Notebooks/Europe Cities_VRP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pulp import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gurobipy in c:\\users\\owner\\anaconda3\\lib\\site-packages (10.0.1)\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 23.1.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ffi (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ffi (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ffi (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ffi (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ffi (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ffi (c:\\users\\owner\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "pip install gurobipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Barcelona</td>\n",
       "      <td>41.3888</td>\n",
       "      <td>2.1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Belgrade</td>\n",
       "      <td>44.8040</td>\n",
       "      <td>20.4651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Berlin</td>\n",
       "      <td>52.5244</td>\n",
       "      <td>13.4105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brussels</td>\n",
       "      <td>50.8505</td>\n",
       "      <td>4.3488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bucharest</td>\n",
       "      <td>44.4323</td>\n",
       "      <td>26.1063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           latitude  longitude\n",
       "City                          \n",
       "Barcelona   41.3888     2.1590\n",
       "Belgrade    44.8040    20.4651\n",
       "Berlin      52.5244    13.4105\n",
       "Brussels    50.8505     4.3488\n",
       "Bucharest   44.4323    26.1063"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a handful of sites\n",
    "sites = ['Barcelona','Belgrade','Berlin','Brussels','Bucharest','Budapest','Copenhagen','Dublin','Hamburg','Istanbul','Kiev','London','Madrid','Milan','Moscow','Munich','Paris','Prague','Rome','Saint Petersburg','Sofia','Stockholm','Vienna','Warsaw']\n",
    "# sites = ['Barcelona','Belgrade','Berlin','Brussels','Bucharest','Budapest','Copenhagen']\n",
    "latlng = ['latitude', 'longitude']\n",
    "position = pd.read_csv('./data/position.csv', index_col=\"City\")\n",
    "flighttime = pd.read_csv('./data/flight_time.csv', index_col=\"City\")\n",
    "distance = pd.read_csv('./data/distance.csv', index_col=\"City\")\n",
    "position.head(5)\n",
    "# print(len(sites))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "#make some positions (so we can plot this)\n",
    "positions = dict( ( city, (position.loc[city, 'longitude'], position.loc[city, 'latitude']) ) for city in sites)\n",
    "for s in positions:\n",
    "    p = positions[s]\n",
    "    plt.plot(p[0],p[1],'o')\n",
    "    plt.text(p[0]+.01,p[1],s,horizontalalignment='left',verticalalignment='center')\n",
    "    \n",
    "plt.gca().axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get distanc between cities\n",
    "distances = dict( ((s1,s2), distance.loc[s1, s2] ) for s1 in positions for s2 in positions if s1!=s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "K = 4 #the number of sales people \n",
    "#create the problme\n",
    "prob=LpProblem(\"vehicle\", LpMinimize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#indicator variable if site i is connected to site j in the tour\n",
    "x = LpVariable.dicts('x',distances, 0,1,LpBinary)\n",
    "#dummy vars to eliminate subtours\n",
    "u = LpVariable.dicts('u', sites, 0, len(sites)-1, LpInteger)\n",
    "#the objective\n",
    "cost = lpSum([x[(i,j)]*distances[(i,j)] for (i,j) in distances])\n",
    "prob+=cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#constraints\n",
    "for k in sites:\n",
    "    cap = 1 if k != 'Berlin' else K\n",
    "    #inbound connection\n",
    "    prob+= lpSum([ x[(i,k)] for i in sites if (i,k) in x]) ==cap\n",
    "    #outbound connection\n",
    "    prob+=lpSum([ x[(k,i)] for i in sites if (k,i) in x]) ==cap\n",
    "    \n",
    "#subtour elimination\n",
    "N=len(sites)/K\n",
    "for i in sites:\n",
    "    for j in sites:\n",
    "        if i != j and (i != 'Berlin' and j!= 'Berlin') and (i,j) in x:\n",
    "            prob += u[i] - u[j] <= (N)*(1-x[(i,j)]) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1h 4min 4s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time prob.solve()\n",
    "#prob.solve(solver=GLPK_CMD(options=['--simplex']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_zero_edges = [ e for e in x if value(x[e]) != 0 ]\n",
    "\n",
    "def get_next_site(parent):\n",
    "    '''helper function to get the next edge'''\n",
    "    edges = [e for e in non_zero_edges if e[0]==parent]\n",
    "    for e in edges:\n",
    "        non_zero_edges.remove(e)\n",
    "    return edges\n",
    "tours = get_next_site('Berlin')\n",
    "tours = [ [e] for e in tours ]\n",
    "\n",
    "for t in tours:\n",
    "    while t[-1][1] !='Berlin':\n",
    "        t.append(get_next_site(t[-1][1])[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Berlin -> Brussels -> Paris -> London -> Dublin -> Hamburg -> Berlin\n",
      "Berlin -> Copenhagen -> Stockholm -> Saint Petersburg -> Moscow -> Kiev -> Warsaw -> Berlin\n",
      "Berlin -> Prague -> Rome -> Barcelona -> Madrid -> Milan -> Munich -> Berlin\n",
      "Berlin -> Vienna -> Belgrade -> Sofia -> Istanbul -> Bucharest -> Budapest -> Berlin\n"
     ]
    }
   ],
   "source": [
    "for t in tours:\n",
    "    print(' -> '.join([ a for a,b in t]+['Berlin']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1988\n"
     ]
    }
   ],
   "source": [
    "totalTime = 0;\n",
    "for t in tours:\n",
    "    time = 0\n",
    "    for i in range(0, len(t)):\n",
    "        time += flighttime.loc[t[i][0], t[i][1]]\n",
    "#         print(flighttime.loc[t[i][0], t[i][1]])\n",
    "#     print(time)\n",
    "    if time > totalTime:\n",
    "        totalTime = time\n",
    "print(totalTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "#draw the tours\n",
    "colors = [np.random.rand(3) for i in range(len(tours))]\n",
    "for t,c in zip(tours,colors):\n",
    "    for a,b in t:\n",
    "        p1,p2 = positions[a], positions[b]\n",
    "        plt.plot([p1[0],p2[0]],[p1[1],p2[1]], color=c)\n",
    "\n",
    "#draw the map again\n",
    "for s in positions:\n",
    "    p = positions[s]\n",
    "    plt.plot(p[0],p[1],'o')\n",
    "    plt.text(p[0]+.01,p[1],s,horizontalalignment='left',verticalalignment='center')\n",
    "    \n",
    "plt.title('%d '%K + 'people' if K > 1 else 'person')\n",
    "plt.xlabel('latitude')\n",
    "plt.ylabel('longitude')\n",
    "# plt.gca().axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'totalTime' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-1d0a64cfab78>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Longest time spent:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtotalTime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'(min)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Total distance:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'(km)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'totalTime' is not defined"
     ]
    }
   ],
   "source": [
    "print('Longest time spent:', totalTime, '(min)')\n",
    "print('Total distance:', value(prob.objective), '(km)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('my_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}