222  561.83979  62.2%  22.2 2290s\n",
      "H 5462  1025                    1425.7347584  562.31760  60.6%  22.2 2298s\n",
      "  5464  1027  682.52083  234  467 1425.73476  562.33582  60.6%  22.2 2307s\n",
      "  5468  1029  754.19636  256  479 1425.73476  562.37125  60.6%  22.1 2315s\n",
      "H 5468   976                    1425.3996003  562.43291  60.5%  22.1 2325s\n",
      "  5474   980  906.38286  460  533 1425.39960  562.48185  60.5%  22.1 2333s\n",
      "  5475   981 1062.33811 1062  476 1425.39960  562.48466  60.5%  22.1 2365s\n",
      "  5476   982  589.12319  175  505 1425.39960  562.48466  60.5%  22.1 2370s\n",
      "  5477   982  977.56652  486  490 1425.39960  562.50175  60.5%  22.1 2377s\n",
      "  5478   983  562.50175   73  492 1425.39960  562.50175  60.5%  22.1 2382s\n",
      "  5479   984 1229.00696  868  485 1425.39960  562.51664  60.5%  22.1 2410s\n",
      "  5481   985 1199.72406  827  494 1425.39960  562.52293  60.5%  22.1 2416s\n",
      "  5482   986  831.44283  488  477 1425.39960  562.52293  60.5%  22.1 2457s\n",
      "H 5482   934                    1421.8568622  562.52293  60.4%  22.1 2458s\n",
      "H 5482   885                    1419.0969216  562.52293  60.4%  22.1 2458s\n",
      "H 5482   839                    1402.1102111  562.52293  59.9%  22.1 2462s\n",
      "H 5482   795                    1398.4769453  562.52293  59.8%  22.1 2462s\n",
      "H 5483   753                    1375.6386147  562.52293  59.1%  22.1 2675s\n",
      "  5485   755  629.51177  250  499 1375.63861  562.52293  59.1%  22.1 2680s\n",
      "  5486   755 1088.37530 1198  481 1375.63861  562.52293  59.1%  22.1 2689s\n",
      "  5487   756 1079.07277  672  477 1375.63861  562.52293  59.1%  22.1 2693s\n",
      "  5489   759  989.69081  497  204 1375.63861  562.52293  59.1%  38.3 2697s\n",
      "H 5490   722                    1373.8953127  562.52293  59.1%  38.3 2702s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H 5490   685                    1368.9676995  562.52293  58.9%  38.3 2703s\n",
      "H 5491   651                    1336.4439442  562.52293  57.9%  38.3 2733s\n",
      "H 5492   619                    1324.7049865  562.52293  57.5%  38.3 2733s\n",
      "  5493   620  848.36236  383  211 1324.70499  562.52293  57.5%  38.2 2735s\n",
      "H 5493   589                    1324.6725750  562.52293  57.5%  38.2 2738s\n",
      "  5494   589  562.52293   54  207 1324.67258  562.52293  57.5%  38.2 2880s\n",
      "H 5494   560                    1319.6574312  562.52293  57.4%  38.2 2881s\n",
      "H 5494   532                    1315.1594152  562.52293  57.2%  38.2 2881s\n",
      "  5496   534  591.73457  165  220 1315.15942  562.52293  57.2%  38.2 2889s\n",
      "  5497   534 1169.08187  705  243 1315.15942  562.52293  57.2%  38.2 2893s\n",
      "  5498   535 1046.73044  993  254 1315.15942  562.52293  57.2%  38.2 2899s\n",
      "  5499   536  581.11472  115  251 1315.15942  562.52293  57.2%  38.2 2904s\n",
      "H 5499   509                    1308.0103619  562.52293  57.0%  38.2 2934s\n",
      "  5501   510  663.00292  170  299 1308.01036  562.52293  57.0%  38.2 2939s\n",
      "  5502   511  611.81286  182  319 1308.01036  562.52293  57.0%  38.2 2947s\n",
      "  5503   511 1031.77463  987  347 1308.01036  562.52293  57.0%  38.2 2952s\n",
      "  5504   512  800.98159  339  327 1308.01036  562.52293  57.0%  38.2 2959s\n",
      "  5505   513  992.13907  811  333 1308.01036  562.52293  57.0%  38.2 2963s\n",
      "  5506   513  818.33669  367  329 1308.01036  562.52293  57.0%  38.2 2972s\n",
      "  5507   514  760.51558  263  336 1308.01036  562.52293  57.0%  38.2 2976s\n",
      "  5508   515  562.52293   23  332 1308.01036  562.52293  57.0%  38.1 2982s\n",
      "  5509   515 1162.19184  771  376 1308.01036  562.52293  57.0%  38.1 2987s\n",
      "  5510   516 1014.86114  943  349 1308.01036  562.52293  57.0%  38.1 2995s\n",
      "  5511   517  818.15719  323  380 1308.01036  562.52293  57.0%  38.1 3000s\n",
      "  5512   517 1196.92188  822  352 1308.01036  562.98838  57.0%  38.1 3043s\n",
      "  5513   518  614.24001  186  383 1308.01036  563.23699  56.9%  38.1 3047s\n",
      "  5514   519  920.03607  605  386 1308.01036  563.68172  56.9%  38.1 3056s\n",
      "H 5517   494                    1297.0785199  564.60356  56.5%  38.1 3060s\n",
      "  5521   496  872.55955  444  450 1297.07852  566.74132  56.3%  38.1 3066s\n",
      "  5524   498 1287.58859  890  449 1297.07852  566.82334  56.3%  38.0 3072s\n",
      "  5525   499  589.30531  142  451 1297.07852  566.89272  56.3%  38.0 3082s\n",
      "  5527   500  567.22759   91  462 1297.07852  567.22759  56.3%  38.0 3085s\n",
      "  5529   502  567.23929   41  465 1297.07852  567.23929  56.3%  38.0 3090s\n",
      "H 5529   476                    1294.1456716  567.23929  56.2%  38.0 3098s\n",
      "  5531   477 1230.18489  790  463 1294.14567  567.48459  56.1%  38.0 3100s\n",
      "  5535   480  567.52228   12  512 1294.14567  567.52228  56.1%  38.0 3108s\n",
      "H 5535   456                    1285.1574079  568.38619  55.8%  38.0 3187s\n",
      "  5537   457  568.38619   75  497 1285.15741  568.38619  55.8%  37.9 3194s\n",
      "  5538   458  789.61667  319  506 1285.15741  568.61069  55.8%  37.9 3204s\n",
      "  5539   458  588.21778  160  515 1285.15741  568.62837  55.8%  37.9 3205s\n",
      "  5546   463  659.42205  209  526 1285.15741  568.72594  55.7%  37.9 3210s\n",
      "  5547   464  568.72664  118  531 1285.15741  568.72664  55.7%  37.9 3216s\n",
      "  5548   464  749.62792  289  487 1285.15741  568.76949  55.7%  37.9 3254s\n",
      "  5549   465  771.54289  280  508 1285.15741  568.76949  55.7%  37.9 3258s\n",
      "H 5549   440                    1284.6192442  569.02485  55.7%  37.9 3266s\n",
      "  5556   445  992.63894  814  530 1284.61924  569.21822  55.7%  37.8 3270s\n",
      "  5563   449  645.11874  204  539 1284.61924  569.28812  55.7%  37.8 3277s\n",
      "  5564   450  682.52083  234  505 1284.61924  569.30216  55.7%  37.8 3286s\n",
      "  5568   453  754.19636  256  569 1284.61924  569.47340  55.7%  37.7 3290s\n",
      "  5570   454  817.48132  368  564 1284.61924  569.47377  55.7%  37.7 3296s\n",
      "H 5570   430                    1280.2919405  569.47377  55.5%  37.7 3303s\n",
      "H 5570   407                    1278.2869495  569.50923  55.4%  37.7 3306s\n",
      "  5577   412  977.56652  486  554 1278.28695  569.58923  55.4%  37.7 3314s\n",
      "  5578   412  569.64018   73  542 1278.28695  569.64018  55.4%  37.7 3321s\n",
      "  5579   413 1229.00696  868  555 1278.28695  569.64018  55.4%  37.7 3326s\n",
      "H 5579   391                    1277.4481272  569.73647  55.4%  37.7 3355s\n",
      "  5585   395  629.51177  250  576 1277.44813  569.81620  55.4%  37.6 3360s\n",
      "H 5587   375                    1276.8239229  569.85222  55.4%  37.6 3411s\n",
      "  5589   377  989.69081  497  533 1276.82392  569.85222  55.4%  37.6 3415s\n",
      "  5590   377 1139.18994  770  527 1276.82392  569.97213  55.4%  37.6 3423s\n",
      "  5592   379 1072.10072  597  579 1276.82392  570.01266  55.4%  37.6 3425s\n",
      "  5595   381  793.09488  323  570 1276.82392  570.06286  55.4%  37.6 3431s\n",
      "  5596   381  591.73457  165  520 1276.82392  570.09750  55.4%  37.5 3439s\n",
      "  5598   383 1046.73044  993  564 1276.82392  570.11455  55.3%  37.5 3440s\n",
      "  5600   384 1019.15687  532  586 1276.82392  570.11821  55.3%  37.5 3445s\n",
      "  5601   385  663.00292  170  552 1276.82392  570.11846  55.3%  37.5 3455s\n",
      "  5606   388  818.33669  367  554 1276.82392  570.14196  55.3%  37.5 3464s\n",
      "  5607   389  760.51558  263  511 1276.82392  570.17433  55.3%  37.5 3477s\n",
      "  5610   391 1014.86114  943  556 1276.82392  570.23860  55.3%  37.4 3480s\n",
      "  5614   393  920.03607  605  579 1276.82392  570.31531  55.3%  37.4 3486s\n",
      "  5615   394  570.35278  104  547 1276.82392  570.35278  55.3%  37.4 3494s\n",
      "  5616   395  611.25638  167  569 1276.82392  570.37961  55.3%  37.4 3495s\n",
      "  5618   396  591.10800  158  584 1276.82392  570.38876  55.3%  37.4 3500s\n",
      "  5619   397  611.91018  188  560 1276.82392  570.40293  55.3%  37.4 3508s\n",
      "  5621   398  872.55955  444  585 1276.82392  570.42358  55.3%  37.4 3514s\n",
      "  5622   399 1234.98496  796  551 1276.82392  570.43402  55.3%  37.4 3523s\n",
      "  5624   400 1276.82392  890  587 1276.82392  570.45311  55.3%  37.4 3525s\n",
      "  5629   403  570.54661   41  625 1276.82392  570.54661  55.3%  37.3 3530s\n",
      "H 5630   381                    1266.3942723  570.57352  54.9%  37.3 3598s\n",
      "H 5631   361                    1265.8440634  570.57352  54.9%  37.3 3598s\n",
      "  5633   362  570.58389   53  620 1265.84406  570.58389  54.9%  37.3 3605s\n",
      "H 5633   341                    1255.3783372  570.58389  54.5%  37.3 3613s\n",
      "H 5633   322                    1252.2160595  570.62987  54.4%  37.3 3616s\n",
      "H 5637   306                    1245.5111967  570.68905  54.2%  37.3 3624s\n",
      "  5638   306  789.61667  319  573 1245.51120  570.68905  54.2%  37.3 3625s\n",
      "H 5638   288                    1238.3575275  570.68905  53.9%  37.3 3633s\n",
      "H 5638   271                    1233.9024133  570.68905  53.7%  37.3 3633s\n",
      "  5639   272  588.21778  160  549 1233.90241  570.75032  53.7%  37.3 3635s\n",
      "H 5642   258                    1225.1912281  570.96303  53.4%  37.2 3638s\n",
      "  5645   260  606.46334  164  619 1225.19123  570.98945  53.4%  37.2 3640s\n",
      "H 5646   245                    1218.5636847  570.98945  53.1%  37.2 3650s\n",
      "  5650   247 1094.03020 1218  536 1218.56368  571.09672  53.1%  37.2 3655s\n",
      "  5652   249  571.09672  101  514 1218.56368  571.09672  53.1%  37.2 3689s\n",
      "H 5652   234                    1211.0223639  571.09672  52.8%  37.2 3689s\n",
      "  5653   234  587.30289  119  542 1211.02236  571.09672  52.8%  37.2 3694s\n",
      "H 5653   220                    1191.1499910  571.09672  52.1%  37.2 3700s\n",
      "  5654   221  571.11473   73  509 1191.14999  571.11473  52.1%  37.2 3709s\n",
      "  5655   222  571.12201   40  542 1191.14999  571.12201  52.1%  37.2 3710s\n",
      "  5659   224  780.09960  289  546 1191.14999  571.17029  52.0%  37.1 3716s\n",
      "H 5659   210                    1180.6190383  571.19479  51.6%  37.1 3783s\n",
      "  5661   212  571.20243   70  542 1180.61904  571.20243  51.6%  37.1 3785s\n",
      "  5665   214  611.91018  188  581 1180.61904  571.20726  51.6%  37.1 3792s\n",
      "  5666   215  611.98026  183  509 1180.61904  571.22322  51.6%  37.1 3801s\n",
      "  5669   217  614.40313  176  554 1180.61904  571.23646  51.6%  37.1 3808s\n",
      "  5670   218  817.48132  368  539 1180.61904  571.23646  51.6%  37.1 3835s\n",
      "  5671   218  571.23646    6  563 1180.61904  571.23646  51.6%  37.0 3840s\n",
      "H 5671   204                    1174.6523296  571.25188  51.4%  37.0 3885s\n",
      "  5679   210 1174.65233  868  554 1174.65233  571.33137  51.4%  37.0 3890s\n",
      "  5682   212  831.44283  488  559 1174.65233  571.33779  51.4%  37.0 3896s\n",
      "H 5682   198                    1136.8306933  571.33826  49.7%  37.0 3956s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  5684   199  648.68599  207  553 1136.83069  571.33826  49.7%  37.0 3962s\n",
      "  5685   200  629.51177  250  534 1136.83069  571.33826  49.7%  37.0 3971s\n",
      "  5689   202  989.69081  497  572 1136.83069  571.34084  49.7%  36.9 3979s\n",
      "  5690   203 1136.83069  770  560 1136.83069  571.34266  49.7%  36.9 4006s\n",
      "  5691   204  571.34266   84  573 1136.83069  571.34266  49.7%  36.9 4011s\n",
      "  5692   204 1072.10072  597  553 1136.83069  571.34266  49.7%  36.9 4019s\n",
      "  5693   205  848.36236  383  569 1136.83069  571.34266  49.7%  36.9 4024s\n",
      "  5694   206  571.34266   54  544 1136.83069  571.34266  49.7%  36.9 4034s\n",
      "  5695   206  793.09488  323  532 1136.83069  571.34266  49.7%  36.9 4038s\n",
      "  5697   209 1136.83069  705  204 1136.83069  571.34266  49.7%  55.5 4041s\n",
      "H 5698   199                    1135.9918710  571.34266  49.7%  55.5 4047s\n",
      "H 5699   189                    1131.4930557  571.34266  49.5%  55.5 4081s\n",
      "  5702   191  611.81286  182  214 1131.49306  571.34266  49.5%  55.5 4096s\n",
      "  5704   193  800.98159  339  226 1131.49306  571.34266  49.5%  55.5 4108s\n",
      "  5705   193  992.13907  811  255 1131.49306  571.34266  49.5%  55.5 4112s\n",
      "  5706   194  818.33669  367  256 1131.49306  571.34266  49.5%  55.5 4121s\n",
      "  5708   195  571.34266   23  276 1131.49306  571.34266  49.5%  55.4 4135s\n",
      "  5710   197 1014.86114  943  297 1131.49306  571.34266  49.5%  55.4 4151s\n",
      "  5711   197  818.15719  323  361 1131.49306  571.34266  49.5%  55.4 4157s\n",
      "  5712   198 1131.49306  822  305 1131.49306  571.34266  49.5%  55.4 4172s\n",
      "  5713   199  614.24001  186  357 1131.49306  571.34266  49.5%  55.4 4179s\n",
      "  5714   199  920.03607  605  337 1131.49306  571.34266  49.5%  55.4 4193s\n",
      "  5715   200  571.34266  104  399 1131.49306  571.34266  49.5%  55.4 4199s\n",
      "  5716   201  611.25638  167  385 1131.49306  571.34266  49.5%  55.4 4207s\n",
      "  5717   201  910.26756  458  387 1131.49306  571.34266  49.5%  55.4 4212s\n",
      "H 5717   191                    1126.9195698  571.34266  49.3%  55.4 4240s\n",
      "  5719   193  611.91018  188  398 1126.91957  571.34266  49.3%  55.3 4245s\n",
      "H 5719   183                    1125.2035160  571.34266  49.2%  55.3 4283s\n",
      "  5722   185 1125.20352  796  393 1125.20352  571.34266  49.2%  55.3 4285s\n",
      "  5725   187  589.30531  142  488 1125.20352  571.34266  49.2%  55.3 4290s\n",
      "  5729   189  571.34266   41  517 1125.20352  571.34266  49.2%  55.2 4295s\n",
      "  5734   193 1125.20352  889  575 1125.20352  571.69532  49.2%  55.2 4300s\n",
      "  5737   195  571.72417   75  566 1125.20352  571.72417  49.2%  55.2 4307s\n",
      "H 5737   184                    1122.3078089  571.72417  49.1%  55.2 4317s\n",
      "  5738   184  789.61667  319  535 1122.30781  571.79996  49.1%  55.1 4342s\n",
      "  5740   186  571.95743   97  499 1122.30781  571.95743  49.0%  55.1 4345s\n",
      "  5743   188  587.86516  120  524 1122.30781  571.98498  49.0%  55.1 4352s\n",
      "H 5743   178                    1121.9366591  571.98498  49.0%  55.1 4361s\n",
      "  5747   180  572.37146  118  551 1121.93666  572.37146  49.0%  55.1 4365s\n",
      "  5750   182 1094.03020 1218  572 1121.93666  572.41695  49.0%  55.0 4373s\n",
      "  5751   183 1121.93666  983  499 1121.93666  572.45431  49.0%  55.0 4384s\n",
      "  5752   184  572.47435  101  545 1121.93666  572.47435  49.0%  55.0 4385s\n",
      "  5756   186  992.63894  814  584 1121.93666  572.50175  49.0%  55.0 4393s\n",
      "  5757   187 1121.93666  827  568 1121.93666  572.62232  49.0%  55.0 4405s\n",
      "  5758   188 1000.79246  508  575 1121.93666  572.62232  49.0%  55.0 4413s\n",
      "  5759   188  780.09960  289  561 1121.93666  572.77473  48.9%  54.9 4425s\n",
      "  5765   192  611.91018  188  578 1121.93666  573.12093  48.9%  54.9 4430s\n",
      "  5768   194  754.19636  256  580 1121.93666  573.12697  48.9%  54.9 4439s\n",
      "H 5768   183                    1104.8380629  573.13712  48.1%  54.9 4469s\n",
      "  5770   185  817.48132  368  574 1104.83806  573.15858  48.1%  54.8 4470s\n",
      "  5774   187  906.38286  460  621 1104.83806  573.18443  48.1%  54.8 4477s\n",
      "  5775   188 1062.33811 1062  608 1104.83806  573.18567  48.1%  54.8 4487s\n",
      "  5777   189  977.56652  486  616 1104.83806  573.29703  48.1%  54.8 4490s\n",
      "  5780   191 1104.83806  977  632 1104.83806  573.34883  48.1%  54.7 4497s\n",
      "  5781   192 1104.83806  827  587 1104.83806  573.36103  48.1%  54.7 4506s\n",
      "  5786   195 1088.37530 1198  646 1104.83806  573.38860  48.1%  54.7 4510s\n",
      "  5789   197  989.69081  497  663 1104.83806  573.39066  48.1%  54.7 4516s\n",
      "  5790   198 1104.83806  770  630 1104.83806  573.40726  48.1%  54.7 4557s\n",
      "H 5790   187                    1075.2285185  573.40726  46.7%  54.7 4557s\n",
      "  5791   188  573.40726   84  653 1075.22852  573.40726  46.7%  54.6 4561s\n",
      "  5792   188 1072.10072  597  620 1075.22852  573.40756  46.7%  54.6 4570s\n",
      "H 5797   180                    1072.2017056  573.43399  46.5%  54.6 4578s\n",
      "  5799   181  581.11472  115  614 1072.20171  573.44965  46.5%  54.6 4588s\n",
      "  5801   182  663.00292  170  688 1072.20171  573.45022  46.5%  54.6 4594s\n",
      "H 5801   171                    1072.2017031  573.45125  46.5%  54.6 4663s\n",
      "  5803   173 1031.77463  987  649 1072.20170  573.45125  46.5%  54.5 4667s\n",
      "  5804   173  800.98159  339  607 1072.20170  573.45125  46.5%  54.5 4676s\n",
      "  5809   177 1072.20170  771  641 1072.20170  573.46339  46.5%  54.5 4680s\n",
      "  5810   177 1014.86114  943  643 1072.20170  573.46339  46.5%  54.5 4685s\n",
      "  5811   178  818.15719  323  597 1072.20170  573.46517  46.5%  54.5 4695s\n",
      "  5817   182  910.26756  458  676 1072.20170  573.48115  46.5%  54.4 4700s\n",
      "  5820   184  587.48680  145  678 1072.20170  573.48454  46.5%  54.4 4707s\n",
      "  5821   185  872.55955  444  626 1072.20170  573.48529  46.5%  54.4 4720s\n",
      "  5822   185 1072.20170  796  619 1072.20170  573.48529  46.5%  54.4 4725s\n",
      "  5823   186  577.33887  137  612 1072.20170  573.48529  46.5%  54.3 4735s\n",
      "  5824   187 1072.20170  890  656 1072.20170  573.48541  46.5%  54.3 4740s\n",
      "  5825   187  589.30531  142  608 1072.20170  573.48616  46.5%  54.3 4749s\n",
      "  5826   188  950.28990  448  620 1072.20170  573.48616  46.5%  54.3 4754s\n",
      "  5827   189  573.48640   91  618 1072.20170  573.48640  46.5%  54.3 4762s\n",
      "  5828   189  573.48640  125  618 1072.20170  573.48640  46.5%  54.3 4766s\n",
      "  5832   193  989.17534  530  202 1072.20170  573.48640  46.5%  65.8 4777s\n",
      "  5833   194  573.48640   53  215 1072.20170  573.48640  46.5%  65.8 4807s\n",
      "H 5833   184                    1061.4697732  573.48640  46.0%  65.8 4807s\n",
      "H 5834   175                    1059.5809448  573.48640  45.9%  65.8 4815s\n",
      "  5835   175  573.48640   12  204 1059.58094  573.48640  45.9%  65.7 4824s\n",
      "  5836   176  952.40386  696  218 1059.58094  573.48640  45.9%  65.7 4827s\n",
      "  5837   177  573.48640   75  213 1059.58094  573.48640  45.9%  65.7 4836s\n",
      "  5838   177  789.61667  319  244 1059.58094  573.48640  45.9%  65.7 4840s\n",
      "  5839   178  588.21778  160  234 1059.58094  573.48640  45.9%  65.7 4847s\n",
      "  5840   179  573.48640   97  263 1059.58094  573.48640  45.9%  65.7 4850s\n",
      "  5841   179  573.48640   23  299 1059.58094  573.48640  45.9%  65.7 4857s\n",
      "  5842   180 1016.01659  939  296 1059.58094  573.48640  45.9%  65.7 4862s\n",
      "  5843   181  587.86516  120  313 1059.58094  573.48640  45.9%  65.7 4869s\n",
      "  5844   181  618.45006  186  369 1059.58094  573.48640  45.9%  65.6 4874s\n",
      "  5845   182  606.46334  164  361 1059.58094  573.48640  45.9%  65.6 4886s\n",
      "  5846   183  659.42205  209  384 1059.58094  573.48640  45.9%  65.6 4892s\n",
      "H 5846   174                    1058.6395553  573.48640  45.8%  65.6 4899s\n",
      "  5847   174  573.48640  118  384 1058.63956  573.48640  45.8%  65.6 4902s\n",
      "  5848   175  749.62792  289  383 1058.63956  573.48640  45.8%  65.6 4907s\n",
      "  5849   176  771.54289  280  385 1058.63956  573.48640  45.8%  65.6 4946s\n",
      "  5850   176 1058.63956 1218  423 1058.63956  573.48640  45.8%  65.6 4951s\n",
      "  5851   177 1058.63956  983  402 1058.63956  573.48640  45.8%  65.6 4958s\n",
      "  5852   178  573.48640  101  409 1058.63956  573.48640  45.8%  65.5 4963s\n",
      "  5853   178  587.30289  119  398 1058.63956  573.48640  45.8%  65.5 4992s\n",
      "  5855   180  573.48640   40  477 1058.63956  573.48640  45.8%  65.5 4996s\n",
      "  5859   182  780.09960  289  471 1058.63956  573.48640  45.8%  65.5 5000s\n",
      "  5863   185  645.11874  204  474 1058.63956  573.48640  45.8%  65.4 5005s\n",
      "  5868   188  754.19636  256  581 1058.63956  573.89781  45.8%  65.4 5010s\n",
      "  5873   192 1058.63956  659  625 1058.63956  574.02309  45.8%  65.3 5018s\n",
      "  5874   192  906.38286  460  575 1058.63956  574.03553  45.8%  65.3 5041s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  5878   195  574.06690   73  604 1058.63956  574.06690  45.8%  65.3 5049s\n",
      "  5879   196 1058.63956  868  585 1058.63956  574.08402  45.8%  65.2 5081s\n",
      "  5883   198  638.64638  202  660 1058.63956  574.10795  45.8%  65.2 5090s\n",
      "  5884   199  648.68599  207  641 1058.63956  574.16416  45.8%  65.2 5101s\n",
      "  5888   202 1058.63956  598  665 1058.63956  574.21964  45.8%  65.1 5109s\n",
      "  5889   202  989.69081  497  614 1058.63956  574.36668  45.7%  65.1 5157s\n",
      "  5892   204 1058.63956  597  681 1058.63956  574.37205  45.7%  65.1 5160s\n",
      "  5896   207  591.73457  165  715 1058.63956  574.39938  45.7%  65.1 5170s\n",
      "  5897   208 1058.63956  705  627 1058.63956  574.40975  45.7%  65.0 5207s\n",
      "  5900   210 1019.15687  532  677 1058.63956  574.43738  45.7%  65.0 5210s\n",
      "  5902   211  611.81286  182  709 1058.63956  574.44384  45.7%  65.0 5218s\n",
      "  5903   212 1031.77463  987  679 1058.63956  574.45076  45.7%  65.0 5251s\n",
      "  5907   214  760.51558  263  704 1058.63956  574.57771  45.7%  64.9 5258s\n",
      "  5908   215  574.60690   23  686 1058.63956  574.60690  45.7%  64.9 5267s\n",
      "  5911   217  818.15719  323  761 1058.63956  574.63127  45.7%  64.9 5270s\n",
      "  5913   218  614.24001  186  768 1058.63956  574.63199  45.7%  64.9 5275s\n",
      "  5914   219  920.03607  605  710 1058.63956  574.63229  45.7%  64.9 5283s\n",
      "  5916   220  611.25638  167  732 1058.63956  574.63783  45.7%  64.8 5288s\n",
      "H 5916   208                    1057.7983808  574.63890  45.7%  64.8 5308s\n",
      "  5919   210  611.91018  188  735 1057.79838  574.64055  45.7%  64.8 5315s\n",
      "  5920   211  587.48680  145  659 1057.79838  574.64273  45.7%  64.8 5324s\n",
      "  5921   212  872.55955  444  672 1057.79838  574.64273  45.7%  64.8 5329s\n",
      "  5922   212 1057.79838  796  648 1057.79838  574.64273  45.7%  64.8 5354s\n",
      "  5923   213  577.33887  137  669 1057.79838  574.64695  45.7%  64.8 5355s\n",
      "  5926   215  950.28990  448  689 1057.79838  574.66389  45.7%  64.7 5363s\n",
      "  5927   216  574.66389   91  666 1057.79838  574.66389  45.7%  64.7 5389s\n",
      "  5928   216  574.66389  125  683 1057.79838  574.66389  45.7%  64.7 5393s\n",
      "  5929   217  574.66389   41  661 1057.79838  574.66389  45.7%  64.7 5402s\n",
      "  5930   218 1028.76073  929  657 1057.79838  574.66400  45.7%  64.7 5406s\n",
      "  5931   218 1057.79838  790  657 1057.79838  574.66400  45.7%  64.7 5415s\n",
      "  5933   220  574.66400   53  631 1057.79838  574.66400  45.7%  64.7 5426s\n",
      "  5934   220 1057.79838  889  631 1057.79838  574.66400  45.7%  64.6 5430s\n",
      "H 5937   212                    1056.3532278  574.66400  45.6%  74.5 5439s\n",
      "  5938   212  789.61667  319  202 1056.35323  574.66400  45.6%  74.4 5440s\n",
      "H 5938   202                    1056.1699183  574.66400  45.6%  74.4 5484s\n",
      "H 5939   192                    1053.2864251  574.66400  45.4%  74.4 5484s\n",
      "  5940   193  574.66400   97  205 1053.28643  574.66400  45.4%  74.4 5486s\n",
      "H 5940   184                    1053.2499343  574.66400  45.4%  74.4 5540s\n",
      "  5943   186  587.86516  120  230 1053.24993  574.66400  45.4%  74.4 5549s\n",
      "  5944   186  618.45006  186  251 1053.24993  574.66400  45.4%  74.4 5552s\n",
      "  5945   187  606.46334  164  251 1053.24993  574.66400  45.4%  74.4 5559s\n",
      "  5946   188  659.42205  209  275 1053.24993  574.66400  45.4%  74.3 5562s\n",
      "H 5946   178                    1041.3141574  574.66400  44.8%  74.3 5568s\n",
      "  5947   178  574.66400  118  302 1041.31416  574.66400  44.8%  74.3 5570s\n",
      "  5949   180  771.54289  280  291 1041.31416  574.66400  44.8%  74.3 5583s\n",
      "  5950   180 1041.31416 1218  314 1041.31416  574.66400  44.8%  74.3 5587s\n",
      "  5951   181 1041.31416  983  327 1041.31416  574.66400  44.8%  74.3 5595s\n",
      "  5953   182  587.30289  119  355 1041.31416  574.66400  44.8%  74.3 5607s\n",
      "  5954   183  574.66400   73  404 1041.31416  574.66400  44.8%  74.2 5611s\n",
      "  5955   184  574.66400   40  389 1041.31416  574.66400  44.8%  74.2 5620s\n",
      "  5956   184  992.63894  814  407 1041.31416  574.66400  44.8%  74.2 5625s\n",
      "  5957   185 1041.31416  827  384 1041.31416  574.66400  44.8%  74.2 5668s\n",
      "  5958   186 1000.79246  508  418 1041.31416  574.66400  44.8%  74.2 5672s\n",
      "  5959   186  780.09960  289  438 1041.31416  574.66400  44.8%  74.2 5711s\n",
      "H 5959   176                    1040.5822250  574.66400  44.8%  74.2 5712s\n",
      "  5962   178 1040.58223  916  497 1040.58223  574.66400  44.8%  74.1 5715s\n",
      "  5967   182  595.65304  153  497 1040.58223  574.66400  44.8%  74.1 5720s\n",
      "  5971   184  574.66400    6  567 1040.58223  574.66400  44.8%  74.0 5725s\n",
      "  5976   188  589.12319  175  689 1040.58223  574.66400  44.8%  74.0 5730s\n",
      "  5978   189  574.66400   73  684 1040.58223  574.66400  44.8%  73.9 5736s\n",
      "  5979   190 1040.58223  868  646 1040.58223  574.66400  44.8%  73.9 5779s\n",
      "  5980   190 1040.58223  977  658 1040.58223  574.66400  44.8%  73.9 5785s\n",
      "  5981   191 1040.58223  827  646 1040.58223  574.66400  44.8%  73.9 5820s\n",
      "  5982   192  831.44283  488  649 1040.58223  574.66400  44.8%  73.9 5825s\n",
      "  5983   192  638.64638  202  662 1040.58223  574.66400  44.8%  73.9 5835s\n",
      "  5984   193  648.68599  207  657 1040.58223  574.66400  44.8%  73.9 5840s\n",
      "H 5984   183                    1039.7902647  574.66400  44.7%  73.9 5848s\n",
      "  5985   184  629.51177  250  659 1039.79026  574.66400  44.7%  73.9 5881s\n",
      "  5987   185 1039.79026  672  659 1039.79026  574.66400  44.7%  73.8 5898s\n",
      "  5988   189  575.33437   79  660 1039.79026  575.33437  44.7%  80.3 5996s\n",
      "  5990   192  575.63843   80  653 1039.79026  575.63843  44.6%  80.3 6068s\n",
      "  5994   195  575.93727   81  650 1039.79026  575.93727  44.6%  80.4 6077s\n",
      "  5998   197  582.10188   81  565 1039.79026  576.37368  44.6%  80.5 6085s\n",
      "  6002   200  576.37368   82  634 1039.79026  576.37368  44.6%  80.6 6091s\n",
      "  6006   203  580.19319   82  639 1039.79026  576.37368  44.6%  80.6 6098s\n",
      "  6010   208  576.37368   83  634 1039.79026  576.37368  44.6%  80.6 6105s\n",
      "H 6017   200                    1034.5196647  576.38798  44.3%  80.6 6145s\n",
      "H 6019   189                    1033.6477405  576.44366  44.2%  80.6 6145s\n",
      "  6021   195  582.10188   84  560 1033.64774  576.44366  44.2%  80.7 6152s\n",
      "  6027   200  576.47793   85  631 1033.64774  576.47793  44.2%  80.7 6158s\n",
      "  6034   204  576.54610   86  633 1033.64774  576.54610  44.2%  80.7 6166s\n",
      "  6041   211  576.56754   87  575 1033.64774  576.56754  44.2%  80.9 6170s\n",
      "H 6050   203                    1033.6451390  576.57307  44.2%  80.9 6206s\n",
      "  6054   211  582.69709   88  479 1033.64514  576.57307  44.2%  81.1 6215s\n",
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      "H 6085   217                    1029.9544368  576.57307  44.0%  81.4 6279s\n",
      "H 6088   208                    1028.0026565  576.57307  43.9%  81.5 6279s\n",
      "  6089   212  579.92920   91  564 1028.00266  576.57307  43.9%  81.5 6294s\n",
      "  6093   218  578.67167   92  491 1028.00266  576.57307  43.9%  81.5 6305s\n",
      "  6100   225  578.67167   93  488 1028.00266  576.57307  43.9%  81.5 6312s\n",
      "  6111   234  579.39506   95  498 1028.00266  576.57307  43.9%  81.7 6321s\n",
      "  6123   243  579.40474   97  486 1028.00266  576.57307  43.9%  81.8 6330s\n",
      "  6139   252  579.40474   98  486 1028.00266  576.57307  43.9%  81.8 6342s\n",
      "  6158   263  580.50462  100  491 1028.00266  576.57307  43.9%  81.9 6352s\n",
      "  6177   261  580.59096  102  491 1028.00266  576.57307  43.9%  82.1 6393s\n",
      "H 6178   252                    1025.8676959  576.57307  43.8%  82.1 6393s\n",
      "H 6179   245                    1024.9576738  576.57307  43.7%  82.1 6393s\n",
      "  6181   259  581.67728  102  439 1024.95767  576.57307  43.7%  82.2 6406s\n",
      "  6196   273  580.76125  104  522 1024.95767  576.57307  43.7%  82.2 6415s\n",
      "  6216   277  580.82152  106  553 1024.95767  576.57307  43.7%  82.4 6426s\n",
      "  6226   293  580.86020  108  546 1024.95767  576.57307  43.7%  82.6 6437s\n",
      "  6246   305  580.95188  110  492 1024.95767  576.57307  43.7%  82.7 6449s\n",
      "  6264   322  580.95188  112  492 1024.95767  576.57307  43.7%  83.0 6460s\n",
      "  6287   335  585.93333  115  424 1024.95767  576.57307  43.7%  83.2 6472s\n",
      "  6308   359  582.05819  117  503 1024.95767  576.57307  43.7%  83.3 6485s\n",
      "  6339   371  582.15960  120  485 1024.95767  576.57307  43.7%  83.5 6500s\n",
      "  6361   389  584.86531  122  486 1024.95767  576.57307  43.7%  83.9 6515s\n",
      "  6386   415  585.45433  124  488 1024.95767  576.57307  43.7%  84.4 6531s\n",
      "  6413   444  585.45790  128  474 1024.95767  576.57307  43.7%  84.6 6545s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  6442   481  587.35891  132  410 1024.95767  576.57307  43.7%  84.8 6560s\n",
      "  6482   508  591.73219  135  278 1024.95767  576.57307  43.7%  85.0 6575s\n",
      "  6516   550  588.50222  139  416 1024.95767  576.57307  43.7%  85.2 6590s\n",
      "  6558   596  588.04505  144  453 1024.95767  576.57307  43.7%  85.5 6606s\n",
      "  6604   641  589.62462  148  370 1024.95767  576.57307  43.7%  85.7 6622s\n",
      "  6649   684  589.82595  151  387 1024.95767  576.57307  43.7%  85.8 6638s\n",
      "  6693   729  589.82278  155  384 1024.95767  576.57307  43.7%  86.1 6685s\n",
      "  6741   772  590.76932  159  395 1024.95767  576.57307  43.7%  86.1 6703s\n",
      "  6785   820  591.46222  163  420 1024.95767  576.57307  43.7%  86.2 6721s\n",
      "  6834   863  598.08377  167  403 1024.95767  576.57307  43.7%  86.5 6741s\n",
      "  6880   893  591.84526  172  425 1024.95767  576.57307  43.7%  86.8 6775s\n",
      "H 6911   893                    1012.7598732  576.57307  43.1%  87.3 6775s\n",
      "  6912   940  591.99572  176  415 1012.75987  576.57307  43.1%  87.3 6796s\n",
      "  6959   987  592.23752  181  417 1012.75987  576.57307  43.1%  87.6 6817s\n",
      "H 7007   991                    1009.9337525  576.57307  42.9%  88.0 6879s\n",
      "  7011  1032  593.03917  186  428 1009.93375  576.57307  42.9%  88.0 6900s\n",
      "  7053  1071  600.93502  189  404 1009.93375  576.57307  42.9%  88.9 6923s\n",
      "  7096  1124  595.36754  194  423 1009.93375  576.57307  42.9%  89.3 6946s\n",
      "  7154  1194  597.28394  200  399 1009.93375  576.57307  42.9%  89.7 6972s\n",
      "  7231  1281  597.61317  212  403 1009.93375  576.57307  42.9%  90.1 7001s\n",
      "  7319  1379  597.86489  222  411 1009.93375  576.57307  42.9%  90.4 7028s\n",
      "  7418  1467  625.97215  232  360 1009.93375  576.57307  42.9%  90.7 7058s\n",
      "  7516  1540  627.05719  241  303 1009.93375  576.57307  42.9%  91.1 7088s\n",
      "  7602  1598  632.44585  253  304 1009.93375  576.57307  42.9%  91.8 7124s\n",
      "  7670  1664  633.12284  262  270 1009.93375  576.57307  42.9%  92.6 7156s\n",
      "H 7761  1758                    1009.9337524  576.57307  42.9%  93.1 7193s\n",
      "  7880  1762  633.83982  291  268 1009.93375  576.57307  42.9%  93.1 7200s\n",
      "\n",
      "Cutting planes:\n",
      "  Gomory: 325\n",
      "  MIR: 145\n",
      "  StrongCG: 8\n",
      "  Flow cover: 115\n",
      "  Zero half: 121\n",
      "  Mod-K: 19\n",
      "  RLT: 147\n",
      "\n",
      "Explored 7884 nodes (743966 simplex iterations) in 7200.39 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 10: 1009.93 1012.76 1024.96 ... 1039.79\n",
      "\n",
      "Time limit reached\n",
      "Best objective 1.009933752363e+03, best bound 5.765730700706e+02, gap 42.9098%\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(20)\n",
    "veh_capacity = veh_capacities\n",
    "veh_depot = [np.random.randint(0,4) for i in range(len(vehicles))]\n",
    "time_limit = 7200\n",
    "\n",
    "routes, veh_routes, node_routes, X, Y, depot, obj_val =  MDCVRP_BP(cust_x, cust_y, cust_demand_item, depot_x, depot_y, \n",
    "                                                                   vehicles, veh_depot, veh_capacity, time_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f8552d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "Overall objective funtion value - 1010\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('-----\\nOverall objective funtion value -', obj_val)\n",
    "\n",
    "X, Y = np.array(X), np.array(Y)\n",
    "plt.scatter(X[depot],Y[depot],color='red', marker='D', label='Depot')\n",
    "\n",
    "for r in range(len(routes)):\n",
    "    color = colors[node_routes[r]]\n",
    "    for n in range(len(routes[r])-1):\n",
    "        i=routes[r][n]\n",
    "        j=routes[r][n+1]\n",
    "        plt.plot([X[i],X[j]],[Y[i],Y[j]], color=color, marker='.')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Network Graph of Multi-Depot Vehicle Routing')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c4c14c",
   "metadata": {},
   "source": [
    "#### ____\n",
    "### Multi-Depot Constrianed Vehicle Routing Problem with k-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4fb1e71d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "Depot Nodes -  [[20, 50], [73, 80], [89, 29], [93, 12]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-----\n",
    "# Vehicle allocation\n",
    "np.random.seed(5)\n",
    "num_vehicle = 12\n",
    "vehicles = np.array(range(1, num_vehicle+1))\n",
    "veh_depot = np.random.randint(0, len(depot_x), size=len(vehicles))\n",
    "depot_nodes = [[depot_x[i], depot_y[i]] for i in range(len(depot_x))]\n",
    "\n",
    "\n",
    "#-----\n",
    "# Clustered VRP Problem\n",
    "cluster = pd.DataFrame(columns=['x', 'y', 'demand'])\n",
    "cluster['x'] = cust_x\n",
    "cluster['y'] = cust_y\n",
    "cluster['demand'] = cust_demand\n",
    "\n",
    "# Model Initialization\n",
    "km = KMeans(n_clusters=num_depot, init=np.array(depot_nodes), verbose=0)\n",
    "#km = MiniBatchKMeans(n_clusters=num_depot, init=np.array(depot_nodes), verbose=0)\n",
    "cluster['cluster'] = km.fit_predict(cluster[['x', 'y']])\n",
    "\n",
    "colorz = colors[:len(depot_nodes)]\n",
    "\n",
    "# Vizualization\n",
    "for i, color in enumerate(colorz):\n",
    "    sm_cl = cluster[cluster['cluster']==i]\n",
    "    plt.scatter(sm_cl.x, sm_cl.y, marker='.', color=color)\n",
    "\n",
    "print('-----\\nDepot Nodes - ', depot_nodes)\n",
    "plt.scatter(depot_x, depot_y, marker='x', color='r')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cd9da6db",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changed value of parameter timelimit to 1800.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 4396 rows, 4481 columns and 33042 nonzeros\n",
      "Model fingerprint: 0x9d0b1bbf\n",
      "Variable types: 35 continuous, 4446 integer (4446 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 4e+01]\n",
      "  Objective range  [1e+00, 1e+02]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 630 rows and 630 columns\n",
      "Presolve time: 0.03s\n",
      "Presolved: 3766 rows, 3851 columns, 29577 nonzeros\n",
      "Variable types: 35 continuous, 3816 integer (3816 binary)\n",
      "\n",
      "Root relaxation: objective 1.881694e+02, 136 iterations, 0.01 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  188.16945    0   71          -  188.16945      -     -    0s\n",
      "     0     0  220.69658    0   78          -  220.69658      -     -    0s\n",
      "     0     0  226.51091    0   72          -  226.51091      -     -    0s\n",
      "     0     0  226.64989    0   72          -  226.64989      -     -    0s\n",
      "     0     0  226.64989    0   78          -  226.64989      -     -    0s\n",
      "     0     0  226.80148    0   92          -  226.80148      -     -    0s\n",
      "     0     0  226.96326    0   87          -  226.96326      -     -    0s\n",
      "     0     0  226.96326    0   88          -  226.96326      -     -    0s\n",
      "     0     0  226.96326    0   85          -  226.96326      -     -    0s\n",
      "     0     0  226.96326    0   93          -  226.96326      -     -    0s\n",
      "H    0     0                     359.9418034  226.96326  36.9%     -    1s\n",
      "     0     0  226.96326    0  100  359.94180  226.96326  36.9%     -    1s\n",
      "     0     0  226.96326    0  102  359.94180  226.96326  36.9%     -    1s\n",
      "     0     0  226.96326    0   77  359.94180  226.96326  36.9%     -    1s\n",
      "     0     0  226.96326    0   74  359.94180  226.96326  36.9%     -    1s\n",
      "H    0     0                     350.5685039  226.96326  35.3%     -    2s\n",
      "     0     2  226.96326    0   72  350.56850  226.96326  35.3%     -    2s\n",
      "H   28    33                     326.3696779  228.10610  30.1%  34.1    3s\n",
      "   436   456  230.30362  124  103  326.36968  228.10610  30.1%  14.0    5s\n",
      "H  676   653                     325.2074002  228.10610  29.9%  12.4    7s\n",
      "  1200  1104  232.77968   65   78  325.20740  228.11660  29.9%  12.8   10s\n",
      "H 1204  1051                     324.8482266  228.11660  29.8%  12.8   10s\n",
      "H 1204   998                     324.3596315  228.11660  29.7%  12.8   11s\n",
      "H 1229   963                     319.8587419  230.79104  27.8%  12.5   13s\n",
      "  1241   971  281.61075   64  106  319.85874  231.36701  27.7%  12.4   15s\n",
      "H 1250   927                     319.3701467  233.56479  26.9%  12.3   16s\n",
      "H 1268   893                     315.6971793  233.56479  26.0%  19.6   18s\n",
      "  1276   898  301.84419  136  102  315.69718  233.66844  26.0%  19.5   20s\n",
      "H 1311   882                     313.7430371  233.96931  25.4%  22.8   23s\n",
      "  1666  1133  237.94830   71   69  313.74304  233.99333  25.4%  25.4   25s\n",
      "H 2054  1243                     309.0433215  234.01054  24.3%  26.4   28s\n",
      "  2064  1271  234.20164   45   90  309.04332  234.01054  24.3%  26.3   30s\n",
      "  4483  2959  235.19519  113   80  309.04332  234.03391  24.3%  19.4   35s\n",
      "H 4679  2983                     309.0433209  234.03391  24.3%  19.2   35s\n",
      "  6956  5164  238.35924  143   56  309.04332  234.08062  24.3%  17.9   40s\n",
      "  9875  7767  264.52675  258   63  309.04332  234.15989  24.2%  18.8   45s\n",
      "H10396  7816                     308.8860925  234.22330  24.2%  18.6   46s\n",
      "H11765  9205                     304.5430093  234.25062  23.1%  17.9   49s\n",
      " 12221  9553  235.73125   41   58  304.54301  234.25751  23.1%  17.7   50s\n",
      " 16449 13238  256.52128  272   20  304.54301  234.41910  23.0%  16.8   55s\n",
      " 20765 16786  269.91177  142   74  304.54301  234.58313  23.0%  16.1   76s\n",
      "H20778 15955                     304.1831201  234.58313  22.9%  16.1   78s\n",
      " 20782 15957  264.59428   91  111  304.18312  234.58313  22.9%  16.1   80s\n",
      " 20789 15962  258.52132   85  106  304.18312  234.58313  22.9%  16.1   85s\n",
      " 20801 15970  263.09997   88  121  304.18312  234.58313  22.9%  16.1   90s\n",
      " 20816 15980  278.35558  148  115  304.18312  235.17975  22.7%  16.1   95s\n",
      " 20833 15991  238.31823   76  115  304.18312  236.59065  22.2%  16.0  100s\n",
      " 20846 16000  265.97545   98  122  304.18312  237.06046  22.1%  16.0  106s\n",
      " 20854 16005  290.07498  199   70  304.18312  237.06653  22.1%  16.0  110s\n",
      " 20862 16014  237.42089   37   70  304.18312  237.42089  21.9%  16.9  115s\n",
      " 20923 16055  237.55086   44  112  304.18312  237.42089  21.9%  17.0  121s\n",
      "H20959 15275                     304.1831008  237.42629  21.9%  17.0  121s\n",
      "H20998 14537                     304.1830083  237.42629  21.9%  17.0  123s\n",
      " 21102 14633  239.02620   59   58  304.18301  237.42629  21.9%  17.1  125s\n",
      " 22158 15187  252.02722   82   67  304.18301  237.42629  21.9%  17.6  130s\n",
      " 22989 15668  243.08173  146   61  304.18301  237.47335  21.9%  17.6  135s\n",
      " 24596 16405  241.32074  195   41  304.18301  237.50239  21.9%  17.6  140s\n",
      " 26609 17692  267.59906   94   52  304.18301  237.68840  21.9%  18.0  145s\n",
      " 27518 18022  283.46973  148   31  304.18301  237.68840  21.9%  18.5  152s\n",
      " 27751 18099  240.49005  162   82  304.18301  237.68840  21.9%  18.6  155s\n",
      " 28913 18901  243.27743  255   51  304.18301  237.68840  21.9%  19.2  160s\n",
      " 31116 20742  244.88340   73   63  304.18301  237.80542  21.8%  19.8  166s\n",
      " 33518 21360     cutoff  157       304.18301  238.01494  21.8%  20.5  170s\n",
      " 36242 22690  290.90415   70   45  304.18301  238.10112  21.7%  20.7  175s\n",
      " 39514 24420  244.98327  105   63  304.18301  238.16237  21.7%  20.8  180s\n",
      " 42145 25647  246.47205   86   55  304.18301  238.22571  21.7%  21.1  185s\n",
      " 44854 26802  289.34318   79  101  304.18301  238.29730  21.7%  21.8  190s\n",
      " 47521 28179  261.25244  202   62  304.18301  238.40831  21.6%  22.1  195s\n",
      " 51293 30016  250.43653  110  116  304.18301  238.57019  21.6%  21.9  200s\n",
      " 54033 31109  244.13411   88   60  304.18301  238.76108  21.5%  21.9  205s\n",
      " 56395 32361  295.90555  287   23  304.18301  238.76108  21.5%  22.6  210s\n",
      " 59505 33923  257.96984  226   63  304.18301  238.97251  21.4%  22.8  215s\n",
      " 63463 35757  288.25884   59   60  304.18301  239.04851  21.4%  22.6  220s\n",
      " 66658 38509  281.06875   61   33  304.18301  239.11961  21.4%  22.6  225s\n",
      " 69882 41097  261.64432  150   76  304.18301  239.22836  21.4%  22.5  230s\n",
      " 71779 42538  248.58788   86   68  304.18301  239.32159  21.3%  22.6  235s\n",
      " 75041 44880  251.74183   60   84  304.18301  239.48925  21.3%  22.6  240s\n",
      " 78025 47573  259.11749   77   70  304.18301  239.58106  21.2%  22.6  245s\n",
      " 81140 50310  249.77472  103   58  304.18301  239.66885  21.2%  22.4  250s\n",
      "H82527 51122                     304.1830082  239.71480  21.2%  22.4  252s\n",
      " 83733 52292  255.90249  224   68  304.18301  239.76922  21.2%  22.5  255s\n",
      " 85955 53976  265.21109   76   75  304.18301  239.84569  21.2%  22.7  260s\n",
      " 88383 56287  269.31030  130   43  304.18301  239.92585  21.1%  22.8  265s\n",
      " 91759 58620  262.19028  133   45  304.18301  240.03312  21.1%  22.6  271s\n",
      " 93257 60019  277.25188   68   49  304.18301  240.08195  21.1%  22.6  275s\n",
      " 95868 62001  288.18905  156   45  304.18301  240.12829  21.1%  22.6  280s\n",
      " 99061 64669  258.55694  160   68  304.18301  240.16901  21.0%  22.5  285s\n",
      " 101881 67217  259.70965  119   54  304.18301  240.23827  21.0%  22.5  290s\n",
      " 104505 69286     cutoff  139       304.18301  240.28888  21.0%  22.6  295s\n",
      " 106762 71095  256.61541  135   74  304.18301  240.33830  21.0%  22.7  300s\n",
      " 109952 73579  267.84143  268   35  304.18301  240.38580  21.0%  22.6  305s\n",
      " 112554 75928  257.42477   81   53  304.18301  240.45319  21.0%  22.5  310s\n",
      " 115899 78713  253.88250   66   68  304.18301  240.54935  20.9%  22.4  315s\n",
      " 119514 81637  250.41457   85   50  304.18301  240.59269  20.9%  22.4  320s\n",
      " 122295 83947  261.76538  327   52  304.18301  240.62524  20.9%  22.5  325s\n",
      " 125422 86411  264.38987   81   48  304.18301  240.68974  20.9%  22.5  330s\n",
      " 128053 88370  303.23046  206   45  304.18301  240.74324  20.9%  22.6  335s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 132610 92222  260.82072   66   93  304.18301  240.80958  20.8%  22.4  340s\n",
      " 135641 94820  271.27290   69   67  304.18301  240.88179  20.8%  22.4  345s\n",
      " 138821 97314     cutoff   64       304.18301  240.95679  20.8%  22.4  350s\n",
      " 141927 99846  285.85475  148   34  304.18301  240.99135  20.8%  22.4  355s\n",
      " 144788 102096  298.71825  161   41  304.18301  241.07182  20.7%  22.4  360s\n",
      " 147861 104583  259.64844   93   53  304.18301  241.11805  20.7%  22.4  365s\n",
      " 150682 106811  253.05713   74   44  304.18301  241.17047  20.7%  22.4  370s\n",
      " 152951 108672  269.14352  256   84  304.18301  241.21196  20.7%  22.4  375s\n",
      " 155434 110713  288.63068  101   34  304.18301  241.22285  20.7%  22.5  380s\n",
      " 157549 112643  253.28026  121   86  304.18301  241.25615  20.7%  22.6  385s\n",
      " 160922 115142  284.45185   87   57  304.18301  241.29781  20.7%  22.6  390s\n",
      " 164225 117595  274.35023  109   58  304.18301  241.34445  20.7%  22.7  395s\n",
      " 167126 120149  292.69967  142   37  304.18301  241.37813  20.6%  22.7  400s\n",
      " 170272 122643  300.14374   86   48  304.18301  241.42289  20.6%  22.7  405s\n",
      " 173309 124898  269.80496   80   75  304.18301  241.46638  20.6%  22.8  410s\n",
      " 175619 126949  266.16162   72   45  304.18301  241.50364  20.6%  22.8  415s\n",
      " 178345 128977  273.11436   87   47  304.18301  241.54246  20.6%  22.9  420s\n",
      " 181106 131185  263.93487  130   42  304.18301  241.56522  20.6%  23.0  425s\n",
      " 184339 133538  269.61952  113   64  304.18301  241.63047  20.6%  23.0  430s\n",
      " 186758 135291  291.27457  257   69  304.18301  241.66858  20.6%  23.1  435s\n",
      " 189604 137401  263.90487   51   44  304.18301  241.74273  20.5%  23.1  440s\n",
      " 191789 138879  284.05593   50   63  304.18301  241.77788  20.5%  23.2  445s\n",
      " 194446 141011  242.44016   53   52  304.18301  241.82218  20.5%  23.3  450s\n",
      " 196670 142788  260.95411   65   43  304.18301  241.84165  20.5%  23.4  455s\n",
      " 199078 144729  265.15016  112   72  304.18301  241.88420  20.5%  23.4  460s\n",
      " 202065 147043  252.18947   67   81  304.18301  241.94474  20.5%  23.5  465s\n",
      " 204897 149416  261.69920  130   88  304.18301  241.99664  20.4%  23.5  470s\n",
      " 206944 150841  301.20812  166   41  304.18301  242.02199  20.4%  23.6  475s\n",
      " 209852 153240  243.05538   70   95  304.18301  242.07183  20.4%  23.6  480s\n",
      " 212434 155337  252.89011   56   58  304.18301  242.11735  20.4%  23.6  485s\n",
      " 215219 157710  260.03656  105   61  304.18301  242.15102  20.4%  23.7  490s\n",
      " 218334 160118  287.63252   92  112  304.18301  242.21635  20.4%  23.7  495s\n",
      " 220698 162128  243.00989   48   68  304.18301  242.24825  20.4%  23.8  500s\n",
      " 222745 163714  255.51937   66   58  304.18301  242.27908  20.4%  23.8  506s\n",
      " 224839 165181  279.61168   77   63  304.18301  242.30888  20.3%  23.8  510s\n",
      " 227294 167109  279.77898   71   44  304.18301  242.34830  20.3%  23.8  515s\n",
      " 229213 168644  269.82706   58   32  304.18301  242.37390  20.3%  23.9  520s\n",
      " 232269 171135  290.40136  108   54  304.18301  242.40930  20.3%  23.9  525s\n",
      " 234847 173093  267.41264   69   90  304.18301  242.45631  20.3%  23.9  530s\n",
      " 236719 174557  262.32905  119   44  304.18301  242.49001  20.3%  24.0  535s\n",
      " 239350 176314  246.11189  110   94  304.18301  242.54965  20.3%  24.1  541s\n",
      " 241694 178094  282.48047   56   91  304.18301  242.55512  20.3%  24.1  545s\n",
      " 244148 180232  277.19131   81  102  304.18301  242.58345  20.3%  24.2  551s\n",
      " 246749 181904  268.45362   83   58  304.18301  242.58487  20.3%  24.2  555s\n",
      " 247456 182673  258.27743   61   47  304.18301  242.58487  20.3%  24.2  560s\n",
      " 250046 184389  258.12885   64   66  304.18301  242.58487  20.3%  24.3  565s\n",
      " 252146 186104  280.25221   75   70  304.18301  242.58487  20.3%  24.3  570s\n",
      " 254961 188415  301.78701   75   42  304.18301  242.61508  20.2%  24.4  575s\n",
      " 257444 190017  266.50687   69   48  304.18301  242.67300  20.2%  24.4  580s\n",
      " 259953 191690  277.58817   81   39  304.18301  242.70785  20.2%  24.5  585s\n",
      " 262362 193728  271.04057   77   37  304.18301  242.74129  20.2%  24.5  590s\n",
      " 264720 195589  273.03368   89   37  304.18301  242.78690  20.2%  24.5  595s\n",
      " 267158 197355  293.80632   81   40  304.18301  242.83696  20.2%  24.6  600s\n",
      " 269424 199116  300.13176   73   74  304.18301  242.87907  20.2%  24.7  605s\n",
      " 271699 200594  261.48042  174   76  304.18301  242.91263  20.1%  24.7  610s\n",
      " 273673 202140     cutoff  179       304.18301  242.93167  20.1%  24.7  615s\n",
      " 276057 203811  270.83441   48   65  304.18301  242.97698  20.1%  24.8  620s\n",
      " 278018 205231  267.08304   61   54  304.18301  243.02244  20.1%  24.8  625s\n",
      " 280561 207166  245.94193   58   88  304.18301  243.06810  20.1%  24.9  630s\n",
      " 282864 208965  271.53204   73   47  304.18301  243.09310  20.1%  24.9  635s\n",
      " 285062 210592  268.51114   66   46  304.18301  243.12309  20.1%  24.9  640s\n",
      " 287242 212273  255.27291   57   97  304.18301  243.15071  20.1%  25.0  645s\n",
      " 289411 213747  296.53287  110   41  304.18301  243.18048  20.1%  25.0  650s\n",
      " 291305 215040  259.09581   66   61  304.18301  243.21477  20.0%  25.1  655s\n",
      " 293151 216672  259.98162   68   96  304.18301  243.23693  20.0%  25.1  660s\n",
      " 295221 218121  271.92596   78   50  304.18301  243.28992  20.0%  25.2  665s\n",
      " 297570 219757  253.14536   52   56  304.18301  243.33214  20.0%  25.3  670s\n",
      " 299865 221830  262.72158   91   57  304.18301  243.36864  20.0%  25.3  675s\n",
      " 302353 223723  271.01979   82   43  304.18301  243.41771  20.0%  25.3  680s\n",
      " 303178 224179  300.84457  109   39  304.18301  243.43091  20.0%  25.3  685s\n",
      " 305436 226167  299.46229   84   36  304.18301  243.46288  20.0%  25.3  690s\n",
      " 307457 227655  268.76205   92   57  304.18301  243.49981  19.9%  25.4  695s\n",
      " 309739 229205  271.22419   83   57  304.18301  243.55650  19.9%  25.4  701s\n",
      " 310796 230209  261.89260   87   40  304.18301  243.56592  19.9%  25.5  705s\n",
      " 313644 232202  269.33698   93   72  304.18301  243.61965  19.9%  25.5  710s\n",
      " 315518 233513  281.12275   80   64  304.18301  243.65158  19.9%  25.5  715s\n",
      " 317433 234976  283.46136   59   65  304.18301  243.67803  19.9%  25.6  720s\n",
      " 319239 236434  243.86860   54   51  304.18301  243.69992  19.9%  25.6  725s\n",
      " 321593 238205  264.51336   74   61  304.18301  243.74332  19.9%  25.6  730s\n",
      " 323850 239888  277.21905   94   70  304.18301  243.77641  19.9%  25.7  735s\n",
      " 326041 241508  260.80330   61   72  304.18301  243.77641  19.9%  25.7  740s\n",
      " 328304 242953  298.54540   70   33  304.18301  243.77641  19.9%  25.8  745s\n",
      " 330649 244621  274.98552   82   64  304.18301  243.77641  19.9%  25.8  750s\n",
      " 332940 246172  268.40421   60   45  304.18301  243.77641  19.9%  25.9  756s\n",
      " 334777 248002  275.06469  186   27  304.18301  243.78151  19.9%  25.9  760s\n",
      " 337803 250010 infeasible  219       304.18301  243.81743  19.8%  25.9  765s\n",
      " 340179 251947  256.87859   76   73  304.18301  243.83409  19.8%  26.0  770s\n",
      " 342772 253994  266.62311   66   51  304.18301  243.88737  19.8%  26.1  775s\n",
      " 345255 255841  276.47240   60   28  304.18301  243.92292  19.8%  26.1  780s\n",
      " 347757 257721  248.45142   50   62  304.18301  243.96250  19.8%  26.1  785s\n",
      " 350361 259566  255.37098   70  116  304.18301  243.99419  19.8%  26.2  790s\n",
      " 353087 261552  281.56874   61   62  304.18301  244.03703  19.8%  26.2  795s\n",
      " 355601 263191  275.41437   57   58  304.18301  244.07649  19.8%  26.2  800s\n",
      " 357700 265058  277.91762  236   36  304.18301  244.10476  19.8%  26.3  805s\n",
      " 361099 267575  266.20741  153   74  304.18301  244.14410  19.7%  26.3  810s\n",
      " 363080 269141  272.57592  129   65  304.18301  244.17498  19.7%  26.3  815s\n",
      " 365587 270656  303.36436   78   20  304.18301  244.18676  19.7%  26.4  820s\n",
      " 367982 272453  274.86944  107   71  304.18301  244.18676  19.7%  26.5  825s\n",
      " 370631 274185  263.80553   62   39  304.18301  244.20546  19.7%  26.5  830s\n",
      " 372860 275809  276.03997  137   63  304.18301  244.23718  19.7%  26.6  835s\n",
      " 375695 277694  273.39884  117   46  304.18301  244.28301  19.7%  26.6  840s\n",
      " 378349 279691  281.40962  138   62  304.18301  244.32198  19.7%  26.6  845s\n",
      " 381391 281882     cutoff  371       304.18301  244.36869  19.7%  26.6  850s\n",
      " 383962 283998  282.78078  258   37  304.18301  244.38417  19.7%  26.7  855s\n",
      " 386768 285916  287.70184   87   47  304.18301  244.42484  19.6%  26.7  860s\n",
      " 389089 287820  298.24173   98   49  304.18301  244.45214  19.6%  26.7  865s\n",
      " 391628 289755  297.96950   95   48  304.18301  244.47620  19.6%  26.8  870s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 393808 291290  258.26210   93   79  304.18301  244.49986  19.6%  26.8  875s\n",
      " 396594 293259  267.41076   54   44  304.18301  244.52960  19.6%  26.8  880s\n",
      " 399398 295475  271.40478   75   45  304.18301  244.56597  19.6%  26.9  885s\n",
      " 401842 296974  281.45470  107   84  304.18301  244.58547  19.6%  26.9  890s\n",
      " 404540 298757  272.20160  166   29  304.18301  244.59763  19.6%  26.9  895s\n",
      " 407163 300684  297.48860  120   51  304.18301  244.61798  19.6%  26.9  900s\n",
      " 410248 303189  275.01555  203   68  304.18301  244.64406  19.6%  27.0  905s\n",
      " 412813 304934  262.25426   61  114  304.18301  244.64971  19.6%  27.0  910s\n",
      " 415301 306707  294.23352  101   52  304.18301  244.66198  19.6%  27.0  915s\n",
      " 418022 308670 infeasible   85       304.18301  244.68624  19.6%  27.0  920s\n",
      " 420489 310385  281.20585   70   37  304.18301  244.70588  19.6%  27.1  925s\n",
      " 423162 312517  267.30090  134   37  304.18301  244.73828  19.5%  27.1  930s\n",
      " 425687 314329  261.76542   63   62  304.18301  244.76919  19.5%  27.2  935s\n",
      " 427990 315899  290.93778  154   46  304.18301  244.79390  19.5%  27.2  940s\n",
      " 430289 317573  269.31941   64   88  304.18301  244.82063  19.5%  27.2  945s\n",
      " 432768 319315  269.10620  110   40  304.18301  244.83957  19.5%  27.3  950s\n",
      " 435384 321252  264.70088   70   67  304.18301  244.86800  19.5%  27.3  955s\n",
      " 437872 323177  270.82015   65   44  304.18301  244.90325  19.5%  27.3  960s\n",
      " 440541 325152  254.67143   66   76  304.18301  244.93298  19.5%  27.4  965s\n",
      " 443088 327093  256.43103   63   85  304.18301  244.96298  19.5%  27.4  970s\n",
      " 445646 328907  257.37928   59   64  304.18301  244.99844  19.5%  27.4  975s\n",
      " 448040 330550  260.72315   56   38  304.18301  245.01545  19.5%  27.5  980s\n",
      " 449878 331868  298.51101  171   43  304.18301  245.02962  19.4%  27.5  985s\n",
      " 452846 334054  267.60594   64   59  304.18301  245.04246  19.4%  27.5  990s\n",
      " 455050 335692  275.83016  101   64  304.18301  245.06051  19.4%  27.6  995s\n",
      " 457774 337632  296.12505   59   57  304.18301  245.09642  19.4%  27.6 1000s\n",
      " 460023 339128  276.31871   75   30  304.18301  245.11352  19.4%  27.6 1005s\n",
      " 462461 340915  251.93280   72   84  304.18301  245.13085  19.4%  27.6 1010s\n",
      " 465158 342624  282.34700   53   68  304.18301  245.16300  19.4%  27.7 1015s\n",
      " 467693 344688  254.06226   68   74  304.18301  245.18279  19.4%  27.7 1020s\n",
      " 469936 346022  286.09510  159   40  304.18301  245.20157  19.4%  27.8 1025s\n",
      " 472273 347968  279.18128  128   69  304.18301  245.22011  19.4%  27.8 1030s\n",
      " 475209 350025  277.13432   53   28  304.18301  245.25013  19.4%  27.8 1035s\n",
      " 477278 351442 infeasible   98       304.18301  245.26765  19.4%  27.9 1040s\n",
      " 479512 352981  260.53930   53   73  304.18301  245.28898  19.4%  27.9 1045s\n",
      " 482209 355065  250.04714  127   81  304.18301  245.31149  19.4%  27.9 1050s\n",
      " 484693 356477  262.93532   59   65  304.18301  245.33224  19.3%  28.0 1055s\n",
      " 487046 358557  264.55372   60   54  304.18301  245.34970  19.3%  28.0 1060s\n",
      " 489393 360410  292.40600   62   78  304.18301  245.37244  19.3%  28.0 1065s\n",
      " 492108 362171  292.71541   54   55  304.18301  245.39646  19.3%  28.0 1070s\n",
      " 494717 364174  273.97999   70   58  304.18301  245.42471  19.3%  28.1 1075s\n",
      " 498031 366737  284.38131   67   53  304.18301  245.45318  19.3%  28.1 1080s\n",
      " 500732 368380  273.24706   70   60  304.18301  245.47447  19.3%  28.1 1085s\n",
      " 503056 370073  268.17587   68   66  304.18301  245.49617  19.3%  28.1 1090s\n",
      " 505551 371659  271.56384  103   50  304.18301  245.52281  19.3%  28.1 1095s\n",
      " 508304 373427  282.66420   80   62  304.18301  245.54644  19.3%  28.1 1100s\n",
      " 510430 375048  290.22107   74   28  304.18301  245.56029  19.3%  28.2 1105s\n",
      " 512253 376730  280.86657   59   63  304.18301  245.57360  19.3%  28.2 1110s\n",
      " 514771 378412  268.57068   80   44  304.18301  245.58514  19.3%  28.2 1116s\n",
      " 516936 379902  294.15536  103   32  304.18301  245.58514  19.3%  28.3 1120s\n",
      " 519134 381374 infeasible  152       304.18301  245.58514  19.3%  28.3 1125s\n",
      " 521393 382973  294.62859   70   42  304.18301  245.59013  19.3%  28.3 1130s\n",
      " 524305 384839  276.33872   89   36  304.18301  245.60979  19.3%  28.3 1135s\n",
      " 527179 386668  270.45558   75   91  304.18301  245.63534  19.2%  28.4 1140s\n",
      " 529978 388886  292.16380  108   34  304.18301  245.64734  19.2%  28.4 1145s\n",
      " 532029 390384  257.75737   66   66  304.18301  245.65639  19.2%  28.4 1150s\n",
      " 534756 392324  293.66584  136   49  304.18301  245.67985  19.2%  28.4 1155s\n",
      " 537108 394049  273.77178   56   80  304.18301  245.69540  19.2%  28.4 1160s\n",
      " 539365 395544     cutoff  132       304.18301  245.71291  19.2%  28.5 1165s\n",
      " 542033 397526     cutoff   67       304.18301  245.73257  19.2%  28.5 1170s\n",
      " 544877 399593  291.32474  134   94  304.18301  245.75303  19.2%  28.5 1175s\n",
      " 547127 401122  278.00682  170   51  304.18301  245.76362  19.2%  28.5 1180s\n",
      " 549414 402627  263.38441   61   85  304.18301  245.78543  19.2%  28.6 1185s\n",
      " 551686 404126  282.39593  113   43  304.18301  245.79951  19.2%  28.6 1190s\n",
      " 554254 406090  281.58674  239   51  304.18301  245.81357  19.2%  28.6 1195s\n",
      " 557137 408204  285.33444  105   44  304.18301  245.83134  19.2%  28.6 1200s\n",
      " 559859 410129  256.99112   69   80  304.18301  245.85548  19.2%  28.6 1205s\n",
      " 562672 412008  280.68391  130   50  304.18301  245.88442  19.2%  28.6 1210s\n",
      " 564923 413381  283.96966   74   62  304.18301  245.90835  19.2%  28.6 1215s\n",
      " 567056 415132  253.78930   61   55  304.18301  245.92365  19.2%  28.7 1220s\n",
      " 569531 417148  300.74438  127   61  304.18301  245.93718  19.1%  28.7 1225s\n",
      " 572337 419189     cutoff  165       304.18301  245.93718  19.1%  28.7 1230s\n",
      " 574459 420719  282.64953  139   52  304.18301  245.93718  19.1%  28.7 1235s\n",
      " 577242 422540  269.11875   96   53  304.18301  245.93718  19.1%  28.8 1240s\n",
      " 579622 424037  266.96450  110   79  304.18301  245.94336  19.1%  28.8 1245s\n",
      " 582190 426015     cutoff   85       304.18301  245.95647  19.1%  28.8 1250s\n",
      " 584448 427771  248.64058   60   71  304.18301  245.97405  19.1%  28.8 1255s\n",
      " 586995 429774  291.10472   97   87  304.18301  245.98863  19.1%  28.8 1260s\n",
      " 589611 431724  264.93363  100   47  304.18301  246.00494  19.1%  28.8 1265s\n",
      " 592473 433919  289.68636   66   50  304.18301  246.01393  19.1%  28.9 1270s\n",
      " 595139 435731  248.58973   61   75  304.18301  246.03253  19.1%  28.9 1275s\n",
      " 597208 437190  278.40420  140   57  304.18301  246.04525  19.1%  28.9 1280s\n",
      " 599844 438977  259.71591   69   57  304.18301  246.06148  19.1%  28.9 1285s\n",
      " 602427 440981  262.78613  109   50  304.18301  246.07520  19.1%  28.9 1290s\n",
      " 604886 442777  267.20050   64   44  304.18301  246.09534  19.1%  28.9 1295s\n",
      " 607923 444969  283.18377   59   59  304.18301  246.11404  19.1%  28.9 1300s\n",
      " 610448 446627  278.89293   84   56  304.18301  246.12878  19.1%  28.9 1305s\n",
      " 612555 448386  276.48755   65   63  304.18301  246.14512  19.1%  29.0 1310s\n",
      " 615210 450353  263.68109   50   67  304.18301  246.16192  19.1%  29.0 1315s\n",
      " 617473 452038  266.45719   67   37  304.18301  246.18084  19.1%  29.0 1320s\n",
      " 620493 454146  270.14083   69   46  304.18301  246.20423  19.1%  29.0 1325s\n",
      " 622489 455599  303.89805   70   45  304.18301  246.21794  19.1%  29.0 1330s\n",
      " 625015 457400  270.97681   50   73  304.18301  246.23711  19.0%  29.0 1335s\n",
      " 627408 458931  248.01992   67   58  304.18301  246.25232  19.0%  29.0 1341s\n",
      " 629604 460733  276.54001   75   57  304.18301  246.26547  19.0%  29.1 1346s\n",
      " 631758 462257  274.48477   69   31  304.18301  246.27710  19.0%  29.1 1350s\n",
      " 634418 464141  252.06211   60   69  304.18301  246.27710  19.0%  29.1 1355s\n",
      " 636263 465482  275.35872   79   79  304.18301  246.27710  19.0%  29.1 1360s\n",
      " 638816 467362  280.39507   74   44  304.18301  246.29041  19.0%  29.1 1365s\n",
      " 640615 468588  270.50206  105   58  304.18301  246.29905  19.0%  29.2 1370s\n",
      " 643400 470656  279.49649   55   53  304.18301  246.31861  19.0%  29.1 1375s\n",
      " 645695 472264  301.41437  121   44  304.18301  246.33412  19.0%  29.2 1380s\n",
      " 647883 473780 infeasible   83       304.18301  246.35051  19.0%  29.2 1385s\n",
      " 649596 475168  273.08564   69   30  304.18301  246.36181  19.0%  29.2 1390s\n",
      " 652171 477056  254.23572   85   46  304.18301  246.37419  19.0%  29.2 1395s\n",
      " 654606 478669  251.15190   69   79  304.18301  246.39054  19.0%  29.3 1400s\n",
      " 656681 480264  272.08379   64   36  304.18301  246.40065  19.0%  29.3 1405s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 659503 481996  258.17983  185   50  304.18301  246.41553  19.0%  29.3 1410s\n",
      " 661401 483202  292.67284   84   37  304.18301  246.42885  19.0%  29.3 1415s\n",
      " 663704 484866  266.77532  137   52  304.18301  246.44520  19.0%  29.3 1420s\n",
      " 666233 486671  249.77842   57   74  304.18301  246.46132  19.0%  29.3 1425s\n",
      " 667946 487907  290.18405  131   23  304.18301  246.47069  19.0%  29.3 1430s\n",
      " 670336 489790  295.55370   53   62  304.18301  246.48554  19.0%  29.3 1435s\n",
      " 672022 490994  298.57897   87   40  304.18301  246.49318  19.0%  29.3 1440s\n",
      " 674016 492498  294.72313   93   41  304.18301  246.50434  19.0%  29.3 1445s\n",
      " 676394 494279  264.59567  121   63  304.18301  246.51884  19.0%  29.3 1450s\n",
      " 678228 495577  256.11970   72   51  304.18301  246.53698  19.0%  29.3 1455s\n",
      " 680055 496871  261.37445  113   56  304.18301  246.54160  18.9%  29.4 1460s\n",
      " 682230 498472  274.81978  239   55  304.18301  246.55172  18.9%  29.4 1465s\n",
      " 683483 499443  263.40836   78   48  304.18301  246.55930  18.9%  29.4 1470s\n",
      " 685333 500527  259.49308  161   75  304.18301  246.56612  18.9%  29.4 1475s\n",
      " 687614 502172  273.68463   86   35  304.18301  246.58637  18.9%  29.4 1480s\n",
      " 690233 504189  247.96197   69   50  304.18301  246.60155  18.9%  29.4 1485s\n",
      " 692733 505933  290.53245   69   45  304.18301  246.61333  18.9%  29.4 1490s\n",
      " 694443 506877  262.57545   54   54  304.18301  246.62500  18.9%  29.4 1495s\n",
      " 695895 508166     cutoff  104       304.18301  246.63305  18.9%  29.5 1500s\n",
      " 698678 509628  300.55058   82   27  304.18301  246.63646  18.9%  29.5 1505s\n",
      " 700589 511301  275.59303   63   74  304.18301  246.64853  18.9%  29.5 1510s\n",
      " 702917 512700  259.54889   60   64  304.18301  246.66206  18.9%  29.5 1515s\n",
      " 705465 514602 infeasible   66       304.18301  246.66603  18.9%  29.5 1520s\n",
      " 707827 516218  281.14438   89   56  304.18301  246.67798  18.9%  29.5 1526s\n",
      " 709716 517600  280.83602   76   49  304.18301  246.68931  18.9%  29.5 1530s\n",
      " 712121 519089  276.76977   66   68  304.18301  246.71072  18.9%  29.5 1535s\n",
      " 714504 520838  253.70419   58   57  304.18301  246.72031  18.9%  29.6 1540s\n",
      " 717129 522521  263.67498   79   91  304.18301  246.73489  18.9%  29.6 1545s\n",
      " 718602 523566  249.39656   91   86  304.18301  246.74034  18.9%  29.6 1550s\n",
      " 721167 525477  274.41619   66   64  304.18301  246.75703  18.9%  29.6 1555s\n",
      " 723630 527233  283.72657   89   62  304.18301  246.76675  18.9%  29.6 1560s\n",
      " 726303 529268  267.57158  102   39  304.18301  246.78201  18.9%  29.6 1565s\n",
      " 728573 530841  273.02674   59   61  304.18301  246.79385  18.9%  29.6 1570s\n",
      " 730980 532636  264.12762   62   71  304.18301  246.80451  18.9%  29.6 1575s\n",
      " 733615 534584  266.74689   68   88  304.18301  246.82172  18.9%  29.6 1580s\n",
      " 735772 536120  266.05186   87   64  304.18301  246.83310  18.9%  29.6 1585s\n",
      " 738332 538108  281.30400  187   54  304.18301  246.84629  18.8%  29.7 1590s\n",
      " 740337 539357  298.23803  119   26  304.18301  246.85579  18.8%  29.7 1595s\n",
      " 742985 541292  275.70744   82   55  304.18301  246.87199  18.8%  29.7 1600s\n",
      " 744991 542771  261.88631   53   90  304.18301  246.88147  18.8%  29.7 1605s\n",
      " 748057 544927  299.71970  120   46  304.18301  246.89943  18.8%  29.7 1610s\n",
      " 750288 546441  266.28703   64   88  304.18301  246.90654  18.8%  29.7 1615s\n",
      " 752851 548365  291.56321  110   94  304.18301  246.91947  18.8%  29.7 1620s\n",
      " 755203 550040  270.43958   63   43  304.18301  246.93292  18.8%  29.7 1625s\n",
      " 756906 551145  282.66147   87   32  304.18301  246.93995  18.8%  29.7 1630s\n",
      " 759414 553109 infeasible  125       304.18301  246.95496  18.8%  29.7 1635s\n",
      " 761968 554927  303.54522  110   49  304.18301  246.96558  18.8%  29.8 1640s\n",
      " 764201 556150  273.57774   75   84  304.18301  246.97839  18.8%  29.8 1645s\n",
      " 766203 557809  291.69343   79   47  304.18301  246.99056  18.8%  29.8 1650s\n",
      " 768502 559528  248.47007   54   58  304.18301  247.00285  18.8%  29.8 1655s\n",
      " 770912 561280  263.18934   64   58  304.18301  247.01433  18.8%  29.8 1660s\n",
      " 773416 563106  264.68319   79   52  304.18301  247.02870  18.8%  29.8 1665s\n",
      " 776020 564900  269.37895   66   63  304.18301  247.04061  18.8%  29.8 1670s\n",
      " 778432 566502  274.18764  113   43  304.18301  247.04752  18.8%  29.8 1675s\n",
      " 780743 568001  265.48531   55  101  304.18301  247.05715  18.8%  29.8 1680s\n",
      " 783719 570213  256.70716  127  130  304.18301  247.06837  18.8%  29.8 1685s\n",
      " 786020 571811  263.12490   51   66  304.18301  247.07936  18.8%  29.8 1690s\n",
      " 788572 573725  291.09346   67   40  304.18301  247.09289  18.8%  29.8 1695s\n",
      " 791291 575490  280.67484   73   25  304.18301  247.10866  18.8%  29.8 1701s\n",
      " 793087 576481  276.75380   55   44  304.18301  247.12272  18.8%  29.8 1706s\n",
      " 794252 577456  263.09438   57   46  304.18301  247.13235  18.8%  29.8 1710s\n",
      " 796389 579010  281.24957   96   65  304.18301  247.14529  18.8%  29.9 1715s\n",
      " 799220 580989  268.94124   68   45  304.18301  247.15964  18.7%  29.9 1720s\n",
      " 801326 582754  291.53177  105   51  304.18301  247.16995  18.7%  29.9 1725s\n",
      " 803950 584225  257.53610   70   60  304.18301  247.17683  18.7%  29.9 1732s\n",
      " 805213 585357  280.28834  153   82  304.18301  247.17778  18.7%  29.9 1735s\n",
      " 807488 586955  291.57512  140   72  304.18301  247.18648  18.7%  29.9 1740s\n",
      " 810042 588591  258.77762   63   71  304.18301  247.19671  18.7%  29.9 1745s\n",
      " 812450 590303  260.65767   50   76  304.18301  247.21151  18.7%  29.9 1750s\n",
      " 815041 592241  267.84826   60   52  304.18301  247.22519  18.7%  29.9 1755s\n",
      " 817683 593983  260.04322   58   52  304.18301  247.23589  18.7%  29.9 1760s\n",
      " 819756 595412  264.69716   52   55  304.18301  247.24340  18.7%  29.9 1765s\n",
      " 822357 597193  266.30544   60   81  304.18301  247.25462  18.7%  29.9 1770s\n",
      " 824527 598592  267.25904   68   51  304.18301  247.26508  18.7%  29.9 1775s\n",
      " 826986 600315  268.51889   70   56  304.18301  247.27618  18.7%  29.9 1780s\n",
      " 829424 601893  268.64721   85   55  304.18301  247.29156  18.7%  29.9 1785s\n",
      " 831806 603346  282.29318   82   55  304.18301  247.30164  18.7%  29.9 1790s\n",
      " 834281 604792  254.80079   64   65  304.18301  247.31459  18.7%  30.0 1795s\n",
      " 836653 606416  286.30184   89   64  304.18301  247.32577  18.7%  30.0 1800s\n",
      "\n",
      "Cutting planes:\n",
      "  Learned: 10\n",
      "  Gomory: 47\n",
      "  Cover: 112\n",
      "  MIR: 48\n",
      "  StrongCG: 1\n",
      "  Flow cover: 309\n",
      "  GUB cover: 2\n",
      "  Inf proof: 75\n",
      "  Zero half: 64\n",
      "  RLT: 160\n",
      "\n",
      "Explored 836831 nodes (25068641 simplex iterations) in 1800.04 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 10: 304.183 304.543 308.886 ... 324.848\n",
      "\n",
      "Time limit reached\n",
      "Best objective 3.041830082239e+02, best bound 2.473260388793e+02, gap 18.6917%\n",
      "Changed value of parameter timelimit to 1800.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 1850 rows, 1887 columns and 13548 nonzeros\n",
      "Model fingerprint: 0xfa53aa4c\n",
      "Variable types: 27 continuous, 1860 integer (1860 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 4e+01]\n",
      "  Objective range  [1e+00, 9e+01]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 324 rows and 324 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 1526 rows, 1563 columns, 11766 nonzeros\n",
      "Variable types: 27 continuous, 1536 integer (1536 binary)\n",
      "\n",
      "Root relaxation: objective 1.414618e+02, 88 iterations, 0.00 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  141.46180    0   54          -  141.46180      -     -    0s\n",
      "     0     0  163.15493    0   53          -  163.15493      -     -    0s\n",
      "     0     0  163.15493    0   56          -  163.15493      -     -    0s\n",
      "H    0     0                     802.6838661  163.15493  79.7%     -    0s\n",
      "     0     0  163.15493    0   55  802.68387  163.15493  79.7%     -    0s\n",
      "H    0     0                     523.9296437  163.15493  68.9%     -    0s\n",
      "H    0     2                     443.9972662  163.15493  63.3%     -    0s\n",
      "     0     2  163.15493    0   52  443.99727  163.15493  63.3%     -    0s\n",
      "H   31    36                     434.4702442  164.03982  62.2%  11.4    0s\n",
      "H   55    60                     363.1574173  164.33984  54.7%   9.4    0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H   56    60                     360.6336973  164.33984  54.4%   9.4    0s\n",
      "H   57    60                     346.7214140  164.33984  52.6%   9.3    0s\n",
      "*  941   951             279     304.7265227  166.19542  45.5%   7.3    1s\n",
      "* 1036   985             199     275.5790448  166.19542  39.7%   7.2    1s\n",
      "H 1318  1198                     275.3531982  166.19542  39.6%   7.4    1s\n",
      "H 1322  1140                     269.6521843  166.19542  38.4%   7.3    2s\n",
      "H 1322  1083                     250.2201058  166.19542  33.6%   7.3    2s\n",
      "H 1323  1030                     246.5442693  166.19542  32.6%   7.3    2s\n",
      "H 1491  1094                     245.5742645  166.58601  32.2%   9.6    3s\n",
      "H 1492  1044                     236.6984610  166.58601  29.6%   9.6    3s\n",
      "H 1499  1006                     236.4910048  166.58601  29.6%   9.6    4s\n",
      "H 1500   962                     232.2198366  166.58601  28.3%   9.6    4s\n",
      "H 1501   919                     226.3830951  166.58601  26.4%   9.6    4s\n",
      "  1972  1218  214.95364   65   30  226.38310  166.58601  26.4%  11.6    5s\n",
      "* 5031  3206              88     222.3216097  167.18710  24.8%  13.0    6s\n",
      "* 7819  5275              52     222.2613807  169.37829  23.8%  13.8    8s\n",
      "* 9632  6721             115     217.6078485  170.06172  21.8%  13.6    9s\n",
      "H10111  6699                     215.3550440  170.10238  21.0%  13.6    9s\n",
      " 11555  8001  195.51165   39   31  215.35504  170.26780  20.9%  13.8   10s\n",
      "*12000  8168             116     213.2860699  170.28197  20.2%  13.9   10s\n",
      "*14667  9666              43     211.2983551  170.58198  19.3%  13.8   11s\n",
      "*15507  9475              91     206.6411634  170.72630  17.4%  13.9   12s\n",
      "*15518  9428              97     206.4563984  170.72630  17.3%  13.8   12s\n",
      "H15545  7863                     200.1608982  170.72630  14.7%  13.9   12s\n",
      "H15906  7760                     199.7587033  170.73948  14.5%  13.8   12s\n",
      "*17740  8859              78     198.8900153  170.99604  14.0%  13.8   13s\n",
      " 21631 11525  194.90924   36   64  198.89002  171.49344  13.8%  13.9   15s\n",
      "H22208 10015                     194.2915518  171.52404  11.7%  14.0   15s\n",
      "H26298 11871                     192.8482782  172.07906  10.8%  14.2   17s\n",
      "H26571 11848                     192.8162121  172.15949  10.7%  14.2   17s\n",
      "H26573 10397                     190.2607268  172.15949  9.51%  14.2   17s\n",
      "H26683  9289                     187.8395132  172.19345  8.33%  14.2   18s\n",
      " 29300 10328  174.51090   43   55  187.83951  172.72252  8.05%  14.4   24s\n",
      "H29307  9816                     186.9504525  172.72252  7.61%  14.4   24s\n",
      " 29312  9819  182.48683   60   93  186.95045  172.72252  7.61%  14.4   25s\n",
      " 29340  9838  174.93440   33   97  186.95045  172.72252  7.61%  14.4   30s\n",
      " 29364  9854  176.77070   36  120  186.95045  172.72252  7.61%  14.4   35s\n",
      "H29368  9362                     186.9504498  172.72252  7.61%  14.4   36s\n",
      " 29394  9381  173.92893   43  110  186.95045  172.72252  7.61%  14.6   40s\n",
      " 29417  9404  172.79471   41   78  186.95045  172.79471  7.57%  14.7   45s\n",
      " 31752 10112  175.65003   54   71  186.95045  173.28524  7.31%  14.9   50s\n",
      " 39475 10797  179.06248   75   51  186.95045  174.95055  6.42%  16.4   55s\n",
      " 46581 10017     cutoff   64       186.95045  177.01084  5.32%  17.9   60s\n",
      " 55470  8483  184.02677   70   43  186.95045  178.78940  4.37%  18.6   65s\n",
      " 64859  8819  181.79237   67   31  186.95045  180.64208  3.37%  18.9   70s\n",
      " 76049 10050     cutoff  100       186.95045  181.86865  2.72%  18.8   75s\n",
      " 87405 10089  183.19146   59   23  186.95045  182.79476  2.22%  18.6   80s\n",
      " 97158  8855     cutoff   73       186.95045  183.51994  1.83%  18.3   85s\n",
      " 107711  6431  186.36254   81   33  186.95045  184.37922  1.38%  18.2   90s\n",
      " 117713  1294 infeasible   69       186.95045  185.61858  0.71%  18.1   95s\n",
      "\n",
      "Cutting planes:\n",
      "  Learned: 16\n",
      "  Gomory: 23\n",
      "  Cover: 83\n",
      "  MIR: 25\n",
      "  StrongCG: 1\n",
      "  Flow cover: 169\n",
      "  Inf proof: 64\n",
      "  Zero half: 34\n",
      "  RLT: 99\n",
      "\n",
      "Explored 120170 nodes (2157359 simplex iterations) in 95.70 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 10: 186.95 187.84 190.261 ... 206.456\n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 1.869504524812e+02, best bound 1.869504524812e+02, gap 0.0000%\n",
      "Changed value of parameter timelimit to 1800.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 3892 rows, 3996 columns and 28992 nonzeros\n",
      "Model fingerprint: 0xb9d6c2b1\n",
      "Variable types: 28 continuous, 3968 integer (3968 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 4e+01]\n",
      "  Objective range  [1e+00, 8e+01]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 672 rows and 672 columns\n",
      "Presolve time: 0.03s\n",
      "Presolved: 3220 rows, 3324 columns, 25296 nonzeros\n",
      "Variable types: 28 continuous, 3296 integer (3296 binary)\n",
      "\n",
      "Root relaxation: objective 9.872756e+01, 97 iterations, 0.00 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0   98.72756    0   56          -   98.72756      -     -    0s\n",
      "H    0     0                    1124.8329445   98.72756  91.2%     -    0s\n",
      "H    0     0                     769.0766345   98.72756  87.2%     -    0s\n",
      "     0     0  106.82185    0   60  769.07663  106.82185  86.1%     -    0s\n",
      "H    0     0                     566.4347439  106.82185  81.1%     -    0s\n",
      "     0     0  126.71436    0   63  566.43474  126.71436  77.6%     -    0s\n",
      "     0     0  126.71436    0   61  566.43474  126.71436  77.6%     -    0s\n",
      "     0     0  126.71436    0   61  566.43474  126.71436  77.6%     -    0s\n",
      "     0     0  126.71436    0   61  566.43474  126.71436  77.6%     -    0s\n",
      "H    0     0                     521.6192722  126.71436  75.7%     -    0s\n",
      "H    0     0                     356.9903890  126.71436  64.5%     -    0s\n",
      "     0     0  126.71436    0   71  356.99039  126.71436  64.5%     -    0s\n",
      "     0     0  126.71436    0   58  356.99039  126.71436  64.5%     -    0s\n",
      "     0     0  126.71436    0   56  356.99039  126.71436  64.5%     -    0s\n",
      "     0     0  126.71436    0   56  356.99039  126.71436  64.5%     -    0s\n",
      "     0     0  126.71436    0   56  356.99039  126.71436  64.5%     -    0s\n",
      "H    0     0                     315.0127222  126.71436  59.8%     -    1s\n",
      "     0     2  126.71436    0   56  315.01272  126.71436  59.8%     -    1s\n",
      "H   28    31                     307.4247881  126.87911  58.7%  34.0    2s\n",
      "H   29    31                     305.6146262  126.87911  58.5%  33.6    2s\n",
      "H   63    67                     293.7887791  126.87911  56.8%  19.7    2s\n",
      "H  520   513                     289.0791882  126.87911  56.1%  11.6    4s\n",
      "H  632   617                     288.1818409  126.87911  56.0%  11.1    5s\n",
      "H  633   616                     266.8456982  126.87911  52.5%  11.1    5s\n",
      "H 1179  1126                     263.9915552  126.89288  51.9%  12.0    7s\n",
      "H 1206  1071                     237.6253195  126.89288  46.6%  12.1    7s\n",
      "H 1210  1016                     232.3892515  126.89288  45.4%  12.0    8s\n",
      "H 1218   971                     228.8704129  129.20727  43.5%  12.0    9s\n",
      "  1225   975  188.81657  107   72  228.87041  130.93224  42.8%  11.9   10s\n",
      "H 1228   927                     228.1456529  131.00839  42.6%  11.9   10s\n",
      "  1259   948  206.22033  114  174  228.14565  141.92018  37.8%  11.6   15s\n",
      "  1314   985  225.43655  186  184  228.14565  142.77405  37.4%  11.1   20s\n",
      "H 1344   955                     226.3593981  142.77483  36.9%  23.7   22s\n",
      "  1356   963  143.54130    7  156  226.35940  143.54130  36.6%  23.5   25s\n",
      "H 1395   940                     223.2067728  144.28610  35.4%  30.7   28s\n",
      "  1916  1217  146.33424   39  113  223.20677  144.88626  35.1%  34.7   30s\n",
      "H 2938  1573                     222.3138506  144.95465  34.8%  32.4   32s\n",
      "H 2958  1524                     222.3138478  144.95465  34.8%  32.4   35s\n",
      "H 4122  2047                     220.8567561  145.03027  34.3%  31.1   39s\n",
      "  4570  2464  191.83075   79   38  220.85676  145.09181  34.3%  30.8   40s\n",
      "  7100  4247  145.65940   43   82  220.85676  145.11148  34.3%  30.2   45s\n",
      "  9020  5707  187.19704   88   47  220.85676  145.12720  34.3%  31.3   50s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H10759  6892                     218.4568393  145.14144  33.6%  31.3   54s\n",
      " 10945  7245  148.26453   43   61  218.45684  145.14354  33.6%  31.3   55s\n",
      " 12946  8670  146.20250   45  100  218.45684  145.20233  33.5%  31.5   60s\n",
      " 15555 10609  186.25336   94   43  218.45684  145.30501  33.5%  31.3   65s\n",
      " 17983 12091  159.13915   58   55  218.45684  145.41758  33.4%  31.0   70s\n",
      " 19972 13761  179.86973   82   15  218.45684  145.47928  33.4%  31.0   75s\n",
      " 20461 13762  199.58443   67   56  218.45684  145.48071  33.4%  30.9   90s\n",
      "H20478 13084                     218.3164644  145.48071  33.4%  30.8   94s\n",
      " 20480 13086  180.59561   64  125  218.31646  145.48071  33.4%  30.8   95s\n",
      " 20491 13093  157.87136   68  126  218.31646  145.48071  33.4%  30.8  100s\n",
      " 20512 13107  147.73854   42  153  218.31646  145.48071  33.4%  30.8  105s\n",
      " 20529 13118  163.84486   59  194  218.31646  145.48071  33.4%  30.8  110s\n",
      " 20538 13124  186.98223   71  144  218.31646  145.64455  33.3%  30.7  115s\n",
      " 20548 13131  145.92305   44  139  218.31646  145.68925  33.3%  30.7  120s\n",
      " 20561 13140  199.58443   67  218  218.31646  145.76677  33.2%  30.7  125s\n",
      " 20572 13147  194.34600   73  206  218.31646  145.86844  33.2%  30.7  130s\n",
      " 20582 13154  191.14962   68  185  218.31646  145.92955  33.2%  30.7  135s\n",
      " 20595 13162  178.23632   51  190  218.31646  146.07366  33.1%  30.7  140s\n",
      " 20602 13167  173.66343   57  163  218.31646  146.07396  33.1%  30.7  145s\n",
      " 20608 13171  149.14392   46  147  218.31646  146.07396  33.1%  30.6  151s\n",
      " 20613 13179  146.91382   38  167  218.31646  146.09026  33.1%  33.1  156s\n",
      " 20633 13195  147.03802   41   77  218.31646  146.23845  33.0%  33.3  160s\n",
      " 20742 13274  149.07707   53   41  218.31646  146.24582  33.0%  33.5  165s\n",
      " 21125 13521  159.95141   96   45  218.31646  146.24582  33.0%  33.7  170s\n",
      " 21593 13812  173.91300  172   23  218.31646  146.24582  33.0%  33.8  175s\n",
      " 22542 14323  214.76992  126   26  218.31646  146.25640  33.0%  33.6  180s\n",
      " 23336 14826  158.71125   97   49  218.31646  146.29292  33.0%  33.4  185s\n",
      " 24276 15443  153.95248   64   20  218.31646  146.32260  33.0%  33.5  190s\n",
      " 25579 16044  177.48655  182   46  218.31646  146.33448  33.0%  33.4  195s\n",
      " 27363 16966  158.08867  117   45  218.31646  146.37150  33.0%  33.1  201s\n",
      " 27509 17013  148.87264  129   74  218.31646  146.37150  33.0%  33.1  211s\n",
      "H27533 16343                     218.3163972  146.37150  33.0%  33.1  211s\n",
      " 28435 17350  149.62874  187   75  218.31640  146.37150  33.0%  32.9  215s\n",
      " 30736 18695  151.40474   71   49  218.31640  146.55092  32.9%  32.2  221s\n",
      " 32250 19193  148.19728   56   28  218.31640  146.60525  32.8%  31.9  225s\n",
      " 33924 20210  153.85861  227   15  218.31640  146.60525  32.8%  32.3  230s\n",
      " 36108 21421  148.55713   53   58  218.31640  146.66749  32.8%  31.8  236s\n",
      " 37668 22595  149.81001  128   29  218.31640  146.66749  32.8%  31.9  240s\n",
      " 39417 23201  147.37635   54   37  218.31640  146.69923  32.8%  31.4  245s\n",
      " 40942 24440  146.99770   58   44  218.31640  146.74475  32.8%  31.2  251s\n",
      " 43096 25138  152.70535   78   45  218.31640  146.76557  32.8%  30.7  255s\n",
      " 45166 26206  150.35811  108   43  218.31640  146.79195  32.8%  30.4  260s\n",
      " 48410 28141  150.53606   56   64  218.31640  146.83388  32.7%  29.7  265s\n",
      " 51522 30233  179.78495  182   55  218.31640  146.85419  32.7%  29.1  270s\n",
      " 54300 31470  153.68064   78   68  218.31640  146.88359  32.7%  28.9  275s\n",
      " 58052 33364  184.65149  196   46  218.31640  146.92811  32.7%  28.4  280s\n",
      " 61365 36103  150.37905   77   39  218.31640  146.97001  32.7%  28.0  285s\n",
      " 65143 39450  150.70975   54  101  218.31640  147.00405  32.7%  27.4  290s\n",
      " 69056 42912  151.39514  273   48  218.31640  147.02345  32.7%  26.8  295s\n",
      " 72180 45933  171.18802   62   41  218.31640  147.04773  32.6%  26.7  301s\n",
      " 75834 48829  159.23518  214   36  218.31640  147.07072  32.6%  26.2  305s\n",
      " 79177 51966  150.90027  114   44  218.31640  147.09533  32.6%  26.0  310s\n",
      " 83323 55645  147.48918   92   39  218.31640  147.11416  32.6%  25.5  315s\n",
      " 87523 59294  149.15096   67  114  218.31640  147.13506  32.6%  25.2  320s\n",
      " 91531 62818  147.94036   40   45  218.31640  147.16158  32.6%  24.8  325s\n",
      " 94991 65819  151.88929   67   58  218.31640  147.18042  32.6%  24.6  330s\n",
      " 99253 69846  151.82311  111   20  218.31640  147.19409  32.6%  24.2  335s\n",
      " 103032 72959  208.64922  106   34  218.31640  147.20693  32.6%  24.0  340s\n",
      " 106113 75867  192.12250   75   50  218.31640  147.21618  32.6%  23.8  345s\n",
      " 109663 78737  150.25005   47  121  218.31640  147.22192  32.6%  23.7  350s\n",
      " 112524 81318  158.46858   70   49  218.31640  147.24041  32.6%  23.5  355s\n",
      " 115418 83944  149.93153   70   77  218.31640  147.25669  32.5%  23.4  360s\n",
      " 119093 86904  151.15355   82   34  218.31640  147.27380  32.5%  23.3  365s\n",
      " 121563 88966  155.53167   63   41  218.31640  147.27961  32.5%  23.4  370s\n",
      " 123862 91170  163.62989   81   51  218.31640  147.28541  32.5%  23.5  375s\n",
      " 126445 93396  154.84329  237   34  218.31640  147.29252  32.5%  23.4  380s\n",
      " 128852 95467  159.54520  127   65  218.31640  147.30112  32.5%  23.4  385s\n",
      " 131658 97372  150.17089  114   89  218.31640  147.30415  32.5%  23.3  390s\n",
      " 133127 98981  177.29817  180   43  218.31640  147.30415  32.5%  23.4  395s\n",
      " 135823 101486  149.49555   62   78  218.31640  147.32400  32.5%  23.4  400s\n",
      " 137480 102838  156.74026  169   53  218.31640  147.33051  32.5%  23.3  405s\n",
      " 140153 105293  151.48880   56   51  218.31640  147.34531  32.5%  23.2  410s\n",
      " 142469 107408  152.76838   86   51  218.31640  147.34923  32.5%  23.1  415s\n",
      " 144841 109240  151.36839   69   36  218.31640  147.35668  32.5%  23.0  420s\n",
      " 147286 111538  149.17628   53   48  218.31640  147.36413  32.5%  23.0  425s\n",
      " 149833 113609  149.98482   58  106  218.31640  147.37027  32.5%  23.0  430s\n",
      " 152573 116263  148.58200   63   66  218.31640  147.37779  32.5%  22.9  435s\n",
      " 155546 118689  150.46223   95   43  218.31640  147.38834  32.5%  22.8  440s\n",
      " 156890 119903  200.57283  228    4  218.31640  147.38838  32.5%  22.9  445s\n",
      " 159989 122389  209.31765  275   34  218.31640  147.40358  32.5%  22.9  450s\n",
      " 162640 124408  159.00738   82   70  218.31640  147.41217  32.5%  22.9  455s\n",
      " 164035 125876  150.39472  165   54  218.31640  147.41217  32.5%  23.0  460s\n",
      " 166058 127606  147.87213   62   65  218.31640  147.41718  32.5%  23.0  465s\n",
      " 169113 130385  171.28732  130   27  218.31640  147.42486  32.5%  23.0  470s\n",
      " 172576 133259  203.83656  228   18  218.31640  147.43742  32.5%  22.9  475s\n",
      " 175891 136421  187.03456  252   24  218.31640  147.44750  32.5%  22.8  481s\n",
      " 179214 139003  152.48888   58   56  218.31640  147.45895  32.5%  22.7  485s\n",
      " 181665 140953  173.61957  114   33  218.31640  147.46559  32.5%  22.7  490s\n",
      " 183877 142604     cutoff   82       218.31640  147.47430  32.4%  22.8  495s\n",
      " 185982 144837  154.76447   81   26  218.31640  147.47756  32.4%  22.7  500s\n",
      " 189513 147804  207.81698  161   13  218.31640  147.48778  32.4%  22.6  505s\n",
      " 192231 150020  152.78841  191   68  218.31640  147.48997  32.4%  22.6  510s\n",
      " 195579 153057  149.00904   61   17  218.31640  147.49428  32.4%  22.5  515s\n",
      " 198398 155564  151.64978  102   65  218.31640  147.49991  32.4%  22.4  520s\n",
      " 200840 157760 infeasible  223       218.31640  147.50319  32.4%  22.3  525s\n",
      " 203109 159524  154.32579  106   65  218.31640  147.50585  32.4%  22.4  530s\n",
      " 205656 162241  160.00135  109   49  218.31640  147.51079  32.4%  22.4  535s\n",
      " 208682 164679  148.14751   52   42  218.31640  147.51720  32.4%  22.4  540s\n",
      " 211681 167115  158.06562   58   16  218.31640  147.51989  32.4%  22.3  545s\n",
      "H212047 167084                     217.9840927  147.51989  32.3%  22.3  546s\n",
      " 214017 169207  156.02045   77   43  217.98409  147.52386  32.3%  22.3  550s\n",
      " 217087 171745  149.86913   69   51  217.98409  147.52803  32.3%  22.3  555s\n",
      " 219449 173651  148.62879   80   68  217.98409  147.53574  32.3%  22.3  560s\n",
      " 223275 177126 infeasible  125       217.98409  147.54555  32.3%  22.2  565s\n",
      "H224200 177263                     217.0060021  147.54555  32.0%  22.2  567s\n",
      " 224855 178340  190.51740  136   36  217.00600  147.54555  32.0%  22.2  570s\n",
      " 227823 181018  150.58361   91   53  217.00600  147.55262  32.0%  22.1  575s\n",
      " 230814 183214  150.28061   71   34  217.00600  147.55872  32.0%  22.1  580s\n",
      " 234355 186516  158.75840   80   18  217.00600  147.56903  32.0%  22.0  585s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 237415 189252  153.16641  132   40  217.00600  147.57232  32.0%  21.9  590s\n",
      " 239378 191232  174.97492  123   63  217.00600  147.57654  32.0%  21.9  595s\n",
      " 242522 193788  151.20792   55   57  217.00600  147.58257  32.0%  21.9  600s\n",
      " 245539 196312  152.76711  195   42  217.00600  147.59116  32.0%  21.8  605s\n",
      " 247864 197986 infeasible  228       217.00600  147.59328  32.0%  21.8  610s\n",
      " 249343 199453  151.99243  105   26  217.00600  147.59632  32.0%  21.8  615s\n",
      " 251988 201776  154.40397  106   31  217.00600  147.59988  32.0%  21.8  620s\n",
      " 254436 204190  155.84176  103   65  217.00600  147.60423  32.0%  21.8  625s\n",
      " 257938 207105  156.08044   78   65  217.00600  147.61034  32.0%  21.8  630s\n",
      " 260362 209038  150.63415   80   27  217.00600  147.61496  32.0%  21.8  635s\n",
      "H261089 209692                     217.0059765  147.61524  32.0%  21.8  637s\n",
      " 262870 211227  214.50686  139   50  217.00598  147.61825  32.0%  21.8  640s\n",
      " 266108 214127  153.83122  123   40  217.00598  147.62523  32.0%  21.7  645s\n",
      " 268881 216688  150.03609   87   74  217.00598  147.63297  32.0%  21.7  650s\n",
      " 271955 219278  149.56227   58   43  217.00598  147.63657  32.0%  21.6  655s\n",
      " 275448 222267  154.47499   95   60  217.00598  147.64692  32.0%  21.6  660s\n",
      " 278560 225057  154.31527   76   37  217.00598  147.65319  32.0%  21.5  665s\n",
      " 281707 227786  170.78638  123   40  217.00598  147.66293  32.0%  21.5  670s\n",
      " 284282 229759  184.15528  101   33  217.00598  147.66865  32.0%  21.4  675s\n",
      " 286863 231805  190.31775   63   69  217.00598  147.67508  31.9%  21.5  680s\n",
      " 288810 234021  160.98495  120   50  217.00598  147.67942  31.9%  21.5  685s\n",
      " 291557 236257  190.20628  141   50  217.00598  147.68291  31.9%  21.4  690s\n",
      " 295042 239336  149.94526   59   38  217.00598  147.68740  31.9%  21.4  695s\n",
      " 298422 242245  156.46959  128   47  217.00598  147.69244  31.9%  21.3  700s\n",
      " 301526 244853  161.53497  102   48  217.00598  147.69598  31.9%  21.3  706s\n",
      " 303983 246764  166.40695  117   48  217.00598  147.70096  31.9%  21.3  710s\n",
      " 306858 249444  156.83942   78   68  217.00598  147.70677  31.9%  21.3  715s\n",
      " 309995 251827  212.63926  141   26  217.00598  147.71299  31.9%  21.2  720s\n",
      " 313322 254551  157.12828   85   41  217.00598  147.71728  31.9%  21.2  725s\n",
      " 315201 256011  202.72286  129   34  217.00598  147.71780  31.9%  21.3  730s\n",
      " 319098 259459  156.02891   57   26  217.00598  147.72863  31.9%  21.2  735s\n",
      " 321746 261560  169.58641  120   46  217.00598  147.73457  31.9%  21.2  740s\n",
      " 324368 264141  151.52018   75   42  217.00598  147.73896  31.9%  21.1  745s\n",
      " 327181 266428  159.80533   58   47  217.00598  147.74331  31.9%  21.1  750s\n",
      " 330186 268983  201.73379  122   38  217.00598  147.74828  31.9%  21.1  755s\n",
      " 332780 271304  154.49790  172   51  217.00598  147.75442  31.9%  21.0  761s\n",
      " 335091 273235  158.83722   66   42  217.00598  147.75769  31.9%  21.0  765s\n",
      " 336628 274277  152.70982  169   42  217.00598  147.75799  31.9%  21.1  771s\n",
      " 338152 275976  151.14733   80   35  217.00598  147.76194  31.9%  21.1  775s\n",
      " 340526 277914  154.64412   90   27  217.00598  147.76772  31.9%  21.1  780s\n",
      " 343196 280286  162.84248  119   47  217.00598  147.77318  31.9%  21.1  785s\n",
      " 346240 282957  212.18382  103   15  217.00598  147.78044  31.9%  21.1  790s\n",
      " 348867 285231  151.64587   62   47  217.00598  147.78511  31.9%  21.0  795s\n",
      " 351776 287688  159.96946  137   24  217.00598  147.79114  31.9%  21.0  800s\n",
      " 353434 288819  176.32849   95   32  217.00598  147.79241  31.9%  21.0  805s\n",
      " 355039 290207  151.97612   57   77  217.00598  147.79742  31.9%  21.0  810s\n",
      " 357812 292767  160.08955   86   48  217.00598  147.80400  31.9%  21.0  815s\n",
      " 360561 295218  206.28659  131   48  217.00598  147.80777  31.9%  21.0  820s\n",
      " 363558 297865  208.27739  257   32  217.00598  147.81172  31.9%  21.0  825s\n",
      " 365553 299316  156.17328   84   68  217.00598  147.81437  31.9%  21.0  830s\n",
      " 368223 301323  153.08916   80   53  217.00598  147.81758  31.9%  21.0  837s\n",
      " 369021 302520  160.70207   91   31  217.00598  147.81940  31.9%  21.0  840s\n",
      " 372106 305160  157.16231   69   25  217.00598  147.82554  31.9%  20.9  845s\n",
      "H374301 306710                     217.0059755  147.83009  31.9%  20.9  847s\n",
      "H374332 306710                     217.0059632  147.83009  31.9%  20.9  847s\n",
      " 375875 307983  190.89635   90   31  217.00596  147.83367  31.9%  20.9  850s\n",
      " 379221 311000  170.89355  100   57  217.00596  147.84011  31.9%  20.9  855s\n",
      " 382143 313714     cutoff  150       217.00596  147.84594  31.9%  20.8  860s\n",
      " 385015 316105  153.99192   60   98  217.00596  147.84955  31.9%  20.8  865s\n",
      " 387795 318598 infeasible   84       217.00596  147.85411  31.9%  20.8  870s\n",
      " 391130 321401  165.71125  110   46  217.00596  147.85897  31.9%  20.8  875s\n",
      " 394840 324475  157.42665  127   50  217.00596  147.86353  31.9%  20.7  880s\n",
      "H397144 326071                     217.0059590  147.86433  31.9%  20.7  884s\n",
      " 397162 326446  157.03041  293   42  217.00596  147.86433  31.9%  20.7  885s\n",
      " 400973 329642  206.62995  122   35  217.00596  147.87332  31.9%  20.7  890s\n",
      "H401189 329642                     217.0058403  147.87347  31.9%  20.7  890s\n",
      "H402075 330412                     217.0058180  147.87533  31.9%  20.7  892s\n",
      "H402773 331088                     217.0058159  147.87551  31.9%  20.7  893s\n",
      "H403003 331088                     217.0058131  147.87568  31.9%  20.7  893s\n",
      " 403749 331866  170.80114  189   58  217.00581  147.87568  31.9%  20.7  895s\n",
      " 406134 333672  163.55569  109   30  217.00581  147.88004  31.9%  20.7  905s\n",
      "H406136 333672                     217.0058089  147.88004  31.9%  20.7  905s\n",
      " 406167 337052  163.55569  110   30  217.00581  147.88055  31.9%  20.6  913s\n",
      "H407361 337052                     217.0058004  147.88058  31.9%  20.6  913s\n",
      " 410547 337690  169.46253   92   28  217.00580  147.88731  31.9%  20.6  915s\n",
      " 413389 339953  159.03559  131   20  217.00580  147.88966  31.8%  20.7  920s\n",
      " 416245 342748  159.18106  108   37  217.00580  147.89426  31.8%  20.6  925s\n",
      " 420024 345724  201.41249  108   35  217.00580  147.90211  31.8%  20.6  930s\n",
      " 422606 347943  183.70613   75   23  217.00580  147.90449  31.8%  20.6  935s\n",
      " 425898 350762  166.60043   93   30  217.00580  147.90859  31.8%  20.6  940s\n",
      " 429034 353561  212.13025   72   20  217.00580  147.91290  31.8%  20.5  945s\n",
      " 432424 356689  166.18858  101   49  217.00580  147.91743  31.8%  20.5  950s\n",
      " 434998 358760  150.46057   63   78  217.00580  147.92040  31.8%  20.5  955s\n",
      " 437913 361184  162.25852  100   41  217.00580  147.92279  31.8%  20.5  960s\n",
      " 441143 363764  202.94686  120   25  217.00580  147.92605  31.8%  20.5  965s\n",
      " 443611 366062  156.06786  189   35  217.00580  147.92922  31.8%  20.5  970s\n",
      " 446356 368402  153.66626   78   66  217.00580  147.93165  31.8%  20.5  975s\n",
      " 449129 370997  150.53693   75   45  217.00580  147.93618  31.8%  20.5  980s\n",
      " 452210 373295  163.63348   86   24  217.00580  147.94220  31.8%  20.5  985s\n",
      " 455813 376553  203.48365  137   44  217.00580  147.94737  31.8%  20.5  990s\n",
      " 458567 379048  155.62581  200   31  217.00580  147.95129  31.8%  20.4  995s\n",
      " 461822 381762  155.20786  160   18  217.00580  147.95751  31.8%  20.5 1000s\n",
      " 464114 383771  161.04458   88   26  217.00580  147.96191  31.8%  20.4 1005s\n",
      " 467139 386027  160.87399  183   27  217.00580  147.96479  31.8%  20.4 1010s\n",
      " 468503 387507  170.62172   70   20  217.00580  147.96667  31.8%  20.5 1015s\n",
      " 471112 389606  215.57305  116   13  217.00580  147.97023  31.8%  20.4 1020s\n",
      " 473805 391617  155.31150   63   49  217.00580  147.97561  31.8%  20.4 1025s\n",
      " 475909 393634  164.04921   94   39  217.00580  147.97899  31.8%  20.4 1030s\n",
      " 479126 396650  149.59285   51   37  217.00580  147.98420  31.8%  20.4 1035s\n",
      " 482223 399216  161.52501   92   22  217.00580  147.99113  31.8%  20.4 1040s\n",
      " 485349 401931  152.58854   62   67  217.00580  147.99511  31.8%  20.4 1045s\n",
      " 487760 403959  162.28733  132   25  217.00580  147.99859  31.8%  20.4 1050s\n",
      " 490776 406426  150.99796   77   19  217.00580  148.00200  31.8%  20.4 1055s\n",
      " 492749 407757  158.04823  119   44  217.00580  148.00454  31.8%  20.4 1060s\n",
      " 496038 410990  168.89715   93   33  217.00580  148.00740  31.8%  20.3 1065s\n",
      " 499588 413883  149.46326   60   41  217.00580  148.01190  31.8%  20.3 1070s\n",
      " 502796 416655  149.29691   49   41  217.00580  148.01655  31.8%  20.3 1075s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 505660 419229  176.11351  166   30  217.00580  148.02107  31.8%  20.3 1080s\n",
      " 507737 420885  153.65124   86   59  217.00580  148.02440  31.8%  20.3 1085s\n",
      " 510818 423584     cutoff  148       217.00580  148.02833  31.8%  20.3 1090s\n",
      " 513642 425809  148.20559   55   54  217.00580  148.03267  31.8%  20.3 1095s\n",
      " 514772 426957  152.93334  170   27  217.00580  148.03422  31.8%  20.3 1100s\n",
      " 517454 429277  152.46810   56   76  217.00580  148.03665  31.8%  20.3 1105s\n",
      " 520933 432039  195.06019  116   16  217.00580  148.04021  31.8%  20.3 1110s\n",
      " 522309 433775  154.41316   96   15  217.00580  148.04238  31.8%  20.3 1115s\n",
      " 526734 436973  179.16467   92   23  217.00580  148.04872  31.8%  20.2 1120s\n",
      " 529891 439848  171.57730   90   35  217.00580  148.05511  31.8%  20.2 1125s\n",
      " 533136 442494  155.60811  134   28  217.00580  148.06086  31.8%  20.2 1130s\n",
      " 535823 444608  169.46075   93   36  217.00580  148.06337  31.8%  20.2 1135s\n",
      " 538275 446691  159.15555   98   19  217.00580  148.06669  31.8%  20.2 1140s\n",
      " 540716 449089  158.54296   90   65  217.00580  148.06989  31.8%  20.2 1145s\n",
      " 543720 451549  158.25778   76   87  217.00580  148.07505  31.8%  20.2 1150s\n",
      " 545724 453117  213.66344  189   14  217.00580  148.07682  31.8%  20.2 1155s\n",
      " 549312 456182  206.55381  142   41  217.00580  148.08159  31.8%  20.2 1160s\n",
      " 550538 457405  156.19282  131   58  217.00580  148.08336  31.8%  20.2 1165s\n",
      " 553472 459577 infeasible  232       217.00580  148.08606  31.8%  20.2 1170s\n",
      " 555993 461898  211.54093  202   25  217.00580  148.08941  31.8%  20.2 1175s\n",
      " 559331 464715  153.89464  111   63  217.00580  148.09313  31.8%  20.2 1180s\n",
      " 562215 467372  149.51094   65  123  217.00580  148.09617  31.8%  20.2 1185s\n",
      " 564728 469479  201.48220  129   19  217.00580  148.10197  31.8%  20.2 1190s\n",
      " 567295 471764  149.46412   61  102  217.00580  148.10512  31.8%  20.2 1195s\n",
      " 570340 474096  158.79564  131   47  217.00580  148.10643  31.8%  20.2 1200s\n",
      " 572466 476062  156.96649   73   49  217.00580  148.10940  31.7%  20.2 1205s\n",
      " 574369 477869  149.15401   55   14  217.00580  148.11143  31.7%  20.2 1210s\n",
      " 577504 480435  152.30134   63   87  217.00580  148.11380  31.7%  20.2 1215s\n",
      " 580891 483149  212.62957  134   26  217.00580  148.11622  31.7%  20.2 1220s\n",
      " 583219 484894  157.58400  171   44  217.00580  148.11782  31.7%  20.2 1232s\n",
      " 584141 486176  175.16913   84   47  217.00580  148.11859  31.7%  20.2 1235s\n",
      " 587776 489407  151.28976   68   40  217.00580  148.12395  31.7%  20.2 1240s\n",
      " 590442 491495  154.41777   90   29  217.00580  148.12829  31.7%  20.1 1245s\n",
      " 593298 494031  192.32448  204   48  217.00580  148.13183  31.7%  20.1 1250s\n",
      " 595819 496011  197.48167  106   52  217.00580  148.13592  31.7%  20.1 1255s\n",
      " 599374 498722  163.10609   85   29  217.00580  148.13823  31.7%  20.1 1260s\n",
      " 602020 501246  179.93734   67   31  217.00580  148.14190  31.7%  20.1 1265s\n",
      " 604686 503343  182.54916   79   21  217.00580  148.14611  31.7%  20.1 1270s\n",
      " 607879 506154  153.62703   94   43  217.00580  148.14936  31.7%  20.1 1276s\n",
      " 610502 508672  179.11575  130   39  217.00580  148.15233  31.7%  20.1 1280s\n",
      " 614314 511896  156.26469   73   64  217.00580  148.15558  31.7%  20.1 1285s\n",
      " 617400 514324  209.44902  121   14  217.00580  148.15818  31.7%  20.1 1290s\n",
      " 620327 516777  197.99428  145   25  217.00580  148.16183  31.7%  20.1 1295s\n",
      " 622957 519177  154.24545  123   47  217.00580  148.16360  31.7%  20.0 1300s\n",
      " 625537 521447  162.78851  145   30  217.00580  148.16964  31.7%  20.0 1305s\n",
      " 628690 524154  204.44375  151   16  217.00580  148.17421  31.7%  20.0 1310s\n",
      " 631232 525998  179.37589   85   43  217.00580  148.17844  31.7%  20.0 1315s\n",
      " 633937 528408  157.99772   87   23  217.00580  148.18276  31.7%  20.0 1320s\n",
      " 636169 530645  188.11630   77   26  217.00580  148.18712  31.7%  20.0 1325s\n",
      " 639781 533503  152.43019   72   48  217.00580  148.19108  31.7%  20.0 1330s\n",
      " 643745 536612  181.36573  112   35  217.00580  148.19514  31.7%  20.0 1335s\n",
      " 646369 538892  152.79723   59   21  217.00580  148.19860  31.7%  20.0 1340s\n",
      " 649835 541844  150.79969   49  138  217.00580  148.20334  31.7%  20.0 1345s\n",
      " 652905 544537  194.75819  155   41  217.00580  148.20728  31.7%  20.0 1350s\n",
      " 655798 547171  161.54317  118   19  217.00580  148.21056  31.7%  20.0 1355s\n",
      " 658524 549376  152.82108   66   34  217.00580  148.21420  31.7%  19.9 1360s\n",
      " 661287 551868  153.84267   77   40  217.00580  148.21677  31.7%  19.9 1365s\n",
      " 663861 554116  181.21207   84   10  217.00580  148.21946  31.7%  19.9 1370s\n",
      " 666582 556044  152.83361   74   17  217.00580  148.22338  31.7%  19.9 1375s\n",
      " 668363 557749  210.61779  102   34  217.00580  148.22530  31.7%  20.0 1380s\n",
      " 671196 559978  168.56520   55   82  217.00580  148.23089  31.7%  19.9 1385s\n",
      " 673649 562367  172.73634  192   37  217.00580  148.23259  31.7%  20.0 1390s\n",
      " 675480 563903  156.07675   77   34  217.00580  148.23524  31.7%  20.0 1395s\n",
      " 678225 566027  152.35229   65   43  217.00580  148.23903  31.7%  20.0 1400s\n",
      " 681064 568621  166.05171  147   43  217.00580  148.24445  31.7%  20.0 1405s\n",
      " 684287 571521  153.87458  226   51  217.00580  148.24685  31.7%  19.9 1410s\n",
      " 685724 572485  187.79265  193   52  217.00580  148.24725  31.7%  19.9 1415s\n",
      " 688959 575361  210.71804  175   12  217.00580  148.25038  31.7%  19.9 1420s\n",
      " 691988 577760  160.12057   63   29  217.00580  148.25815  31.7%  19.9 1425s\n",
      " 694610 579777  175.86794   99   52  217.00580  148.26105  31.7%  19.9 1430s\n",
      " 697562 582404  156.91717   64   20  217.00580  148.26569  31.7%  19.9 1435s\n",
      " 700926 585070  150.47039   59   55  217.00580  148.27123  31.7%  19.9 1440s\n",
      " 702818 586839  183.77123  107   17  217.00580  148.27489  31.7%  19.9 1445s\n",
      " 705897 589266  159.98863  121   49  217.00580  148.27501  31.7%  19.9 1450s\n",
      " 707610 590863  211.62581  105   36  217.00580  148.27779  31.7%  19.9 1455s\n",
      " 708431 591440  153.07250   98   15  217.00580  148.27818  31.7%  19.9 1460s\n",
      " 711423 593848  156.54112   60   33  217.00580  148.28001  31.7%  19.9 1465s\n",
      " 714287 596511  179.18734   99   28  217.00580  148.28238  31.7%  19.9 1470s\n",
      " 718048 599680  204.35084  106   22  217.00580  148.28778  31.7%  19.9 1475s\n",
      " 720906 602028  152.30703   81   40  217.00580  148.29056  31.7%  19.9 1480s\n",
      " 722652 603740  186.75314  188   37  217.00580  148.29267  31.7%  19.9 1485s\n",
      " 726161 606483  152.81290  130   20  217.00580  148.29541  31.7%  19.9 1490s\n",
      " 728473 608754  186.66917  118   30  217.00580  148.29774  31.7%  19.9 1495s\n",
      " 731717 611274  162.73725  229   21  217.00580  148.30059  31.7%  19.9 1500s\n",
      " 734568 613485  174.05963   89   16  217.00580  148.30403  31.7%  19.9 1505s\n",
      " 736751 615433  159.72353   75   50  217.00580  148.30611  31.7%  19.9 1510s\n",
      " 739658 618013  160.23283  117   41  217.00580  148.31011  31.7%  19.9 1515s\n",
      " 741101 618843  162.78315  151   46  217.00580  148.31011  31.7%  19.9 1520s\n",
      " 742759 620290  175.86538   94   53  217.00580  148.31011  31.7%  19.9 1525s\n",
      " 745637 622655  168.12537   88   25  217.00580  148.31456  31.7%  19.9 1530s\n",
      " 748195 624672  178.41951  164   29  217.00580  148.31562  31.7%  19.9 1535s\n",
      " 750499 626567  159.54259  137   24  217.00580  148.32073  31.7%  19.9 1540s\n",
      " 753066 628777  162.23910  113   26  217.00580  148.32427  31.6%  19.9 1545s\n",
      " 755865 631055  167.96248  151   42  217.00580  148.32799  31.6%  19.9 1550s\n",
      " 758620 633266  154.61567  104   52  217.00580  148.33121  31.6%  19.9 1555s\n",
      " 760899 635060  159.96041  126   56  217.00580  148.33327  31.6%  19.9 1560s\n",
      " 763388 637422  155.60001  108   49  217.00580  148.33558  31.6%  19.9 1565s\n",
      " 766153 639807  190.51550   68   68  217.00580  148.33836  31.6%  19.9 1570s\n",
      " 769520 642370  194.33970  164   39  217.00580  148.34498  31.6%  19.9 1575s\n",
      " 771236 643780  155.01269  113   45  217.00580  148.34644  31.6%  19.9 1580s\n",
      " 774293 646160  214.28647  112   23  217.00580  148.34979  31.6%  19.9 1585s\n",
      " 775985 647899  153.93441   98   75  217.00580  148.35146  31.6%  19.9 1590s\n",
      " 778239 649755  180.42772  114   34  217.00580  148.35426  31.6%  19.9 1595s\n",
      " 780944 651871  151.73467   76   51  217.00580  148.35613  31.6%  19.9 1600s\n",
      " 781941 652723  153.17115  119   22  217.00580  148.35613  31.6%  19.9 1605s\n",
      " 783912 654603  180.97353  223   14  217.00580  148.35784  31.6%  19.9 1610s\n",
      " 786436 656705  157.01851   66   38  217.00580  148.35970  31.6%  19.9 1615s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 789004 659117  199.36753  102   31  217.00580  148.36274  31.6%  19.9 1620s\n",
      " 792235 661388  155.68297  120   34  217.00580  148.36444  31.6%  19.9 1625s\n",
      " 794764 663584  158.75958  188   51  217.00580  148.36733  31.6%  19.9 1630s\n",
      " 796557 665278  157.22685  143   21  217.00580  148.36857  31.6%  19.9 1635s\n",
      " 798722 667286  152.18054   97   50  217.00580  148.36981  31.6%  19.9 1640s\n",
      " 801087 669015  156.68957   96   19  217.00580  148.37169  31.6%  19.9 1645s\n",
      " 804010 671827  185.70882  221   27  217.00580  148.37389  31.6%  19.9 1651s\n",
      " 806260 673375  154.30244   54   62  217.00580  148.37605  31.6%  19.9 1655s\n",
      " 808476 675496  161.88463  137   46  217.00580  148.37813  31.6%  19.9 1660s\n",
      " 811606 678029  166.28187   97   31  217.00580  148.37957  31.6%  19.8 1665s\n",
      " 813861 680243  169.52702  243   26  217.00580  148.38129  31.6%  19.8 1670s\n",
      " 815768 681861  182.89595   88   35  217.00580  148.38405  31.6%  19.8 1675s\n",
      " 817909 683362  159.14678   91   46  217.00580  148.38613  31.6%  19.9 1680s\n",
      " 819751 685489  163.58874  121   27  217.00580  148.38723  31.6%  19.9 1685s\n",
      " 822269 687026  171.19800  132   42  217.00580  148.39026  31.6%  19.9 1690s\n",
      " 824077 688737  174.89212  105   23  217.00580  148.39280  31.6%  19.9 1696s\n",
      " 825470 690266  158.06897  170   37  217.00580  148.39440  31.6%  19.9 1700s\n",
      " 828734 692856  168.12489   99   23  217.00580  148.39781  31.6%  19.9 1705s\n",
      " 831758 695357  150.78531   61   75  217.00580  148.40134  31.6%  19.9 1710s\n",
      " 834111 697411  158.59180  131   27  217.00580  148.40278  31.6%  19.8 1715s\n",
      " 836332 699307  204.81372  207   21  217.00580  148.40523  31.6%  19.8 1720s\n",
      " 838752 701576  156.62889   99   42  217.00580  148.40725  31.6%  19.8 1725s\n",
      " 840398 702659  190.55859  187   19  217.00580  148.40796  31.6%  19.8 1730s\n",
      " 842820 704874  149.28451   62   69  217.00580  148.40966  31.6%  19.8 1735s\n",
      " 844882 706451  156.60943  117   54  217.00580  148.41136  31.6%  19.8 1740s\n",
      " 846819 708327  173.73132   99   35  217.00580  148.41355  31.6%  19.8 1745s\n",
      " 849588 710928  206.73452  126   22  217.00580  148.41576  31.6%  19.8 1750s\n",
      " 852112 713004  153.56082   56   69  217.00580  148.41780  31.6%  19.8 1755s\n",
      " 854487 715080  150.83967   74   32  217.00580  148.42033  31.6%  19.8 1760s\n",
      " 857878 717790  152.42814  202   68  217.00580  148.42193  31.6%  19.8 1765s\n",
      " 860387 719528  149.75972   66   53  217.00580  148.42434  31.6%  19.8 1771s\n",
      " 861507 720906  183.64214  177   38  217.00580  148.42434  31.6%  19.8 1775s\n",
      " 863260 722115  160.02932   97   30  217.00580  148.42467  31.6%  19.8 1780s\n",
      " 865076 723876  181.59674   99   16  217.00580  148.42467  31.6%  19.8 1785s\n",
      " 867007 725236  171.83605   82   36  217.00580  148.42467  31.6%  19.8 1790s\n",
      " 869105 727231  155.29605   86   29  217.00580  148.42467  31.6%  19.8 1795s\n",
      " 871442 728646  156.88666   67   44  217.00580  148.42575  31.6%  19.8 1800s\n",
      "\n",
      "Cutting planes:\n",
      "  Learned: 7\n",
      "  Gomory: 43\n",
      "  Cover: 48\n",
      "  Clique: 2\n",
      "  MIR: 45\n",
      "  Flow cover: 243\n",
      "  GUB cover: 2\n",
      "  Inf proof: 38\n",
      "  Zero half: 181\n",
      "  RLT: 175\n",
      "\n",
      "Explored 871568 nodes (17300918 simplex iterations) in 1800.05 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 10: 217.006 217.984 218.316 ... 228.87\n",
      "\n",
      "Time limit reached\n",
      "Best objective 2.170060020933e+02, best bound 1.484257491967e+02, gap 31.6029%\n",
      "Changed value of parameter timelimit to 1800.0\n",
      "   Prev: inf  Min: 0.0  Max: inf  Default: inf\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 521 rows, 556 columns and 3492 nonzeros\n",
      "Model fingerprint: 0x76e772cb\n",
      "Variable types: 10 continuous, 546 integer (546 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 4e+01]\n",
      "  Objective range  [2e+00, 8e+01]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 4e+02]\n",
      "Presolve removed 183 rows and 180 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 338 rows, 376 columns, 2202 nonzeros\n",
      "Variable types: 10 continuous, 366 integer (366 binary)\n",
      "\n",
      "Root relaxation: objective 6.764151e+01, 24 iterations, 0.00 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0   67.64151    0   20          -   67.64151      -     -    0s\n",
      "H    0     0                     475.7136489   67.64151  85.8%     -    0s\n",
      "H    0     0                      90.9991417   67.64151  25.7%     -    0s\n",
      "     0     0   68.56669    0   20   90.99914   68.56669  24.7%     -    0s\n",
      "H    0     0                      83.3563139   68.56669  17.7%     -    0s\n",
      "     0     0   80.59407    0   22   83.35631   80.59407  3.31%     -    0s\n",
      "     0     0   80.59407    0   20   83.35631   80.59407  3.31%     -    0s\n",
      "     0     0   80.59407    0   20   83.35631   80.59407  3.31%     -    0s\n",
      "     0     0   80.59407    0   20   83.35631   80.59407  3.31%     -    0s\n",
      "     0     0   80.59407    0   44   83.35631   80.59407  3.31%     -    0s\n",
      "     0     0   80.59407    0   24   83.35631   80.59407  3.31%     -    0s\n",
      "     0     0   80.59407    0   22   83.35631   80.59407  3.31%     -    0s\n",
      "H    0     0                      82.6809885   80.83556  2.23%     -    0s\n",
      "H    0     0                      81.9464357   80.84583  1.34%     -    0s\n",
      "     0     0   80.84583    0   31   81.94644   80.84583  1.34%     -    0s\n",
      "     0     0   81.09759    0   20   81.94644   81.09759  1.04%     -    0s\n",
      "\n",
      "Cutting planes:\n",
      "  Learned: 4\n",
      "  Gomory: 9\n",
      "  Clique: 6\n",
      "  MIR: 26\n",
      "  Inf proof: 1\n",
      "  RLT: 36\n",
      "\n",
      "Explored 1 nodes (473 simplex iterations) in 0.15 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 5: 81.9464 82.681 83.3563 ... 475.714\n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 8.194643574011e+01, best bound 8.194643574011e+01, gap 0.0000%\n"
     ]
    }
   ],
   "source": [
    "#-----\n",
    "# Running VPR for each cluster\n",
    "cl_routes, cl_X, cl_Y, cl_obj_val, cl_veh_routes = [], [], [], [], []\n",
    "colorz = colors[:len(depot_nodes)]\n",
    "for l, color in enumerate(colorz):\n",
    "    sm_cl = cluster[cluster['cluster']==l].reset_index(drop=True)\n",
    "    #A = [(sm_cl.x[i], sm_cl.y[i]) for i in range(sm_cl.shape[0])]\n",
    "    \n",
    "    custx = np.array(sm_cl.x)\n",
    "    custy = np.array(sm_cl.y)\n",
    "    custd = np.array(sm_cl.demand)\n",
    "    #depot_x1, depot_y1 = [depot_nodes[l][0]], [depot_nodes[l][1]]\n",
    "    \n",
    "    veh = [vehicles[i] for i in range(len(vehicles)) if veh_depot[i]==l]\n",
    "    veh_dep = [l for i in range(len(veh))]\n",
    "    veh_cap = [400 for i in range(len(veh))]\n",
    "    \n",
    "    #rout, x, y, obj, veh = CVRP(custx, custy, custd, depot, veh, 100)\n",
    "    time_limit = 1800\n",
    "    rout, route_veh, rout_dep, x, y, vrp_depot, obj = MDCVRP(custx, custy, custd, depot_x, depot_y, \n",
    "                                                                     veh, veh_dep, veh_cap, time_limit)\n",
    "    cl_routes.append(rout)\n",
    "    cl_X.append(x)\n",
    "    cl_Y.append(y)\n",
    "    cl_obj_val.append(obj)\n",
    "    cl_veh_routes.append(route_veh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a58b0614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "Sum of overall objective funtion value - 790\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depot Nodes -  [[20, 50], [73, 80], [89, 29], [93, 12]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#-----\n",
    "# Plotting results of Clustered VRP\n",
    "print('-----\\nSum of overall objective funtion value -', sum(cl_obj_val))\n",
    "for l, color in enumerate(colorz):\n",
    "    for r in range(len(cl_routes[l])):\n",
    "        for n in range(len(cl_routes[l][r])-1):\n",
    "            i = cl_routes[l][r][n]\n",
    "            j = cl_routes[l][r][n+1]\n",
    "            plt.plot([cl_X[l][i], cl_X[l][j]], [cl_Y[l][i], cl_Y[l][j]], color=color)\n",
    "    plt.scatter(cl_X[l][1:], cl_Y[l][1:], color=color, marker='.', label='Depot '+str(l+1))\n",
    "#plt.scatter(cl_X[l][0], cl_Y[l][0], color='red', marker='x')\n",
    "plt.scatter(depot_x, depot_y, marker='x', color='r')\n",
    "plt.legend()\n",
    "plt.title('Network Graph of Multi-Depot Vehicle Routing with k-Means Clustering')\n",
    "plt.show()\n",
    "print('Depot Nodes - ', depot_nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715ae50d",
   "metadata": {},
   "source": [
    "#### _____\n",
    "### References\n",
    "\n",
    "* https://stats.stackexchange.com/questions/109547/k-means-vs-k-median\n",
    "* https://stackoverflow.com/questions/38355153/initial-centroids-for-scikit-learn-kmeans-clustering"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
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