Airliner-twostage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyomo.environ import *\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V</th>\n",
       "      <th>Prob</th>\n",
       "      <th>F</th>\n",
       "      <th>B</th>\n",
       "      <th>E</th>\n",
       "      <th>Space</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S1</td>\n",
       "      <td>0.15</td>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>210</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S2</td>\n",
       "      <td>0.30</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>170</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S4</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    V  Prob   F   B    E  Space   Value\n",
       "0  S1  0.15  25  60  210    2.0  1600.0\n",
       "1  S2  0.30  12  30  170    1.5  1100.0\n",
       "2  S3  0.30   5   9  150    1.0   500.0\n",
       "3  S4  0.15   7  20  120    NaN     NaN\n",
       "4  S5  0.10  20  18  160    NaN     NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Airliner-v2.csv\", delimiter=',', skiprows=0, low_memory=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nspace=200\n",
    "model = AbstractModel()\n",
    "model.s = RangeSet(1,5)\n",
    "model.clas = RangeSet(1,3)\n",
    "model.x = Var(model.clas, initialize=0, bounds=(0,Nspace),domain=NonNegativeIntegers)\n",
    "model.S = Var(model.s,model.clas, initialize=0, bounds=(0,Nspace),domain=NonNegativeIntegers)\n",
    "\n",
    "model.E = Var( initialize=0, bounds=(0,1000000),domain=Reals)\n",
    "\n",
    "model.pr =Param(model.s,mutable=True, initialize=1) \n",
    "model.V =Param(model.clas,mutable=True, initialize=1) \n",
    "model.coef =Param(model.clas,mutable=True, initialize=1) \n",
    "def rue_c1(model):\n",
    "    return sum(model.coef[c]*model.x[c] for c in model.clas)<=Nspace\n",
    "model.C1 =Constraint(rule=rue_c1)\n",
    "def rue_c2(model,s,c):\n",
    "    return model.S[s,c] <= df.iloc[s-1,c+1]\n",
    "model.C2 =Constraint(model.s, model.clas,rule=rue_c2)\n",
    "def rue_c2A(model,s,c):\n",
    "    return model.S[s,c] <= model.x[c]\n",
    "model.C2A =Constraint(model.s,model.clas,rule=rue_c2A)\n",
    "\n",
    "def rue_c3A(model):\n",
    "    return model.E==sum(model.pr[s]*model.V[c]*model.S[s,c] for s in model.s for c in model.clas)\n",
    "model.C3A =Constraint(rule=rue_c3A)\n",
    "\n",
    "model.obj = Objective(rule= model.E, sense=maximize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is feasible and optimal\n"
     ]
    }
   ],
   "source": [
    "instance = model.create_instance()\n",
    "\n",
    "opt = SolverFactory('cbc')\n",
    "results=opt.solve(instance)\n",
    "\n",
    "from pyomo.opt import SolverStatus, TerminationCondition\n",
    "if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\n",
    "     print (\"this is feasible and optimal\")\n",
    "elif results.solver.termination_condition == TerminationCondition.infeasible:\n",
    "     print (\"do something about it? or exit?\")\n",
    "else:\n",
    "     print (str(results.solver))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 12.0 12.0\n",
      "2 28.0 28.0\n",
      "3 160.0 160.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "for c in instance.clas:\n",
    "    print(c,value(instance.x[c]), value(instance.coef[c]*instance.x[c]))\n",
    "    \n",
    "plt.subplot(2,1,1)\n",
    "    \n",
    "Seats=[value(instance.x[c]) for c in instance.clas ]\n",
    "cls=[c for c in instance.clas]\n",
    "plt.bar(cls,Seats,  color=['red','gold','cornflowerblue'])\n",
    "plt.xticks(cls, ['First' ,'Business' ,'Economy'], fontweight='bold')  \n",
    "plt.yticks(fontweight='bold')  \n",
    "plt.ylabel('Dedicated seats', fontweight='bold')\n",
    "plt.xlim(0,4)\n",
    "plt.grid()\n",
    "plt.subplot(2,1,2)\n",
    "\n",
    "for c in instance.clas:\n",
    "    for s in instance.s:\n",
    "        plt.bar(c+0.2-0.08*s,value(instance.S[s,c]), width=0.07)\n",
    "    \n",
    "plt.xticks(cls, ['First' ,'Business' ,'Economy'], fontweight='bold')  \n",
    "plt.yticks(fontweight='bold')  \n",
    "\n",
    "plt.ylabel('Sold seats', fontweight='bold')\n",
    "plt.xlim(0,4)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}