Supply Chain Optimization.ipynb

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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pulp import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Plant Location  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manufacturing variable costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USA</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</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>USA</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>Germany</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>Japan</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>Brazil</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>India</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",
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      "text/plain": [
       "         USA  Germany  Japan  Brazil  India\n",
       "Country                                    \n",
       "USA       12       12     12      12     12\n",
       "Germany   13       13     13      13     13\n",
       "Japan     10       10     10      10     10\n",
       "Brazil     8        8      8       8      8\n",
       "India      5        5      5       5      5"
      ]
     },
     "execution_count": 169,
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   ],
   "source": [
    "# Import Costs\n",
    "manvar_costs = pd.read_excel('variable_costs.xlsx', index_col = 0)\n",
    "manvar_costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Freight costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USA</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freight Costs ($/Container)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0</td>\n",
       "      <td>12250</td>\n",
       "      <td>1100</td>\n",
       "      <td>16100</td>\n",
       "      <td>8778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>13335</td>\n",
       "      <td>0</td>\n",
       "      <td>8617</td>\n",
       "      <td>20244</td>\n",
       "      <td>10073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>15400</td>\n",
       "      <td>22750</td>\n",
       "      <td>0</td>\n",
       "      <td>43610</td>\n",
       "      <td>14350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>16450</td>\n",
       "      <td>22050</td>\n",
       "      <td>28000</td>\n",
       "      <td>0</td>\n",
       "      <td>29750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>13650</td>\n",
       "      <td>15400</td>\n",
       "      <td>24500</td>\n",
       "      <td>29400</td>\n",
       "      <td>0</td>\n",
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       "                               USA  Germany  Japan  Brazil  India\n",
       "Freight Costs ($/Container)                                      \n",
       "USA                              0    12250   1100   16100   8778\n",
       "Germany                      13335        0   8617   20244  10073\n",
       "Japan                        15400    22750      0   43610  14350\n",
       "Brazil                       16450    22050  28000       0  29750\n",
       "India                        13650    15400  24500   29400      0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
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    }
   ],
   "source": [
    "# Import Costs\n",
    "freight_costs = pd.read_excel('freight_costs.xlsx', index_col = 0)\n",
    "freight_costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variable Costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USA</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freight Costs ($/Container)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>12.000</td>\n",
       "      <td>24.25</td>\n",
       "      <td>13.100</td>\n",
       "      <td>28.100</td>\n",
       "      <td>20.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>26.335</td>\n",
       "      <td>13.00</td>\n",
       "      <td>21.617</td>\n",
       "      <td>33.244</td>\n",
       "      <td>23.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>25.400</td>\n",
       "      <td>32.75</td>\n",
       "      <td>10.000</td>\n",
       "      <td>53.610</td>\n",
       "      <td>24.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>24.450</td>\n",
       "      <td>30.05</td>\n",
       "      <td>36.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>37.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>18.650</td>\n",
       "      <td>20.40</td>\n",
       "      <td>29.500</td>\n",
       "      <td>34.400</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                USA  Germany   Japan  Brazil   India\n",
       "Freight Costs ($/Container)                                         \n",
       "USA                          12.000    24.25  13.100  28.100  20.778\n",
       "Germany                      26.335    13.00  21.617  33.244  23.073\n",
       "Japan                        25.400    32.75  10.000  53.610  24.350\n",
       "Brazil                       24.450    30.05  36.000   8.000  37.750\n",
       "India                        18.650    20.40  29.500  34.400   5.000"
      ]
     },
     "execution_count": 171,
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   ],
   "source": [
    "# Variable Costs\n",
    "var_cost = freight_costs/1000 + manvar_costs \n",
    "\n",
    "var_cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fixed Costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Low</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>6500</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>4980</td>\n",
       "      <td>7270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>6230</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3230</td>\n",
       "      <td>4730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>2110</td>\n",
       "      <td>6160</td>\n",
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      "text/plain": [
       "          Low  High\n",
       "USA      6500  9500\n",
       "Germany  4980  7270\n",
       "Japan    6230  9100\n",
       "Brazil   3230  4730\n",
       "India    2110  6160"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Costs\n",
    "fixed_costs = pd.read_excel('fixed_cost.xlsx', index_col = 0)\n",
    "fixed_costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plants Capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
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       "  <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>USA</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>500</td>\n",
       "      <td>3000</td>\n",
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      "text/plain": [
       "                         Low  High\n",
       "Capacity (kUnits/month)           \n",
       "USA                      500  1500\n",
       "Germany                  500  1500\n",
       "Japan                    500  1500\n",
       "Brazil                   500  1500\n",
       "India                    500  3000"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Two types of plants: Low Capacity and High Capacity Plant\n",
    "cap = pd.read_excel('capacity.xlsx', index_col = 0)\n",
    "cap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demand "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>2800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>145000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>160000</td>\n",
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      "text/plain": [
       "          Demand\n",
       "USA      2800000\n",
       "Germany    90000\n",
       "Japan    1700000\n",
       "Brazil    145000\n",
       "India     160000"
      ]
     },
     "execution_count": 174,
     "metadata": {},
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   ],
   "source": [
    "# -- Demand\n",
    "demand = pd.read_excel('demand.xlsx', index_col = 0)\n",
    "demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Costs = 92,981,000 ($/Month)\n",
      "\n",
      "Status: Optimal\n",
      "('Brazil','High') = 0.0\n",
      "('Brazil','Low') = 1.0\n",
      "('Germany','High') = 0.0\n",
      "('Germany','Low') = 0.0\n",
      "('India','High') = 1.0\n",
      "('India','Low') = 0.0\n",
      "('Japan','High') = 1.0\n",
      "('Japan','Low') = 0.0\n",
      "('USA','High') = 1.0\n",
      "('USA','Low') = 0.0\n",
      "('Brazil','Brazil') = 145000.0\n",
      "('Brazil','Germany') = 0.0\n",
      "('Brazil','India') = 0.0\n",
      "('Brazil','Japan') = 0.0\n",
      "('Brazil','USA') = 0.0\n",
      "('Germany','Brazil') = 0.0\n",
      "('Germany','Germany') = 0.0\n",
      "('Germany','India') = 0.0\n",
      "('Germany','Japan') = 0.0\n",
      "('Germany','USA') = 0.0\n",
      "('India','Brazil') = 0.0\n",
      "('India','Germany') = 90000.0\n",
      "('India','India') = 160000.0\n",
      "('India','Japan') = 0.0\n",
      "('India','USA') = 1500000.0\n",
      "('Japan','Brazil') = 0.0\n",
      "('Japan','Germany') = 0.0\n",
      "('Japan','India') = 0.0\n",
      "('Japan','Japan') = 1500000.0\n",
      "('Japan','USA') = 0.0\n",
      "('USA','Brazil') = 0.0\n",
      "('USA','Germany') = 0.0\n",
      "('USA','India') = 0.0\n",
      "('USA','Japan') = 200000.0\n",
      "('USA','USA') = 1300000.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Samir\\Anaconda3\\lib\\site-packages\\pulp\\pulp.py:1199: 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 = ['USA', 'Germany', 'Japan', 'Brazil', 'India']\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)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>USA</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Low  High\n",
       "Location           \n",
       "USA         0     1\n",
       "Germany     0     0\n",
       "Japan       0     1\n",
       "Brazil      1     0\n",
       "India       0     1"
      ]
     },
     "execution_count": 176,
     "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"
   ]
  }
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