supply chain optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pulp import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plant Location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manufacturing variable costs"
   ]
  },
  {
   "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>NewYork</th>\n",
       "      <th>Berlin</th>\n",
       "      <th>Tokyo</th>\n",
       "      <th>SaoPaulo</th>\n",
       "      <th>delhi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable Costs ($/Unit)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaoPaulo</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delhi</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         NewYork  Berlin  Tokyo  SaoPaulo  delhi\n",
       "Variable Costs ($/Unit)                                         \n",
       "NewYork                       12      12     12        12     12\n",
       "Berlin                        13      13     13        13     13\n",
       "Tokyo                         10      10     10        10     10\n",
       "SaoPaulo                       8       8      8         8      8\n",
       "delhi                          5       5      5         5      5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Costs\n",
    "manvar_costs = pd.read_excel('C:/Users/anmul/Downloads/supply optimization/variable_costs.xlsx', index_col = 0)\n",
    "manvar_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>NewYork</th>\n",
       "      <th>Berlin</th>\n",
       "      <th>Tokyo</th>\n",
       "      <th>SaoPaulo</th>\n",
       "      <th>delhi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freight Costs ($/Container)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>0</td>\n",
       "      <td>12250</td>\n",
       "      <td>1100</td>\n",
       "      <td>16100</td>\n",
       "      <td>8778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>13335</td>\n",
       "      <td>0</td>\n",
       "      <td>8617</td>\n",
       "      <td>20244</td>\n",
       "      <td>10073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>15400</td>\n",
       "      <td>22750</td>\n",
       "      <td>0</td>\n",
       "      <td>43610</td>\n",
       "      <td>14350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaoPaulo</th>\n",
       "      <td>16450</td>\n",
       "      <td>22050</td>\n",
       "      <td>28000</td>\n",
       "      <td>0</td>\n",
       "      <td>29750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delhi</th>\n",
       "      <td>13650</td>\n",
       "      <td>15400</td>\n",
       "      <td>24500</td>\n",
       "      <td>29400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             NewYork  Berlin  Tokyo  SaoPaulo  delhi\n",
       "Freight Costs ($/Container)                                         \n",
       "NewYork                            0   12250   1100     16100   8778\n",
       "Berlin                         13335       0   8617     20244  10073\n",
       "Tokyo                          15400   22750      0     43610  14350\n",
       "SaoPaulo                       16450   22050  28000         0  29750\n",
       "delhi                          13650   15400  24500     29400      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Costs\n",
    "freight_costs = pd.read_excel('C:/Users/anmul/Downloads/supply optimization/freight_costs.xlsx', index_col = 0)\n",
    "freight_costs"
   ]
  },
  {
   "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>NewYork</th>\n",
       "      <th>Berlin</th>\n",
       "      <th>Tokyo</th>\n",
       "      <th>SaoPaulo</th>\n",
       "      <th>delhi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freight Costs ($/Container)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>12.000</td>\n",
       "      <td>24.25</td>\n",
       "      <td>13.100</td>\n",
       "      <td>28.100</td>\n",
       "      <td>20.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>26.335</td>\n",
       "      <td>13.00</td>\n",
       "      <td>21.617</td>\n",
       "      <td>33.244</td>\n",
       "      <td>23.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>25.400</td>\n",
       "      <td>32.75</td>\n",
       "      <td>10.000</td>\n",
       "      <td>53.610</td>\n",
       "      <td>24.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaoPaulo</th>\n",
       "      <td>24.450</td>\n",
       "      <td>30.05</td>\n",
       "      <td>36.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>37.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delhi</th>\n",
       "      <td>18.650</td>\n",
       "      <td>20.40</td>\n",
       "      <td>29.500</td>\n",
       "      <td>34.400</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             NewYork  Berlin   Tokyo  SaoPaulo   delhi\n",
       "Freight Costs ($/Container)                                           \n",
       "NewYork                       12.000   24.25  13.100    28.100  20.778\n",
       "Berlin                        26.335   13.00  21.617    33.244  23.073\n",
       "Tokyo                         25.400   32.75  10.000    53.610  24.350\n",
       "SaoPaulo                      24.450   30.05  36.000     8.000  37.750\n",
       "delhi                         18.650   20.40  29.500    34.400   5.000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variable Costs\n",
    "var_cost = freight_costs/1000 + manvar_costs \n",
    "\n",
    "var_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Low</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>6500</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>4980</td>\n",
       "      <td>7270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6230</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaoPaulo</th>\n",
       "      <td>3230</td>\n",
       "      <td>4730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delhi</th>\n",
       "      <td>2110</td>\n",
       "      <td>6160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Low  High\n",
       "NewYork   6500  9500\n",
       "Berlin    4980  7270\n",
       "Tokyo     6230  9100\n",
       "SaoPaulo  3230  4730\n",
       "delhi     2110  6160"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Costs\n",
    "fixed_costs = pd.read_excel('C:/Users/anmul/Downloads/supply optimization/fixed_cost.xlsx', index_col = 0)\n",
    "fixed_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Low</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capacity (kUnits/month)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaoPaulo</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delhi</th>\n",
       "      <td>500</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Low  High\n",
       "Capacity (kUnits/month)           \n",
       "NewYork                  500  1500\n",
       "Berlin                   500  1500\n",
       "Tokyo                    500  1500\n",
       "SaoPaulo                 500  1500\n",
       "delhi                    500  3000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Two types of plants: Low Capacity and High Capacity Plant\n",
    "cap = pd.read_excel('C:/Users/anmul/Downloads/supply optimization/capacity.xlsx', index_col = 0)\n",
    "cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Demand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Units/month)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>2800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>1700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaoPaulo</th>\n",
       "      <td>145000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delhi</th>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Demand\n",
       "(Units/month)         \n",
       "NewYork        2800000\n",
       "Berlin           90000\n",
       "Tokyo          1700000\n",
       "SaoPaulo        145000\n",
       "delhi           160000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- Demand\n",
    "demand = pd.read_excel('C:/Users/anmul/Downloads/supply optimization/demand.xlsx', index_col = 0)\n",
    "demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Costs = 92,981,000 ($/Month)\n",
      "\n",
      "Status: Optimal\n",
      "('Berlin','High') = 0.0\n",
      "('Berlin','Low') = 0.0\n",
      "('NewYork','High') = 1.0\n",
      "('NewYork','Low') = 0.0\n",
      "('SaoPaulo','High') = 0.0\n",
      "('SaoPaulo','Low') = 1.0\n",
      "('Tokyo','High') = 1.0\n",
      "('Tokyo','Low') = 0.0\n",
      "('delhi','High') = 1.0\n",
      "('delhi','Low') = 0.0\n",
      "('Berlin','Berlin') = 0.0\n",
      "('Berlin','NewYork') = 0.0\n",
      "('Berlin','SaoPaulo') = 0.0\n",
      "('Berlin','Tokyo') = 0.0\n",
      "('Berlin','delhi') = 0.0\n",
      "('NewYork','Berlin') = 0.0\n",
      "('NewYork','NewYork') = 1300000.0\n",
      "('NewYork','SaoPaulo') = 0.0\n",
      "('NewYork','Tokyo') = 200000.0\n",
      "('NewYork','delhi') = 0.0\n",
      "('SaoPaulo','Berlin') = 0.0\n",
      "('SaoPaulo','NewYork') = 0.0\n",
      "('SaoPaulo','SaoPaulo') = 145000.0\n",
      "('SaoPaulo','Tokyo') = 0.0\n",
      "('SaoPaulo','delhi') = 0.0\n",
      "('Tokyo','Berlin') = 0.0\n",
      "('Tokyo','NewYork') = 0.0\n",
      "('Tokyo','SaoPaulo') = 0.0\n",
      "('Tokyo','Tokyo') = 1500000.0\n",
      "('Tokyo','delhi') = 0.0\n",
      "('delhi','Berlin') = 90000.0\n",
      "('delhi','NewYork') = 1500000.0\n",
      "('delhi','SaoPaulo') = 0.0\n",
      "('delhi','Tokyo') = 0.0\n",
      "('delhi','delhi') = 160000.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anmul\\anaconda3.6\\lib\\site-packages\\pulp\\pulp.py:1352: UserWarning: Spaces are not permitted in the name. Converted to '_'\n",
      "  warnings.warn(\"Spaces are not permitted in the name. Converted to '_'\")\n"
     ]
    }
   ],
   "source": [
    "# Define Decision Variables\n",
    "loc = ['NewYork', 'Berlin', 'Tokyo', 'SaoPaulo', 'delhi']\n",
    "size = ['Low', 'High']\n",
    "\n",
    "# Initialize Class\n",
    "model = LpProblem(\"Capacitated Plant Location Model\", LpMinimize)\n",
    "\n",
    "\n",
    "# Create Decision Variables\n",
    "x = LpVariable.dicts(\"production_\", [(i,j) for i in loc for j in loc],\n",
    "                     lowBound=0, upBound=None, cat='continuous')\n",
    "y = LpVariable.dicts(\"plant_\", \n",
    "                     [(i,s) for s in size for i in loc], cat='Binary')\n",
    "\n",
    "# Define Objective Function\n",
    "model += (lpSum([fixed_costs.loc[i,s] * y[(i,s)] * 1000 for s in size for i in loc])\n",
    "          + lpSum([var_cost.loc[i,j] * x[(i,j)]   for i in loc for j in loc]))\n",
    "\n",
    "# Add Constraints\n",
    "for j in loc:\n",
    "    model += lpSum([x[(i, j)] for i in loc]) == demand.loc[j,'Demand']\n",
    "for i in loc:\n",
    "    model += lpSum([x[(i, j)] for j in loc]) <= lpSum([cap.loc[i,s]*y[(i,s)] * 1000\n",
    "                                                       for s in size])\n",
    "\n",
    "\n",
    "# Define logical constraint: Add a logical constraint so that if the high capacity plant in USA is open, then a low capacity plant in Germany is also opened.\n",
    "# model += y[('USA','High_Cap')] <= y[('Germany','Low_Cap')]                                                       \n",
    "                                                       \n",
    "# Solve Model\n",
    "model.solve()\n",
    "print(\"Total Costs = {:,} ($/Month)\".format(int(value(model.objective))))\n",
    "print('\\n' + \"Status: {}\".format(LpStatus[model.status]))\n",
    "\n",
    "\n",
    "# Dictionnary\n",
    "dict_plant = {}\n",
    "dict_prod = {}\n",
    "for v in model.variables():\n",
    "    if 'plant' in v.name:\n",
    "        name = v.name.replace('plant__', '').replace('_', '')\n",
    "        dict_plant[name] = int(v.varValue)\n",
    "        p_name = name\n",
    "    else:\n",
    "        name = v.name.replace('production__', '').replace('_', '')\n",
    "        dict_prod[name] = v.varValue\n",
    "    print(name, \"=\", v.varValue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Low</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaoPaulo</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delhi</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Low  High\n",
       "Location           \n",
       "NewYork     0     1\n",
       "Berlin      0     0\n",
       "Tokyo       0     1\n",
       "SaoPaulo    1     0\n",
       "delhi       0     1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Capacity Plant\n",
    "list_low, list_high = [], []\n",
    "for l in loc:\n",
    "    for cap in ['Low', 'High']:\n",
    "        x = \"('{}','{}')\".format(l, cap)\n",
    "        if cap == 'Low':\n",
    "            list_low.append(dict_plant[x])\n",
    "        else:\n",
    "            list_high.append(dict_plant[x])\n",
    "df_capacity = pd.DataFrame({'Location': loc, 'Low': list_low, 'High': list_high}).set_index('Location')\n",
    "    \n",
    "df_capacity"
   ]
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}