MyCode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84ce0738",
   "metadata": {},
   "source": [
    "## Importing Libraries which we will use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "621881e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pulp import *\n",
    "from num2words import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17ca5f5",
   "metadata": {},
   "source": [
    "## Data-Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4c42fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "capacity0 = pd.read_excel('capacity.xlsx')\n",
    "countries = list(capacity0['Capacity (kUnits/month)'].unique())\n",
    "capacity0 = capacity0.set_index(\"Capacity (kUnits/month)\")\n",
    "emmision0 = pd.read_excel('CO2_Emissions.xlsx').set_index(\"CO2 Emissions (kgs)\")\n",
    "Leadtime0 = pd.read_excel('Delivery_LeadTime.xlsx').set_index(\"Unnamed: 0\")\n",
    "demand0  = pd.read_excel('demand.xlsx').set_index(\"(Units/month)\")\n",
    "fixedcost0 = pd.read_excel('fixed_cost.xlsx').set_index(\"Unnamed: 0\")\n",
    "freightcost0 = pd.read_excel('freight_costs.xlsx').set_index(\"Freight Costs ($/Container)\")\n",
    "storagecost0 = pd.read_excel('Storage_costs.xlsx').set_index(\"Country\")\n",
    "manufacture0 = pd.read_excel('manufacturing_cost.xlsx').set_index(\"Country\")\n",
    "maxemmision0 = pd.read_excel('max-emmision.xlsx').set_index(\"Country\")\n",
    "factor = pd.read_excel('factor.xlsx').set_index('factors')\n",
    "#\n",
    "size = ['High','Low']\n",
    "storagetypes = ['Port-Store','Factory-Store']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0399ec7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('USA', 'High'): 1000,\n",
       " ('USA', 'Low'): 500,\n",
       " ('Germany', 'High'): 1000,\n",
       " ('Germany', 'Low'): 500,\n",
       " ('Japan', 'High'): 1000,\n",
       " ('Japan', 'Low'): 500,\n",
       " ('Brazil', 'High'): 1000,\n",
       " ('Brazil', 'Low'): 500,\n",
       " ('India', 'High'): 1000,\n",
       " ('India', 'Low'): 500}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capacity = {}\n",
    "for i in countries:\n",
    "    for j in size:\n",
    "        capacity[i,j]=capacity0.loc[i][j]\n",
    "del capacity0\n",
    "capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d66fae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('USA', 'USA'): 0.0,\n",
       " ('USA', 'Germany'): 84.06209480000001,\n",
       " ('USA', 'Japan'): 299.31716568,\n",
       " ('USA', 'Brazil'): 125.5220626,\n",
       " ('USA', 'India'): 181.06915104,\n",
       " ('Germany', 'USA'): 84.06209480000001,\n",
       " ('Germany', 'Germany'): 0.0,\n",
       " ('Germany', 'Japan'): 267.65434000000005,\n",
       " ('Germany', 'Brazil'): 140.79148464000002,\n",
       " ('Germany', 'India'): 149.30790664,\n",
       " ('Japan', 'USA'): 299.31716568,\n",
       " ('Japan', 'Germany'): 267.65434000000005,\n",
       " ('Japan', 'Japan'): 0.0,\n",
       " ('Japan', 'Brazil'): 282.62497856000005,\n",
       " ('Japan', 'India'): 127.72629135999999,\n",
       " ('Brazil', 'USA'): 125.5220626,\n",
       " ('Brazil', 'Germany'): 140.79148464000002,\n",
       " ('Brazil', 'Japan'): 282.62497856000005,\n",
       " ('Brazil', 'Brazil'): 0.0,\n",
       " ('Brazil', 'India'): 191.40908928000002,\n",
       " ('India', 'USA'): 181.06915104,\n",
       " ('India', 'Germany'): 149.30790664,\n",
       " ('India', 'Japan'): 127.72629135999999,\n",
       " ('India', 'Brazil'): 191.40908928000002,\n",
       " ('India', 'India'): 0.0}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emmision = {}\n",
    "for i in countries:\n",
    "    for j in countries:\n",
    "        emmision[i,j]=emmision0.loc[i][j]\n",
    "del emmision0\n",
    "emmision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe3e0bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('USA', 'USA'): 3,\n",
       " ('USA', 'Germany'): 21,\n",
       " ('USA', 'Japan'): 65,\n",
       " ('USA', 'Brazil'): 29,\n",
       " ('USA', 'India'): 41,\n",
       " ('Germany', 'USA'): 21,\n",
       " ('Germany', 'Germany'): 3,\n",
       " ('Germany', 'Japan'): 59,\n",
       " ('Germany', 'Brazil'): 33,\n",
       " ('Germany', 'India'): 34,\n",
       " ('Japan', 'USA'): 65,\n",
       " ('Japan', 'Germany'): 59,\n",
       " ('Japan', 'Japan'): 3,\n",
       " ('Japan', 'Brazil'): 62,\n",
       " ('Japan', 'India'): 30,\n",
       " ('Brazil', 'USA'): 29,\n",
       " ('Brazil', 'Germany'): 33,\n",
       " ('Brazil', 'Japan'): 62,\n",
       " ('Brazil', 'Brazil'): 3,\n",
       " ('Brazil', 'India'): 43,\n",
       " ('India', 'USA'): 41,\n",
       " ('India', 'Germany'): 34,\n",
       " ('India', 'Japan'): 30,\n",
       " ('India', 'Brazil'): 43,\n",
       " ('India', 'India'): 3}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leadtime = {}\n",
    "for i in countries:\n",
    "    for j in countries:\n",
    "        leadtime[i,j] = Leadtime0.loc[i][j]\n",
    "del Leadtime0\n",
    "leadtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d34cadb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'USA': 2800000,\n",
       " 'Germany': 90000,\n",
       " 'Japan': 1700000,\n",
       " 'Brazil': 145000,\n",
       " 'India': 160000}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand = {}\n",
    "for i in countries:\n",
    "    demand[i] = demand0.loc[i][\"Demand\"]\n",
    "del demand0\n",
    "demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47b92007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('USA', 'High'): 9500,\n",
       " ('USA', 'Low'): 6500,\n",
       " ('Germany', 'High'): 7270,\n",
       " ('Germany', 'Low'): 4980,\n",
       " ('Japan', 'High'): 9100,\n",
       " ('Japan', 'Low'): 6230,\n",
       " ('Brazil', 'High'): 4730,\n",
       " ('Brazil', 'Low'): 3230,\n",
       " ('India', 'High'): 3080,\n",
       " ('India', 'Low'): 2110}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixedcost = {}\n",
    "for i in countries:\n",
    "    for j in size:\n",
    "        fixedcost[i,j]= fixedcost0.loc[i][j]\n",
    "del fixedcost0\n",
    "fixedcost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c0f490d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('USA', 'USA'): 0,\n",
       " ('USA', 'Germany'): 12250,\n",
       " ('USA', 'Japan'): 1100,\n",
       " ('USA', 'Brazil'): 16100,\n",
       " ('USA', 'India'): 8778,\n",
       " ('Germany', 'USA'): 13335,\n",
       " ('Germany', 'Germany'): 0,\n",
       " ('Germany', 'Japan'): 8617,\n",
       " ('Germany', 'Brazil'): 20244,\n",
       " ('Germany', 'India'): 10073,\n",
       " ('Japan', 'USA'): 15400,\n",
       " ('Japan', 'Germany'): 22750,\n",
       " ('Japan', 'Japan'): 0,\n",
       " ('Japan', 'Brazil'): 43610,\n",
       " ('Japan', 'India'): 14350,\n",
       " ('Brazil', 'USA'): 16450,\n",
       " ('Brazil', 'Germany'): 22050,\n",
       " ('Brazil', 'Japan'): 28000,\n",
       " ('Brazil', 'Brazil'): 0,\n",
       " ('Brazil', 'India'): 29750,\n",
       " ('India', 'USA'): 13650,\n",
       " ('India', 'Germany'): 15400,\n",
       " ('India', 'Japan'): 24500,\n",
       " ('India', 'Brazil'): 29400,\n",
       " ('India', 'India'): 0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freightcost = {}\n",
    "for i in countries:\n",
    "    for j in countries:\n",
    "        freightcost[i,j] = freightcost0.loc[i][j]\n",
    "del freightcost0\n",
    "freightcost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f37fed60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('USA', 'Port-Store'): 4.0,\n",
       " ('USA', 'Factory-Store'): 2.0,\n",
       " ('Germany', 'Port-Store'): 5.0,\n",
       " ('Germany', 'Factory-Store'): 3.0,\n",
       " ('Japan', 'Port-Store'): 3.0,\n",
       " ('Japan', 'Factory-Store'): 1.0,\n",
       " ('Brazil', 'Port-Store'): 5.0,\n",
       " ('Brazil', 'Factory-Store'): 2.0,\n",
       " ('India', 'Port-Store'): 4.0,\n",
       " ('India', 'Factory-Store'): 0.5}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storagecost = {}\n",
    "for i in countries:\n",
    "    for j in storagetypes:\n",
    "        storagecost[i,j] = storagecost0.loc[i][j]\n",
    "del storagecost0\n",
    "storagecost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f5dd56da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'USA': 12, 'Germany': 13, 'Japan': 10, 'Brazil': 8, 'India': 5}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manufacture = {}\n",
    "for i in countries:\n",
    "    manufacture[i] = manufacture0.loc[i][\"Manufacturing cost\"]\n",
    "del manufacture0\n",
    "manufacture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "125b4d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxemmision = {}\n",
    "for i in countries:\n",
    "    maxemmision[i]=maxemmision0.loc[i]['Emmision']\n",
    "del maxemmision0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "979b6191",
   "metadata": {},
   "outputs": [],
   "source": [
    "factorsdict = {}\n",
    "factorsdict['storage_cost_factor'] = factor.loc['storage_cost_factor']['value']\n",
    "factorsdict['fixed_cost_factor'] = factor.loc['fixed_cost_factor']['value']\n",
    "factorsdict['manudfacturing_cost_factor'] = factor.loc['manudfacturing_cost_factor']['value']\n",
    "factorsdict['shipping_cost_factor'] = factor.loc['shipping_cost_factor']['value']\n",
    "factorsdict['delivery_leadtime_factor'] = factor.loc['delivery_leadtime_factor']['value']\n",
    "factorsdict['max_emmision_factor'] = factor.loc['max_emmision_factor']['value']\n",
    "factorsdict['delivery_deadtime'] = factor.loc['delivery_deadtime']['value']\n",
    "factorsdict['Further Evaluate'] = factor.loc['Further Evaluate']['value']\n",
    "#\n",
    "storage_cost_factor = int(factorsdict['storage_cost_factor'])\n",
    "fixed_cost_factor = int(factorsdict['fixed_cost_factor'])\n",
    "manudfacturing_cost_factor = int(factorsdict['manudfacturing_cost_factor'])\n",
    "shipping_cost_factor = int(factorsdict['shipping_cost_factor'])\n",
    "delivery_leadtime_factor = int(factorsdict['delivery_leadtime_factor'])\n",
    "max_emmision_factor = int(factorsdict['max_emmision_factor'])\n",
    "delivery_deadtime = int(factorsdict['delivery_deadtime'])\n",
    "further_evaluate = int(factorsdict['Further Evaluate'] )\n",
    "del factorsdict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "727127af",
   "metadata": {},
   "source": [
    "## Defining our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a490cace",
   "metadata": {},
   "outputs": [],
   "source": [
    "def variablecost(i,j,shipping_cost_factor,manudfacturing_cost_factor):\n",
    "    return freightcost[i,j]*shipping_cost_factor/10000 + manufacture[i]*manudfacturing_cost_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3c4cbff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Costtostore (i,j,storage_cost_factor):\n",
    "      if i==j:\n",
    "            return storagecost[j,'Factory-Store']*storage_cost_factor\n",
    "      else:\n",
    "            return storagecost[j,'Port-Store']*storage_cost_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3797f2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixedtocost(i,s,fixed_cost_factor):\n",
    "    return fixedcost[i,s]*y[i,s]*fixed_cost_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c7e7f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = LpProblem('Minimize-the-Cost',LpMinimize)\n",
    "x = LpVariable.dict('F2C',(countries,countries),lowBound=0,cat='Continous')\n",
    "y = LpVariable.dict('OpenOrNot',(countries,size),lowBound=0,cat='Integer')\n",
    "# Yet to add storage cost constaint , Total Cost is Fixed Cost + variable cost\n",
    "\n",
    "z += (lpSum(fixedtocost(i,s,fixed_cost_factor) for i in countries for s in size)\n",
    "      + lpSum(x[i,j]*variablecost(i,j,shipping_cost_factor,manudfacturing_cost_factor) for i in countries for j in countries)\n",
    "      + lpSum(x[i,j]*Costtostore(i,j,storage_cost_factor) for i in countries for j in countries))\n",
    "\n",
    "# The demand of all countries must be met\n",
    "for j in countries:\n",
    "      z += lpSum(x[i,j] for i in countries ) == demand[j]\n",
    "\n",
    "# A country cannot cater the demand more than its production\n",
    "for i in countries:\n",
    "      z += lpSum(x[i,j] for j in countries) <= lpSum(capacity[i,j]*y[i,j]*1000 for j in size)\n",
    "\n",
    "# Emmision Constraint can be changed with more better understanding\n",
    "for j in countries:\n",
    "      z += lpSum(x[i,j]*emmision[i,j] for i in countries) <= maxemmision[j]*max_emmision_factor\n",
    "\n",
    "# Constaint for delivery time\n",
    "for i in countries:\n",
    "      for j in countries:\n",
    "            z += leadtime[i,j]*x[i,j]*delivery_leadtime_factor <= delivery_deadtime*x[i,j]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33f0d6a",
   "metadata": {},
   "source": [
    "## Solution Status , Total Cost and its BreakDown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e143a30f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution is : Optimal\n",
      "The total cost is : 61366906.93175001\n",
      "Total Cost in Words : six crore, thirteen lakh, sixty-six thousand, nine hundred and six point nine three one seven five zero zero zero\n"
     ]
    }
   ],
   "source": [
    "status = z.solve()\n",
    "total_cost = value(z.objective)\n",
    "print(\"Solution is :\",LpStatus[status])\n",
    "print(\"The total cost is :\",total_cost)\n",
    "print(\"Total Cost in Words :\",(num2words(total_cost,lang='en_IN')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "749626b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixed Cost : 53620.0\n",
      "Fixed Cost in Words : fifty-three thousand, six hundred and twenty\n"
     ]
    }
   ],
   "source": [
    "fixed_cost = value(lpSum(fixedtocost(i,s,fixed_cost_factor) for i in countries for s in size))\n",
    "print('Fixed Cost :',fixed_cost)\n",
    "print('Fixed Cost in Words :',(num2words(fixed_cost,lang='en_IN')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c2a9b354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manufacturing Cost : 50179344.6\n",
      "Manufacturing Cost in words : five crore, one lakh, seventy-nine thousand, three hundred and forty-four point six\n"
     ]
    }
   ],
   "source": [
    "manufacturing_cost = value(lpSum(x[i,j]*manufacture[i] for i in countries for j in countries))\n",
    "print('Manufacturing Cost :',manufacturing_cost)\n",
    "print('Manufacturing Cost in words :',(num2words(manufacturing_cost,lang='en_IN')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5d6cd3c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shipping Cost : 1614345.39175\n",
      "Shipping Cost in words : sixteen lakh, fourteen thousand, three hundred and forty-five point three nine one seven five\n"
     ]
    }
   ],
   "source": [
    "shipping_cost = value(lpSum(x[i,j]*freightcost[i,j]/10000 for i in countries for j in countries))\n",
    "print('Shipping Cost :',shipping_cost)\n",
    "print('Shipping Cost in words :',(num2words(shipping_cost,lang='en_IN')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2cf208fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Storage Cost  9519596.940000001\n",
      "Sotrage Cost in words : ninety-five lakh, nineteen thousand, five hundred and ninety-six point nine four zero zero zero zero zero zero one\n"
     ]
    }
   ],
   "source": [
    "store_cost = value(lpSum(x[i,j]*Costtostore(i,j,storage_cost_factor) for i in countries for j in countries))\n",
    "print('Storage Cost ',store_cost)\n",
    "print('Sotrage Cost in words :',(num2words(store_cost,lang='en_IN')))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b299ad35",
   "metadata": {},
   "source": [
    "## Which country serves how much to which country and which factories to open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8a6137c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Storing the data in other variable for further manipulation\n",
    "c2c = {}\n",
    "for i in countries:\n",
    "    for j in countries:\n",
    "        c2c[i,j] = x[i,j].varValue\n",
    "fac = {}\n",
    "for i in countries:\n",
    "    for s in size:\n",
    "        fac[i,s] = y[i,s].varValue\n",
    "del x\n",
    "del y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cc387f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADMIN\\AppData\\Local\\Temp\\ipykernel_13988\\1126569517.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  output1[i][j] = c2c[i,j]\n"
     ]
    }
   ],
   "source": [
    "# Storing the output in dataframe\n",
    "output1 = pd.read_excel('Output1.xlsx').set_index('Country')\n",
    "for i in countries:\n",
    "    for j in countries:\n",
    "        output1[i][j] = c2c[i,j]\n",
    "output2 = pd.read_excel('Output2.xlsx').set_index('Country')\n",
    "for i in countries:\n",
    "    for s in size:\n",
    "        output2.loc[i][s] = fac[i,s]\n",
    "output1.to_excel('Output1.xlsx')\n",
    "output2.to_excel('Output2.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c299b8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Demands are fullfilled by'}, xlabel='Country'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output1.plot(kind='bar',title='Demands are fullfilled by')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b5614243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Country'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output2.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fce7a87",
   "metadata": {},
   "source": [
    "## CO2 Emmision Data for Shipping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "649ab7e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USA = 50000000.260555506\n",
      "Germany = 0.0\n",
      "Japan = 49999999.686471626\n",
      "Brazil = 0.0\n",
      "India = 0.0\n"
     ]
    }
   ],
   "source": [
    "for j in countries:\n",
    "    print(j,\"=\",lpSum(c2c[i,j]*emmision[i,j] for i in countries))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37f225a",
   "metadata": {},
   "source": [
    "## Now the decision variables is fixed , so we can do further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efa5ada1",
   "metadata": {},
   "outputs": [],
   "source": [
    "if (further_evaluate==1):\n",
    "    storage_cost_factor = int(input(\"Storage Cost Factor :\") or storage_cost_factor)\n",
    "    fixed_cost_factor = int(input(\"Fixed Cost Factor :\") or fixed_cost_factor)\n",
    "    manudfacturing_cost_factor = int(input(\"Manufacturing cost factor :\") or manudfacturing_cost_factor)\n",
    "    shipping_cost_factor = int(input(\"Shipping Cost Factor :\") or shipping_cost_factor)\n",
    "    delivery_leadtime_factor = int(input(\"Delivery leadtime factor :\") or delivery_leadtime_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09782805",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cost = (lpSum(fixedcost[i,s]*fac[i,s] for i in countries for s in size) \n",
    "      + lpSum(c2c[i,j]*variablecost(i,j,shipping_cost_factor,manudfacturing_cost_factor) for i in countries for j in countries)\n",
    "      + lpSum(c2c[i,j]*Costtostore(i,j,storage_cost_factor) for i in countries for j in countries))\n",
    "print(\"Total Cost is :\",Cost)\n",
    "print('Words :',num2words(value(Cost),lang='en_IN'))"
   ]
  }
 ],
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