SolveForWings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max orders for three digits of wings: 7.0\n"
     ]
    }
   ],
   "source": [
    "from ortools.linear_solver import pywraplp\n",
    "\n",
    "COUNTS = [\n",
    "    4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, \n",
    "    24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 75, 80, 90, 100, 125, \n",
    "    150, 200\n",
    "]\n",
    "    \n",
    "# PRICES = dict(\n",
    "#     zip(\n",
    "#         wingCounts,\n",
    "#         [\n",
    "#             4.55, 5.70, 6.80, 7.95, 9.10, 10.20, 11.35, 12.50, 13.60, 14.75, 15.90, 17.00, \n",
    "#             18.15, 19.30, 20.40, 21.55, 22.70, 23.80, 24.95, 26.10, 27.25, 27.80, 28.95, \n",
    "#             30.10, 31.20, 32.35, 33.50, 39.15, 44.80, 50.50, 55.60, 67.00, 78.30, 83.45, \n",
    "#             89.10, 100.45, 111.25, 139.00, 166.85, 222.50\n",
    "#         ]\n",
    "#     )\n",
    "# )\n",
    "\n",
    "def solver_for_wings(n):\n",
    "    solver = pywraplp.Solver(\n",
    "        'SolveIntegerProblem',\n",
    "        pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING\n",
    "    )\n",
    "    objective = solver.Objective()\n",
    "    constraint = solver.Constraint(n, n)\n",
    "    for count in COUNTS:\n",
    "        xn = solver.IntVar(0, n, 'x%d' % count)\n",
    "        objective.SetCoefficient(xn, 1)\n",
    "        constraint.SetCoefficient(xn, count)\n",
    "    objective.SetMinimization()\n",
    "    result_status = solver.Solve()\n",
    "    assert result_status == pywraplp.Solver.OPTIMAL\n",
    "    return solver\n",
    "\n",
    "solvers = [solver_for_wings(n) for n in range(100, 1000)]\n",
    "solutions = [solver.Objective().Value() for solver in solvers]\n",
    "\n",
    "print('Max orders for three digits of wings: {}'.format(max(solutions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel('Orders')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.hist(solutions)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 x 4 wings\n",
      "1 x 27 wings\n",
      "1 x 150 wings\n",
      "4 x 200 wings\n",
      "= 981 wings\n"
     ]
    }
   ],
   "source": [
    "solver = [solver for solver, solution in zip(solvers, solutions) if solution == 7][0]\n",
    "n = 0\n",
    "for count in COUNTS:\n",
    "    xn = solver.LookupVariable('x%d' % count)\n",
    "    c = int(xn.solution_value())\n",
    "    if c > 0:\n",
    "        print('{} x {} wings'.format(c, count))\n",
    "        n += count * c\n",
    "print('= {} wings'.format(n))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}