doc/3-build-solver.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build New Solver\n",
    "\n",
    "To develop your new solver, inherit from `JSSolver` and implement method `do_solver()`.\n",
    "\n",
    "1. Initialize an empty solution and specify solving mode.\n",
    "\n",
    "    * direct_mode=True, solve start time directly;\n",
    "\n",
    "    * direct_mode=False, solve operations sequence first and deduce start time.\n",
    "\n",
    "    ```python\n",
    "    solution = self.init_solution(direct_mode=False)\n",
    "    ```\n",
    "\n",
    "2. Solve or optimize the solution.\n",
    "\n",
    "    - if direct_mode=True, i.e., **mathematical model**, solve `start_time` and assign to each operation explicitly.\n",
    "\n",
    "    ```python\n",
    "    for op in solution.ops:\n",
    "        op.update_start_time(start_time)\n",
    "    ``` \n",
    "\n",
    "    - direct_mode=False, e.g., **disjunctive graph model**, dispatch each operation according to solved sequence.\n",
    "\n",
    "    ```python\n",
    "    for op in solved_sequence_ops:\n",
    "        solution.dispatch(op=op)\n",
    "    ```\n",
    "\n",
    "3. Update solution.\n",
    "\n",
    "    call the following method when a better solution is found, which triggers updating Gantt chart dynamically and running user defined callback.\n",
    "    \n",
    "    ```python\n",
    "    self.update_solution(solution)\n",
    "    ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jsp import (JSProblem, JSSolver)\n",
    "\n",
    "\n",
    "class UserSolver(JSSolver):\n",
    "    '''Sample solver.'''\n",
    "\n",
    "    def do_solve(self):\n",
    "        '''A sample solving process: the sequence of operations assigned to each machine\n",
    "        is determined by job id. Obviously, it's a feasible but far from optimal solution.\n",
    "        '''\n",
    "        # (1) Initialize an empty solution from problem\n",
    "        solution = self.init_solution(direct_mode=False)\n",
    "\n",
    "        # (2) Solve or optimize the solution, i.e., determine the start_time of\n",
    "        # OperationStep instances. In this case, just sort operations by job id,\n",
    "        # then create chain accordingly.\n",
    "        ops = sorted(solution.ops, key=lambda op: op.source.job.id)\n",
    "        solution.dispatch(ops=ops)\n",
    "        \n",
    "        # (3) set final solution explicitly\n",
    "        self.update_solution(solution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimum: 666\n",
      "solved makespan: 2272.0\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = JSProblem(benchmark='la01')\n",
    "s = UserSolver(problem=p)\n",
    "s.solve()\n",
    "s.wait()\n",
    "print('optimum:', p.optimum)\n",
    "print('solved makespan:', s.solution.makespan)\n",
    "s.solution.plot_gantt_chart()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}