Air_Cargo_Full_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from gurobi import *\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/Booking_new.xlsx\")\n",
    "bd_zones = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/BreakDownZones.xlsx\")\n",
    "dz_to_bd = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/DistanceMatrixDropZoneToBreakDownZone.xlsx\")\n",
    "drop_zones = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/DropZones.xlsx\")\n",
    "bu_zones = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/Outbound.xlsx\", sheet_name='BU Zones')\n",
    "bu_zones_workstations = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/Outbound.xlsx\", sheet_name='WorkStation')\n",
    "bu_zones_to_flight = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/Outbound.xlsx\", sheet_name='FlightNumber-BUZone')\n",
    "flight_default_processing = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/Outbound.xlsx\", sheet_name='DefaultProcessingTime')\n",
    "flight_pre_processing = pd.read_excel(\"/media/luckshan/New Volume/AirCargo/Ab-Ovo Project/Ab Ovo Data/Outbound.xlsx\", sheet_name='Pre-ProcessingBufferTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dz = list(drop_zones['Name'].unique())\n",
    "break_down_zones = list(bd_zones['Name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dz_to_bd[['h','m','s']] = pd.DataFrame([(x.hour, x.minute, x.second) for x in dz_to_bd['TransportDuration']])\n",
    "bd_zones[['handling_h','handling_m','handling_s']] = pd.DataFrame([(x.hour, x.minute, x.second) for x in bd_zones['HandlingTimePerULD']])\n",
    "bd_zones[['transport_WH_h','transport_WH_m','transport_WH_s']] = pd.DataFrame([(x.hour, x.minute, x.second) for x in bd_zones['TransportationTimeToWH']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking['key_shipment_id_arrival_date'] = booking['Shipment ID'].astype(str) + booking['ShipmentArrivalDateUTC'].astype(str)\n",
    "booking = booking.drop_duplicates('key_shipment_id_arrival_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking = booking[booking.TotalWeight<=400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####################################################################################\n",
    "#Handling multiple ULDs having same number arriving less than 4 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luckshan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(312, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uld_number_times = booking[['ShipmentArrivalDateUTC', 'ArrivalULDNumber']]\n",
    "uld_number_times['id'] = booking['ShipmentArrivalDateUTC'].astype(str) + '_' + booking['ArrivalULDNumber'].astype(str)\n",
    "uld_number_times = uld_number_times.drop_duplicates('id')\n",
    "uld_number_times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "uld_number_times['count_uld_number'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_uld_count_by_number = uld_number_times.groupby(['ArrivalULDNumber']).agg({\"count_uld_number\":\"count\"}).rename(columns={\"count_uld_number\":\"Count\"}).reset_index()\n",
    "multiple_uld_numbers = unique_uld_count_by_number[unique_uld_count_by_number.Count>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiple_uld_number_and_times = uld_number_times[uld_number_times['ArrivalULDNumber'].isin((multiple_uld_numbers['ArrivalULDNumber'].unique()))]\n",
    "multiple_uld_number_and_times = multiple_uld_number_and_times.sort_values(by=['ArrivalULDNumber', 'ShipmentArrivalDateUTC'])\n",
    "multiple_uld_number_and_times['arrival_time'] = pd.to_datetime(multiple_uld_number_and_times['ShipmentArrivalDateUTC'])\n",
    "multiple_uld_number_and_times['diff'] = multiple_uld_number_and_times.sort_values(by=['ArrivalULDNumber', 'arrival_time']).groupby('ArrivalULDNumber')['arrival_time'].diff()\n",
    "multiple_ULD_arriving_below_4_hours = list(multiple_uld_number_and_times[multiple_uld_number_and_times['diff'].notnull() & (multiple_uld_number_and_times['diff'] < pd.Timedelta(4, unit='h'))]['ArrivalULDNumber'].unique())\n",
    "booking = booking[~booking['ArrivalULDNumber'].isin(multiple_ULD_arriving_below_4_hours)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering shipments arriving after departure\n",
    "booking['Shipment_arrival_date_time'] = pd.to_datetime(booking['ShipmentArrivalDateUTC'])\n",
    "booking = booking[booking.ShipmentDepartureDateUTC != '2018-10-24T26:20:00']\n",
    "booking['Shipment_departure_date_time'] = pd.to_datetime(booking['ShipmentDepartureDateUTC'])\n",
    "booking['Shipment_duration'] = booking['Shipment_departure_date_time']-booking['Shipment_arrival_date_time']\n",
    "booking = booking[booking.Shipment_duration>pd.Timedelta(0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking['count_shipments'] = 1\n",
    "multple_dropzone_check = booking.pivot_table(index=['ArrivalULDNumber', 'Shipment_arrival_date_time'], columns='DropZone', values= 'count_shipments').reset_index()\n",
    "multple_dropzone_check = multple_dropzone_check.fillna(0)\n",
    "multple_dropzone_check['Two_different_dropzones_check'] = multple_dropzone_check['DZ NML-1'] + multple_dropzone_check['DZ NRML-1'] + multple_dropzone_check['DZ NRML-2']\n",
    "unique_dropzone_check = multple_dropzone_check[multple_dropzone_check.Two_different_dropzones_check==1.0]\n",
    "two_drop_zones = multple_dropzone_check[multple_dropzone_check.Two_different_dropzones_check>1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luckshan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: Passing datetime64-dtype data to TimedeltaIndex is deprecated, will raise a TypeError in a future version\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/home/luckshan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "two_drop_zones['unique_id'] = two_drop_zones['ArrivalULDNumber'].astype(str) + '_' + (pd.to_timedelta(two_drop_zones['Shipment_arrival_date_time']).astype('timedelta64[m]').astype(int).astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dz_to_bd['DropZoneName_filter'] = dz_to_bd['DropZoneName'].apply(lambda x: x.split('-')[0]).str[3:]\n",
    "dz_to_bd['bd_type1'] = dz_to_bd['BreakDownZoneName'].apply(lambda x: x.split(' ')[1])\n",
    "dz_to_bd.loc[dz_to_bd['BreakDownZoneName'] == 'B BD NRML-1', 'bd_type1'] = \"NRML-1\"\n",
    "dz_to_bd.loc[dz_to_bd['BreakDownZoneName'] == 'B BD NRML-2', 'bd_type1'] = \"NRML-2\"\n",
    "dz_to_bd['bd_type'] =  dz_to_bd['bd_type1'].apply(lambda x: x.split('-')[0])\n",
    "dz_to_bd['check'] = np.where(dz_to_bd['DropZoneName_filter']==dz_to_bd['bd_type'], 1, 0)\n",
    "dz_to_bd = dz_to_bd[dz_to_bd['check']==1]\n",
    "dz_to_bd = dz_to_bd[['BreakDownZoneName', 'DropZoneName', 'TransportDuration', 'h', 'm', 's']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luckshan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dz_ship_data = booking[['DropZone','ArrivalULDNumber', 'ShipmentArrivalDateUTC']]\n",
    "dz_ship_data['unique_key'] =  dz_ship_data['DropZone'].astype(str) + '_' + dz_ship_data['ArrivalULDNumber'].astype(str) + '_' + dz_ship_data['ShipmentArrivalDateUTC'].astype(str)\n",
    "dz_ship_data = dz_ship_data.drop_duplicates('unique_key')\n",
    "dz_ship_data = dz_ship_data[['DropZone', 'ArrivalULDNumber', 'ShipmentArrivalDateUTC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "uld_bd_data = pd.merge(dz_ship_data, dz_to_bd, left_on='DropZone', right_on='DropZoneName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "uld_bd_data['key'] = uld_bd_data['DropZone'].astype(str) + '_' + uld_bd_data['ArrivalULDNumber'].astype(str) + '_' + uld_bd_data['ShipmentArrivalDateUTC'].astype(str) + '_' + uld_bd_data['BreakDownZoneName'].astype(str)\n",
    "uld_bd_data = uld_bd_data.drop_duplicates('key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "uld_bd_data['Shipment_arrival_date_time'] = pd.to_datetime(uld_bd_data['ShipmentArrivalDateUTC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting BU workstations count\n",
    "bu_workstation_count = bu_zones_workstations.groupby(['Name']).count().reset_index()\n",
    "bu_workstation_count.columns = ['Name', 'Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "bu_zones_data = pd.merge(bu_zones_to_flight, bu_zones, left_on='BU Zone', right_on='Name')\n",
    "bu_zone_all = pd.merge(bu_zones_data, bu_workstation_count, left_on='BU Zone', right_on='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bu_zone_all[['handling_ULD_h','handling_ULD_m','handling_ULD_s']] = pd.DataFrame([(x.hour, x.minute, x.second) for x in bu_zone_all['HandlingTimePerULD']])\n",
    "bu_zone_all[['transport_WH_h','transport_WH_m','transport_WH_s']] = pd.DataFrame([(x.hour, x.minute, x.second) for x in bu_zone_all['TransportationTimeToWH']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating booking_new variable for future use\n",
    "booking['build_up_arrival_time'] =  booking['Shipment_arrival_date_time'] #+ np.timedelta64(2, 'h')\n",
    "booking['build_up_finish_time'] =  booking['Shipment_departure_date_time'] #- np.timedelta64(1, 'h')\n",
    "booking_new = booking[['ProductName', 'Priority', 'DropZone', 'ArrivalULDNumber', 'Pieces', 'Weight', 'TotalWeight',\n",
    "         'FlightNumberDeparture', 'Shipment_arrival_date_time', 'Shipment_departure_date_time',\n",
    "         'Shipment_duration', 'build_up_arrival_time', 'build_up_finish_time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking_new = pd.merge(booking_new, bu_zone_all, left_on='FlightNumberDeparture', right_on='FlightNumber')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking_new[['ULD_handling_h','ULD_handling_m','ULD_handling_s']] = pd.DataFrame([(x.hour, x.minute, x.second) for x in booking_new['HandlingTimePerULD']])\n",
    "booking_new['arrival_date'] = booking_new['Shipment_arrival_date_time'].dt.normalize()\n",
    "booking_new['departure_date'] = booking_new['Shipment_departure_date_time'].dt.normalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame used in  BD zone\n",
    "#sample_shipments = booking_new.iloc[98:118,:]\n",
    "# sample_shipments = booking_new[booking_new['ArrivalULDNumber'].isin(list([34842, 34222, 92870, 21231, 49815, 51228, 59243]))]\n",
    "# sample_shipments = booking_new[booking_new['ArrivalULDNumber'].isin(list([34842, 34222, 92870]))]\n",
    "# sample_shipments = booking_new[booking_new['ArrivalULDNumber'].isin(list([34842, 34222,92870, 21231, 49815, 59243]))]\n",
    "# sample_shipments = booking_new[booking_new['ArrivalULDNumber'].isin(list([51228]))]\n",
    "sample_shipments = booking_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luckshan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Passing datetime64-dtype data to TimedeltaIndex is deprecated, will raise a TypeError in a future version\n",
      "  \n",
      "/home/luckshan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: Passing datetime64-dtype data to TimedeltaIndex is deprecated, will raise a TypeError in a future version\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "sample_shipments['Shipment_duration_minutes'] = (sample_shipments['Shipment_duration'] / np.timedelta64(1, 'm')).astype(int)\n",
    "sample_shipments['Shipment_arrival_time_minutes'] = pd.to_timedelta(sample_shipments['Shipment_arrival_date_time']).astype('timedelta64[m]').astype(int)\n",
    "sample_shipments['Shipment_departure_time_minutes'] = pd.to_timedelta(sample_shipments['Shipment_departure_date_time']).astype('timedelta64[m]').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = sample_shipments[['DropZone','ArrivalULDNumber', 'Shipment_arrival_date_time', 'Shipment_duration_minutes','Shipment_arrival_time_minutes', 'Shipment_departure_time_minutes']]\n",
    "df_merge= pd.merge(uld_bd_data, new, on=['DropZone','ArrivalULDNumber', 'Shipment_arrival_date_time'])\n",
    "df_merge = df_merge.drop_duplicates('key')\n",
    "bd_capacity = bd_zones[['Name','NumberOfWorkstations', 'handling_m']]\n",
    "df_merge1 = df_merge.merge(bd_capacity, left_on='BreakDownZoneName', right_on='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BD process needed to to be completed with in 60 minutes\n",
    "df_merge1['one_hour_duration'] = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge1['unique_id'] = df_merge1['ArrivalULDNumber'].astype(str) + '_' + df_merge1['Shipment_arrival_time_minutes'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#two_drop_zones_ULD_Numbers = list(two_drop_zones['unique_id'].unique())\n",
    "two_drop_zones_ULD_Numbers = list(two_drop_zones[two_drop_zones['ArrivalULDNumber'].isin([34842, 34222, 92870, 21231, 49815, 51228, 59243])]['unique_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "uld_uniqueid_list = list(df_merge1['unique_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ArrivalULDNumber_list = list(df_merge1['ArrivalULDNumber'].unique())\n",
    "BreakDownZoneName_list = list(df_merge1['BreakDownZoneName'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################################################\n",
    "##################################################################################################################\n",
    "###############################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking_new_BU = sample_shipments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking_new_BU['shipment_ID'] = range(1, len(booking_new_BU) + 1)\n",
    "shipment_id_list = list(booking_new_BU['shipment_ID'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data used for BU process 'sample_shipments_bu'\n",
    "sample_shipments_bu = booking_new_BU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_shipments_bu['unique_id'] = sample_shipments_bu['ArrivalULDNumber'].astype(str) + '_' + sample_shipments_bu['Shipment_arrival_time_minutes'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ArrivalULDNumber_list = list(sample_shipments_bu['ArrivalULDNumber'].unique())\n",
    "filghts_list = list(sample_shipments_bu['FlightNumberDeparture'].unique())\n",
    "bu_zones_list = list(sample_shipments_bu['Name_x'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################\n",
    "#####################################################################################\n",
    "#######################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Full Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Academic license - for non-commercial use only\n"
     ]
    }
   ],
   "source": [
    "model = Model('Full model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Discretisation has been done based on each BD zone handling times\n",
    "x = {}\n",
    "for i in uld_uniqueid_list:\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    #print(BreakDownZoneName_list1)\n",
    "    for j in BreakDownZoneName_list1:\n",
    "        #print(int(i),str(j))\n",
    "        new_check = df_merge1[(df_merge1.unique_id==i) & (df_merge1.BreakDownZoneName==j)]\n",
    "        min_time = min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique()))\n",
    "        arr = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "            #print(arr,\"--\",i)\n",
    "        shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "        handling_minutes = list(new_check['handling_m'].unique())[0]\n",
    "            #print(shipment_arrival_time_minutes_loop, handling_minutes)\n",
    "        t_start = (min_time + (math.ceil((arr-min_time)/handling_minutes)*handling_minutes))\n",
    "            #print(arr,\"-----\",t_start)\n",
    "        #if(i == '51228_25671360'):   \n",
    "            #print(t_start)\n",
    "        for k in range(t_start, t_start+shipment_arrival_duation_minutes_loop, handling_minutes):\n",
    "            x[i,j,k] = model.addVar(vtype=GRB.BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = {}\n",
    "for i in ArrivalULDNumber_list:\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.ArrivalULDNumber==i]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "        #print(shipment_list)\n",
    "        for l in shipment_list:\n",
    "        #print(int(i),str(j))\n",
    "            number_of_ws = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "            shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "            for m in range(1,number_of_ws+1):\n",
    "                for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop):\n",
    "                    z[l,m,t] = model.addVar(vtype=GRB.BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = {}\n",
    "for k in filghts_list:\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.FlightNumberDeparture==k]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        ws_list = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "    #print(shipment_list)\n",
    "        for m in range(1, ws_list+1):\n",
    "        #print(int(i),str(j))\n",
    "            for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop):\n",
    "                q[k,m,t] = model.addVar(vtype=GRB.BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BD Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Handling time of each bd zone take into account for the quicksum\n",
    "for i in (list(set(uld_uniqueid_list) - set(two_drop_zones_ULD_Numbers))):\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    new_check = df_merge1[(df_merge1.unique_id==i)]\n",
    "    arr = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "    #min_time = min(list(df_merge1[df_merge1.BreakDownZoneName.isin(BreakDownZoneName_list1)]['Shipment_arrival_time_minutes'].unique()))\n",
    "    #t_start = shipment_arrival_time_minutes_loop + (handling_minutes - (shipment_arrival_time_minutes_loop%min_time))\n",
    "    model.addConstr(quicksum(x[i,b,c] for b in BreakDownZoneName_list1 for c in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])) == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Handling time of each bd zone take into account for the quicksum\n",
    "for i in two_drop_zones_ULD_Numbers:\n",
    "    #print(i)\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    bd_NML_list = [k for k in BreakDownZoneName_list1 if 'NML' in k]\n",
    "    bd_NRML_list = [k for k in BreakDownZoneName_list1 if 'NRML' in k]\n",
    "    #print(BreakDownZoneName_list1)\n",
    "    #print(bd_NML_list)\n",
    "    #print(bd_NRML_list)\n",
    "    new_check = df_merge1[(df_merge1.unique_id==i)]\n",
    "    arr = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    #print(arr)\n",
    "    shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "    #min_time = min(list(df_merge1[df_merge1.BreakDownZoneName.isin(BreakDownZoneName_list1)]['Shipment_arrival_time_minutes'].unique()))\n",
    "    #t_start = shipment_arrival_time_minutes_loop + (handling_minutes - (shipment_arrival_time_minutes_loop%min_time))\n",
    "    model.addConstr(quicksum(x[i,b,c] for b in bd_NML_list for c in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])) == 1)\n",
    "    model.addConstr(quicksum(x[i,b,c] for b in bd_NRML_list for c in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])) == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Precedence of NML before NRML\n",
    "for i in two_drop_zones_ULD_Numbers:\n",
    "    #print(i)\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    bd_NML_list = [k for k in BreakDownZoneName_list1 if 'NML' in k]\n",
    "    bd_NRML_list = [k for k in BreakDownZoneName_list1 if 'NRML' in k]\n",
    "    #print(BreakDownZoneName_list1)\n",
    "    #print(bd_NML_list)\n",
    "    #print(bd_NRML_list)\n",
    "    new_check = df_merge1[(df_merge1.unique_id==i)]\n",
    "    arr = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    #print(arr)\n",
    "    shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "    #min_time = min(list(df_merge1[df_merge1.BreakDownZoneName.isin(BreakDownZoneName_list1)]['Shipment_arrival_time_minutes'].unique()))\n",
    "    #t_start = shipment_arrival_time_minutes_loop + (handling_minutes - (shipment_arrival_time_minutes_loop%min_time))\n",
    "    model.addConstr(quicksum((c+(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*x[i,b,c] for b in bd_NML_list for c in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])) <= quicksum(c*x[i,b,c] for b in bd_NRML_list for c in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BD Assgnment Time constraint\n",
    "for i in (list(set(uld_uniqueid_list) - set(two_drop_zones_ULD_Numbers))):\n",
    "    #for j in BreakDownZoneName_list:\n",
    "        #print(int(i),str(j))\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    new_check = df_merge1[(df_merge1.unique_id==i)]\n",
    "    arr = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "    #arrival_time_each_uld = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    transport_time_each_bd_zone = list(new_check['m'].unique())[0]\n",
    "    model.addConstr(quicksum((c-df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['m'])*x[i,b,c] for b in BreakDownZoneName_list1 for c in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])) >= arr)\n",
    "        #for k in range(shipment_arrival_duation_minutes_loop):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BD Assgnment Time constraint\n",
    "for i in two_drop_zones_ULD_Numbers:\n",
    "    #for j in BreakDownZoneName_list:\n",
    "        #print(int(i),str(j))\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    bd_NML_list = [k for k in BreakDownZoneName_list1 if 'NML' in k]\n",
    "    new_check = df_merge1[(df_merge1.unique_id==i)]\n",
    "    arr = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "    #arrival_time_each_uld = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    transport_time_each_bd_zone = list(new_check['m'].unique())[0]\n",
    "    model.addConstr(quicksum((c-df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['m'])*x[i,b,c] for b in bd_NML_list for c in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((arr-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])) >= arr)\n",
    "        #for k in range(shipment_arrival_duation_minutes_loop):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in BreakDownZoneName_list:\n",
    "        #print(int(i),str(j))\n",
    "    new_check = df_merge1[df_merge1.BreakDownZoneName==j]\n",
    "    min_time = min(list(new_check['Shipment_arrival_time_minutes'].unique()))\n",
    "    max_time = max(list(new_check['Shipment_departure_time_minutes'].unique()))\n",
    "    uld_break = list(new_check['unique_id'].unique())\n",
    "    #shipment_arrival_time_minutes_loop = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "    #shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "    handling_minutes = list(new_check['handling_m'].unique())[0]\n",
    "    #print(handling_minutes)\n",
    "    cap = list(new_check['NumberOfWorkstations'].unique())[0]\n",
    "    for t in range(min_time, max_time-handling_minutes-1, handling_minutes):\n",
    "        #print(t)\n",
    "        uld_list1=[]\n",
    "        for i in uld_break:\n",
    "            if(t in range((min_time + (math.ceil((new_check[new_check.unique_id==i].iloc[0]['Shipment_arrival_time_minutes']-min_time)/df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m'])*df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m'])), (min_time + (math.ceil((new_check[new_check.unique_id==i].iloc[0]['Shipment_arrival_time_minutes']-min_time)/df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m'])*df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))+shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m'])):#new_check[new_check.ArrivalULDNumber==i].iloc[0]['Shipment_departure_time_minutes'])): \n",
    "                uld_list1.append(i)\n",
    "                #print(t,uld_list1)\n",
    "        if(len(uld_list1)!=0):\n",
    "            model.addConstr(quicksum(x[a,j,t] for a in uld_list1) <= cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################################################################################################################################################\n",
    "##########################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BU Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in filghts_list:\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.FlightNumberDeparture==k]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "        ws_list = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "    #print(shipment_list)\n",
    "        for l in shipment_list:\n",
    "        #print(int(i),str(j))\n",
    "            model.addConstr(quicksum(z[l,m,t] for m in range(1, ws_list+1) for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop))==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in filghts_list:\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.FlightNumberDeparture==k]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "        ws_list = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "        bu_handling_time = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['handling_ULD_m'].unique()[0]\n",
    "        #print(bu_handling_time.dtype)\n",
    "    #print(shipment_list)\n",
    "        for m in range(1, ws_list+1):\n",
    "            for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop-bu_handling_time):\n",
    "                model.addConstr(400*q[k,m,t] - quicksum(sample_shipments_bu[sample_shipments_bu.shipment_ID==l].iloc[0]['TotalWeight']*z[l,m,t] for l in shipment_list) \n",
    "                                >= quicksum(sample_shipments_bu[sample_shipments_bu.shipment_ID==l].iloc[0]['TotalWeight']*z[l,m,tau] for l in shipment_list for tau in range(t+1,t+bu_handling_time)))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in filghts_list:\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.FlightNumberDeparture==k]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k)  & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "        ws_list = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k)   & (sample_shipments_bu.departure_date==d)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k)  & (sample_shipments_bu.departure_date==d)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "        bu_handling_time = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k)  & (sample_shipments_bu.departure_date==d)]['handling_ULD_m'].unique()[0]\n",
    "        model.addConstr(quicksum(q[k,m,t] for m in range(1, ws_list+1) for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop)) <= (quicksum(sample_shipments_bu[sample_shipments_bu.shipment_ID==l].iloc[0]['TotalWeight']*z[l,m,t] for l in shipment_list for t in range(shipment_departure_time_minutes_loop-60,shipment_departure_time_minutes_loop) for m in range(1, ws_list+1))/400) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in filghts_list:\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.FlightNumberDeparture==k]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        bu_zone_for_flight = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Name_x'].unique()[0]\n",
    "        othe_flights_df = sample_shipments_bu[(sample_shipments_bu.Name_x==bu_zone_for_flight) & (sample_shipments_bu.departure_date==d)]\n",
    "        ws_list = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "        other_flights = list(othe_flights_df[(othe_flights_df['FlightNumberDeparture'].isin([k]) == False) & (othe_flights_df.departure_date==d)]['FlightNumberDeparture'].unique())\n",
    "        bu_handling_time = sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==k) & (sample_shipments_bu.departure_date==d)]['handling_ULD_m'].unique()[0]\n",
    "        #print(k, \"----\",filghts_list,\"-------\", other_flights) \n",
    "        #print(shipment_list)\n",
    "        for m in range(1, ws_list+1):\n",
    "        #print(int(i),str(j))\n",
    "            for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop - bu_handling_time):\n",
    "                other_list  =[]\n",
    "                for i in other_flights:\n",
    "                    if(t in range((sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==i)].iloc[0]['Shipment_departure_time_minutes']-60),sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==i)].iloc[0]['Shipment_departure_time_minutes'])): \n",
    "                        other_list.append(i)\n",
    "                #print(k, \"----\",other_list)\n",
    "                if(len(other_list)!=0):\n",
    "                    model.addConstr(1-q[k,m,t] >= quicksum(q[p,m,ta] for p in other_list for ta in range(t,min(t+bu_handling_time,(sample_shipments_bu[(sample_shipments_bu.FlightNumberDeparture==p)].iloc[0]['Shipment_departure_time_minutes'])))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in (list(set(uld_uniqueid_list) - set(two_drop_zones_ULD_Numbers))):\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.unique_id==i]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "        #print(shipment_list)\n",
    "        for l in shipment_list:\n",
    "            number_of_ws = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "            shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "            #shipment_arrival_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_arrival_time_minutes'].unique()[0]\n",
    "            new_check = df_merge1[(df_merge1.unique_id==i)]\n",
    "            shipment_arrival_time_minutes_loop = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "            model.addConstr(quicksum(t*z[l,m,t] for m in range(1, ws_list+1) for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop)) >=\n",
    "                            quicksum((k + df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'] + \n",
    "                                      df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['m'])*x[i,b,k] for b in BreakDownZoneName_list1 for k in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])), name=\"Constraint 9\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in (two_drop_zones_ULD_Numbers):\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    bd_NRML_list = [k for k in BreakDownZoneName_list1 if 'NRML' in k]\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.unique_id==i]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "        #print(shipment_list)\n",
    "        for l in shipment_list:\n",
    "            number_of_ws = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "            shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "            #shipment_arrival_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_arrival_time_minutes'].unique()[0]\n",
    "            new_check = df_merge1[(df_merge1.unique_id==i)]\n",
    "            shipment_arrival_time_minutes_loop = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "            model.addConstr(quicksum(t*z[l,m,t] for m in range(1, ws_list+1) for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop)) >=\n",
    "                            quicksum((k + df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'] + \n",
    "                                      df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['m'])*x[i,b,k] for b in bd_NRML_list for k in range((min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==b]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==b].iloc[0]['handling_m'])), name=\"Constraint Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = 0\n",
    "for i in ArrivalULDNumber_list:\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.ArrivalULDNumber==i]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "    #print(shipment_list)\n",
    "        for l in shipment_list:\n",
    "        #print(int(i),str(j))\n",
    "            number_of_ws = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "            shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "            obj += quicksum(t*z[l,m,t] for m in range(1,number_of_ws+1) for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimize a model with 22165 rows, 205838 columns and 2934086 nonzeros\n",
      "Variable types: 0 continuous, 205838 integer (205838 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [5e-04, 3e+07]\n",
      "  Objective range  [3e+07, 3e+07]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 3e+07]\n",
      "Presolve removed 4228 rows and 45306 columns (presolve time = 5s) ...\n",
      "Presolve removed 4942 rows and 77243 columns (presolve time = 11s) ...\n",
      "Presolve removed 4990 rows and 78517 columns (presolve time = 17s) ...\n",
      "Presolve removed 21658 rows and 202650 columns\n",
      "Presolve time: 19.76s\n",
      "Presolved: 507 rows, 3188 columns, 10367 nonzeros\n",
      "Variable types: 0 continuous, 3188 integer (3188 binary)\n",
      "Found heuristic solution: objective 1.715094e+10\n",
      "\n",
      "Root simplex log...\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "       0    1.1784918e+10   3.804300e+04   0.000000e+00     21s\n",
      "     506    1.7150927e+10   0.000000e+00   0.000000e+00     21s\n",
      "\n",
      "Root relaxation: objective 1.715093e+10, 506 iterations, 0.03 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0 1.7151e+10    0   29 1.7151e+10 1.7151e+10  0.00%     -   20s\n",
      "\n",
      "Explored 1 nodes (569 simplex iterations) in 20.79 seconds\n",
      "Thread count was 4 (of 4 available processors)\n",
      "\n",
      "Solution count 1: 1.71509e+10 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 1.715093576300e+10, best bound 1.715092684843e+10, gap 0.0001%\n"
     ]
    }
   ],
   "source": [
    "model.setObjective(obj, GRB.MINIMIZE)\n",
    "model.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-06-30 01:50:25.404908\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Computing Irreducible Inconsistent Subsystem (IIS)...\n",
      "\n",
      "      Constraints           Bounds       Runtime\n",
      "     Min       Max       Min      Max\n",
      "------------------------------------------------\n",
      "        0     3844         0        0         0s\n",
      "        3        3         0        0         2s\n",
      "\n",
      "IIS computed: 3 constraints, 0 bounds\n",
      "IIS runtime: 1.58 seconds\n"
     ]
    }
   ],
   "source": [
    "model.computeIIS()\n",
    "model.write('Infesible_Model.ilp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(model.status== GRB.INFEASIBLE):\n",
    "    model.computeIIS()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: constraint name \"Constraint 9\" has a space\n"
     ]
    }
   ],
   "source": [
    "model.write('Full Model.lp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ULD 21231_25671250 assigned to BD BD NRML-4 at 25671312\n",
      "ULD 21231_25671250 assigned to BD BD NML-3 at 25671280\n",
      "ULD 24321_25671250 assigned to BD BD NRML-1 at 25671270\n",
      "ULD 42309_25671550 assigned to BD BD NRML-1 at 25671570\n",
      "ULD 49809_25673105 assigned to BD BD NRML-4 at 25673119\n",
      "ULD 44509_25671785 assigned to BD BD NRML-1 at 25671790\n",
      "ULD 44509_25673225 assigned to BD BD NRML-1 at 25673230\n",
      "ULD 34521_25671730 assigned to BD BD NRML-1 at 25671750\n",
      "ULD 34576_25671730 assigned to BD BD NRML-1 at 25671750\n",
      "ULD 34849_25673345 assigned to BD BD NRML-1 at 25673350\n",
      "ULD 53252_25671910 assigned to BD BD NRML-4 at 25671923\n",
      "ULD 92841_25672010 assigned to BD BD NRML-4 at 25672027\n",
      "ULD 92852_25680385 assigned to BD BD NRML-1 at 25680390\n",
      "ULD 92852_25671325 assigned to BD BD NRML-1 at 25671330\n",
      "ULD 92854_25674170 assigned to BD BD NRML-4 at 25674185\n",
      "ULD 92854_25672730 assigned to BD B BD NRML-1 at 25672750\n",
      "ULD 92862_25671510 assigned to BD BD NRML-1 at 25671530\n",
      "ULD 92863_25672040 assigned to BD BD NRML-1 at 25672050\n",
      "ULD 92874_25673595 assigned to BD BD NRML-1 at 25673610\n",
      "ULD 55210_25671480 assigned to BD BD NRML-1 at 25671490\n",
      "ULD 55233_25671900 assigned to BD BD NRML-1 at 25671910\n",
      "ULD 55234_25671960 assigned to BD BD NRML-1 at 25671970\n",
      "ULD 55244_25672140 assigned to BD BD NRML-1 at 25672150\n",
      "ULD 55242_25674680 assigned to BD BD NRML-1 at 25674690\n",
      "ULD 55256_25674295 assigned to BD BD NRML-1 at 25674310\n",
      "ULD 12199_25674165 assigned to BD BD NRML-1 at 25674170\n",
      "ULD 55267_25672735 assigned to BD B BD NRML-1 at 25672750\n",
      "ULD 55267_25674175 assigned to BD BD NRML-1 at 25674190\n",
      "ULD 51207_25670350 assigned to BD BD NRML-4 at 25670363\n",
      "ULD 51217_25673010 assigned to BD BD NRML-4 at 25673028\n",
      "ULD 51217_25673700 assigned to BD BD NRML-1 at 25673710\n",
      "ULD 51216_25671740 assigned to BD BD NRML-1 at 25671750\n",
      "ULD 51219_25672035 assigned to BD BD NRML-1 at 25672050\n",
      "ULD 51227_25672240 assigned to BD BD NRML-1 at 25672250\n",
      "ULD 51227_25675120 assigned to BD BD NRML-1 at 25675130\n",
      "ULD 51228_25671360 assigned to BD BD NRML-4 at 25671403\n",
      "ULD 51228_25671360 assigned to BD BD NML-3 at 25671380\n",
      "ULD 51229_25674220 assigned to BD BD NRML-1 at 25674230\n",
      "ULD 51230_25671470 assigned to BD BD NRML-1 at 25671490\n",
      "ULD 59235_25673080 assigned to BD BD NRML-1 at 25673090\n",
      "ULD 59247_25673640 assigned to BD BD NRML-1 at 25673650\n",
      "ULD 59247_25672200 assigned to BD BD NRML-1 at 25672210\n",
      "ULD 59248_25671295 assigned to BD BD NRML-1 at 25671310\n",
      "ULD 59259_25671330 assigned to BD BD NRML-1 at 25671350\n",
      "ULD 59268_25672210 assigned to BD BD NRML-1 at 25672230\n",
      "ULD 59271_25671300 assigned to BD BD NRML-1 at 25671310\n",
      "ULD 43330_25673080 assigned to BD BD NRML-1 at 25673090\n",
      "ULD 59290_25672955 assigned to BD BD NRML-1 at 25672970\n",
      "ULD 44913_25672695 assigned to BD BD NRML-1 at 25672710\n",
      "ULD 44914_25673110 assigned to BD BD NRML-1 at 25673130\n",
      "ULD 44915_25672785 assigned to BD BD NML-3 at 25672800\n",
      "ULD 44916_25673085 assigned to BD BD NRML-1 at 25673090\n",
      "ULD 54552_25673030 assigned to BD BD NRML-1 at 25673050\n",
      "ULD 44924_25673055 assigned to BD BD NRML-1 at 25673070\n",
      "ULD 44925_25672940 assigned to BD BD NRML-1 at 25672950\n",
      "ULD 44926_25671855 assigned to BD BD NRML-1 at 25671870\n",
      "ULD 44927_25673150 assigned to BD BD NRML-1 at 25673170\n",
      "ULD 46300_25672740 assigned to BD B BD NRML-1 at 25672774\n",
      "ULD 44935_25674820 assigned to BD BD NRML-1 at 25674830\n",
      "ULD 44947_25672925 assigned to BD BD NRML-1 at 25672930\n",
      "ULD 44948_25671925 assigned to BD BD NRML-1 at 25671930\n",
      "ULD 44948_25673365 assigned to BD BD NRML-1 at 25673370\n",
      "ULD 44949_25673140 assigned to BD BD NRML-1 at 25673150\n",
      "ULD 44957_25672770 assigned to BD BD NML-3 at 25672800\n",
      "ULD 44958_25672860 assigned to BD BD NRML-1 at 25672870\n",
      "ULD 44959_25673340 assigned to BD BD NRML-1 at 25673350\n",
      "ULD 49808_25671320 assigned to BD BD NRML-1 at 25671330\n",
      "ULD 49811_25672390 assigned to BD BD NRML-4 at 25672404\n",
      "ULD 49820_25672855 assigned to BD BD NRML-1 at 25672870\n",
      "ULD 49822_25673150 assigned to BD BD NRML-1 at 25673170\n",
      "ULD 49831_25673655 assigned to BD BD NRML-1 at 25673670\n",
      "ULD 49832_25673110 assigned to BD BD NRML-1 at 25673130\n",
      "ULD 49833_25672620 assigned to BD BD NRML-1 at 25672630\n",
      "ULD 49841_25674465 assigned to BD BD NRML-1 at 25674470\n",
      "ULD 32109_25673205 assigned to BD BD NRML-1 at 25673210\n",
      "ULD 80114_25680255 assigned to BD BD NRML-1 at 25680270\n",
      "ULD 80136_25671540 assigned to BD BD NRML-1 at 25671550\n",
      "ULD 80137_25672200 assigned to BD BD NRML-1 at 25672210\n",
      "ULD 80146_25671865 assigned to BD BD NRML-1 at 25671870\n",
      "ULD 80157_25672800 assigned to BD BD NRML-1 at 25672810\n",
      "ULD 80158_25671650 assigned to BD B BD NRML-1 at 25671670\n",
      "ULD 80159_25672200 assigned to BD BD NRML-1 at 25672210\n",
      "ULD 80168_25671750 assigned to BD BD NRML-4 at 25671767\n",
      "ULD 80169_25672890 assigned to BD BD NRML-4 at 25672911\n",
      "ULD 80182_25672060 assigned to BD BD NRML-1 at 25672070\n",
      "ULD 80190_25673055 assigned to BD BD NRML-1 at 25673070\n",
      "ULD 80192_25671740 assigned to BD BD NRML-1 at 25671750\n",
      "ULD 80224_25671865 assigned to BD BD NRML-1 at 25671870\n",
      "ULD 80225_25673280 assigned to BD BD NRML-1 at 25673290\n",
      "ULD 80234_25676310 assigned to BD BD NRML-1 at 25676330\n",
      "ULD 80235_25672890 assigned to BD BD NRML-4 at 25672911\n",
      "ULD 80236_25672895 assigned to BD BD NRML-4 at 25672911\n",
      "ULD 80245_25671830 assigned to BD BD NRML-4 at 25671845\n",
      "ULD 80247_25672645 assigned to BD BD NRML-1 at 25672650\n",
      "ULD 80248_25673690 assigned to BD BD NRML-4 at 25673704\n",
      "ULD 80248_25673280 assigned to BD BD NRML-1 at 25673290\n",
      "ULD 80258_25673865 assigned to BD BD NRML-1 at 25673870\n",
      "ULD 80280_25675800 assigned to BD BD NRML-1 at 25675810\n",
      "ULD 80314_25672230 assigned to BD BD NRML-4 at 25672248\n",
      "ULD 23127_25671250 assigned to BD BD NRML-1 at 25671270\n",
      "ULD 33565_25672775 assigned to BD B BD NRML-1 at 25672798\n",
      "ULD 42313_25672985 assigned to BD BD NRML-1 at 25672990\n",
      "ULD 42371_25673045 assigned to BD BD NRML-1 at 25673050\n",
      "ULD 42371_25671605 assigned to BD BD NRML-1 at 25671610\n",
      "ULD 34842_25673285 assigned to BD BD NRML-1 at 25673290\n",
      "ULD 53456_25671910 assigned to BD BD NRML-4 at 25671923\n",
      "ULD 53409_25671970 assigned to BD BD NRML-4 at 25671988\n",
      "ULD 76421_25672030 assigned to BD BD NRML-1 at 25672050\n",
      "ULD 76631_25672090 assigned to BD BD NRML-4 at 25672105\n",
      "ULD 76609_25673545 assigned to BD BD NRML-1 at 25673550\n",
      "ULD 76932_25674160 assigned to BD BD NRML-1 at 25674170\n",
      "ULD 92812_25673345 assigned to BD BD NRML-1 at 25673350\n",
      "ULD 92812_25671905 assigned to BD BD NRML-1 at 25671910\n",
      "ULD 92859_25671385 assigned to BD BD NRML-1 at 25671390\n",
      "ULD 92866_25673510 assigned to BD BD NRML-1 at 25673530\n",
      "ULD 92868_25674130 assigned to BD BD NRML-4 at 25674146\n",
      "ULD 92868_25672690 assigned to BD BD NRML-4 at 25672703\n",
      "ULD 92869_25672750 assigned to BD B BD NRML-1 at 25672774\n",
      "ULD 92869_25674190 assigned to BD BD NRML-1 at 25674210\n",
      "ULD 92870_25671240 assigned to BD BD NRML-1 at 25671250\n",
      "ULD 42424_25671240 assigned to BD BD NRML-1 at 25671250\n",
      "ULD 92871_25671360 assigned to BD BD NRML-1 at 25671370\n",
      "ULD 55215_25671540 assigned to BD BD NRML-1 at 25671550\n",
      "ULD 55219_25671600 assigned to BD BD NRML-1 at 25671610\n",
      "ULD 55224_25673170 assigned to BD BD NRML-4 at 25673184\n",
      "ULD 55224_25671730 assigned to BD BD NRML-1 at 25671750\n",
      "ULD 55226_25671720 assigned to BD BD NRML-1 at 25671730\n",
      "ULD 55228_25673230 assigned to BD BD NRML-4 at 25673249\n",
      "ULD 55230_25671840 assigned to BD BD NRML-1 at 25671850\n",
      "ULD 55235_25672020 assigned to BD BD NRML-1 at 25672030\n",
      "ULD 55239_25672080 assigned to BD BD NRML-1 at 25672090\n",
      "ULD 55251_25673535 assigned to BD BD NRML-4 at 25673548\n",
      "ULD 55257_25673120 assigned to BD BD NRML-1 at 25673130\n",
      "ULD 55258_25676300 assigned to BD BD NRML-1 at 25676310\n",
      "ULD 55259_25671520 assigned to BD BD NRML-1 at 25671530\n",
      "ULD 96654_25672900 assigned to BD BD NRML-4 at 25672924\n",
      "ULD 74544_25673460 assigned to BD BD NRML-1 at 25673470\n",
      "ULD 11131_25672715 assigned to BD BD NRML-4 at 25672729\n",
      "ULD 55260_25671240 assigned to BD BD NRML-1 at 25671250\n",
      "ULD 55261_25672210 assigned to BD BD NRML-1 at 25672230\n",
      "ULD 55263_25679125 assigned to BD BD NRML-1 at 25679130\n",
      "ULD 55268_25671385 assigned to BD BD NRML-1 at 25671390\n",
      "ULD 55269_25673095 assigned to BD BD NRML-1 at 25673110\n",
      "ULD 51200_25671655 assigned to BD B BD NRML-1 at 25671670\n",
      "ULD 51201_25671775 assigned to BD BD NRML-1 at 25671790\n",
      "ULD 51202_25671895 assigned to BD BD NRML-1 at 25671910\n",
      "ULD 51203_25672135 assigned to BD BD NRML-1 at 25672150\n",
      "ULD 51209_25673055 assigned to BD BD NRML-1 at 25673070\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ULD 51209_25674495 assigned to BD BD NRML-1 at 25674510\n",
      "ULD 51210_25671890 assigned to BD BD NRML-1 at 25671910\n",
      "ULD 51212_25671720 assigned to BD BD NRML-1 at 25671730\n",
      "ULD 51213_25672000 assigned to BD BD NRML-1 at 25672010\n",
      "ULD 51214_25673155 assigned to BD BD NRML-1 at 25673170\n",
      "ULD 51215_25674260 assigned to BD BD NRML-1 at 25674270\n",
      "ULD 51221_25673350 assigned to BD BD NRML-4 at 25673366\n",
      "ULD 51223_25672075 assigned to BD BD NRML-1 at 25672090\n",
      "ULD 51233_25674320 assigned to BD BD NRML-1 at 25674330\n",
      "ULD 51235_25671570 assigned to BD BD NRML-4 at 25671585\n",
      "ULD 59239_25671760 assigned to BD BD NRML-1 at 25671770\n",
      "ULD 59243_25671650 assigned to BD B BD NRML-1 at 25671670\n",
      "ULD 59243_25673090 assigned to BD BD NRML-4 at 25673106\n",
      "ULD 59243_25673345 assigned to BD BD NRML-1 at 25673390\n",
      "ULD 59243_25673345 assigned to BD BD NML-3 at 25673360\n",
      "ULD 59245_25672025 assigned to BD BD NRML-1 at 25672030\n",
      "ULD 59254_25671855 assigned to BD BD NRML-1 at 25671870\n",
      "ULD 59255_25671980 assigned to BD BD NRML-1 at 25671990\n",
      "ULD 59263_25672825 assigned to BD BD NRML-1 at 25672830\n",
      "ULD 59264_25674540 assigned to BD BD NRML-1 at 25674550\n",
      "ULD 59265_25671800 assigned to BD BD NRML-1 at 25671810\n",
      "ULD 59284_25673080 assigned to BD BD NRML-1 at 25673090\n",
      "ULD 59285_25672610 assigned to BD BD NRML-4 at 25672625\n",
      "ULD 59287_25672710 assigned to BD BD NRML-4 at 25672729\n",
      "ULD 59288_25672015 assigned to BD BD NRML-1 at 25672030\n",
      "ULD 44909_25674600 assigned to BD BD NRML-1 at 25674610\n",
      "ULD 44911_25671385 assigned to BD BD NRML-1 at 25671390\n",
      "ULD 44912_25673030 assigned to BD BD NRML-1 at 25673050\n",
      "ULD 44917_25672775 assigned to BD B BD NRML-1 at 25672798\n",
      "ULD 44918_25673115 assigned to BD BD NRML-1 at 25673130\n",
      "ULD 44919_25672690 assigned to BD BD NRML-4 at 25672703\n",
      "ULD 44921_25673195 assigned to BD BD NRML-1 at 25673210\n",
      "ULD 44923_25672915 assigned to BD BD NRML-1 at 25672930\n",
      "ULD 44928_25672740 assigned to BD B BD NRML-1 at 25672774\n",
      "ULD 44929_25673330 assigned to BD BD NRML-1 at 25673350\n",
      "ULD 44932_25673240 assigned to BD BD NRML-1 at 25673250\n",
      "ULD 44932_25671800 assigned to BD BD NRML-1 at 25671810\n",
      "ULD 44934_25672740 assigned to BD B BD NRML-1 at 25672774\n",
      "ULD 44941_25671400 assigned to BD BD NRML-1 at 25671410\n",
      "ULD 44942_25673260 assigned to BD BD NRML-1 at 25673270\n",
      "ULD 44944_25671535 assigned to BD BD NRML-1 at 25671550\n",
      "ULD 44944_25672975 assigned to BD BD NRML-4 at 25672989\n",
      "ULD 44945_25672775 assigned to BD B BD NRML-1 at 25672798\n",
      "ULD 44951_25672555 assigned to BD BD NRML-1 at 25672570\n",
      "ULD 44953_25672555 assigned to BD BD NRML-1 at 25672570\n",
      "ULD 44953_25673995 assigned to BD BD NRML-1 at 25674010\n",
      "ULD 54322_25673175 assigned to BD BD NRML-1 at 25673190\n",
      "ULD 54323_25673255 assigned to BD BD NRML-1 at 25673270\n",
      "ULD 54324_25672620 assigned to BD BD NRML-1 at 25672630\n",
      "ULD 49802_25673485 assigned to BD BD NRML-1 at 25673490\n",
      "ULD 49803_25673675 assigned to BD BD NRML-1 at 25673690\n",
      "ULD 49804_25673610 assigned to BD BD NRML-4 at 25673626\n",
      "ULD 49806_25672020 assigned to BD BD NRML-1 at 25672030\n",
      "ULD 49813_25673700 assigned to BD BD NRML-1 at 25673710\n",
      "ULD 49813_25675140 assigned to BD BD NRML-1 at 25675150\n",
      "ULD 49815_25672940 assigned to BD BD NRML-1 at 25672950\n",
      "ULD 49815_25674380 assigned to BD BD NRML-1 at 25674430\n",
      "ULD 49815_25674380 assigned to BD BD NML-3 at 25674400\n",
      "ULD 49817_25672770 assigned to BD BD NML-3 at 25672800\n",
      "ULD 49817_25674210 assigned to BD BD NRML-4 at 25674224\n",
      "ULD 49823_25672595 assigned to BD BD NRML-1 at 25672610\n",
      "ULD 49824_25674200 assigned to BD BD NRML-1 at 25674210\n",
      "ULD 49826_25673785 assigned to BD BD NRML-1 at 25673790\n",
      "ULD 49827_25673690 assigned to BD BD NRML-4 at 25673704\n",
      "ULD 49828_25671275 assigned to BD BD NRML-1 at 25671290\n",
      "ULD 49835_25673235 assigned to BD BD NRML-4 at 25673249\n",
      "ULD 49835_25674675 assigned to BD BD NRML-1 at 25674690\n",
      "ULD 49836_25672640 assigned to BD BD NRML-1 at 25672650\n",
      "ULD 49837_25672855 assigned to BD BD NRML-1 at 25672870\n",
      "ULD 49839_25674340 assigned to BD BD NRML-1 at 25674350\n",
      "ULD 49846_25672600 assigned to BD BD NRML-1 at 25672610\n",
      "ULD 80117_25675230 assigned to BD BD NRML-1 at 25675250\n",
      "ULD 80118_25673045 assigned to BD BD NRML-1 at 25673050\n",
      "ULD 80119_25672275 assigned to BD BD NRML-1 at 25672290\n",
      "ULD 80121_25673055 assigned to BD BD NRML-1 at 25673070\n",
      "ULD 80129_25672780 assigned to BD B BD NRML-1 at 25672798\n",
      "ULD 80131_25671700 assigned to BD BD NRML-1 at 25671710\n",
      "ULD 80132_25671365 assigned to BD BD NRML-1 at 25671370\n",
      "ULD 80133_25672150 assigned to BD BD NRML-1 at 25672170\n",
      "ULD 80134_25671425 assigned to BD BD NRML-1 at 25671430\n",
      "ULD 80139_25673160 assigned to BD BD NRML-1 at 25673170\n",
      "ULD 80141_25674420 assigned to BD BD NRML-1 at 25674430\n",
      "ULD 80142_25672765 assigned to BD B BD NRML-1 at 25672798\n",
      "ULD 80143_25672185 assigned to BD BD NRML-1 at 25672190\n",
      "ULD 80144_25671380 assigned to BD BD NRML-1 at 25671390\n",
      "ULD 80151_25671760 assigned to BD BD NRML-1 at 25671770\n",
      "ULD 80152_25672060 assigned to BD BD NRML-1 at 25672070\n",
      "ULD 80155_25672865 assigned to BD BD NRML-1 at 25672870\n",
      "ULD 80156_25671355 assigned to BD BD NRML-1 at 25671370\n",
      "ULD 80161_25671340 assigned to BD BD NRML-1 at 25671350\n",
      "ULD 80162_25672960 assigned to BD BD NRML-1 at 25672970\n",
      "ULD 80164_25671860 assigned to BD BD NRML-1 at 25671870\n",
      "ULD 80167_25672975 assigned to BD BD NRML-1 at 25672990\n",
      "ULD 80173_25673040 assigned to BD BD NRML-1 at 25673050\n",
      "ULD 80175_25672185 assigned to BD BD NRML-1 at 25672190\n",
      "ULD 80176_25672900 assigned to BD BD NRML-4 at 25672924\n",
      "ULD 80177_25671650 assigned to BD B BD NRML-1 at 25671670\n",
      "ULD 80178_25672190 assigned to BD BD NRML-1 at 25672210\n",
      "ULD 80183_25671315 assigned to BD BD NRML-1 at 25671330\n",
      "ULD 80185_25676090 assigned to BD BD NRML-1 at 25676110\n",
      "ULD 80194_25671915 assigned to BD BD NRML-1 at 25671930\n",
      "ULD 80196_25673055 assigned to BD BD NRML-1 at 25673070\n",
      "ULD 80197_25671650 assigned to BD B BD NRML-1 at 25671670\n",
      "ULD 80199_25674595 assigned to BD BD NRML-1 at 25674610\n",
      "ULD 80220_25673650 assigned to BD BD NRML-4 at 25673665\n",
      "ULD 80220_25672210 assigned to BD BD NRML-1 at 25672230\n",
      "ULD 80227_25673100 assigned to BD BD NRML-1 at 25673110\n",
      "ULD 80228_25673020 assigned to BD BD NRML-1 at 25673030\n",
      "ULD 80230_25673690 assigned to BD BD NRML-4 at 25673704\n",
      "ULD 80232_25673220 assigned to BD BD NRML-1 at 25673230\n",
      "ULD 80233_25672900 assigned to BD BD NRML-4 at 25672924\n",
      "ULD 80241_25673720 assigned to BD BD NRML-1 at 25673730\n",
      "ULD 80241_25674110 assigned to BD BD NRML-1 at 25674130\n",
      "ULD 80243_25672335 assigned to BD BD NRML-1 at 25672350\n",
      "ULD 80249_25676930 assigned to BD BD NRML-1 at 25676950\n",
      "ULD 80251_25673765 assigned to BD BD NRML-1 at 25673770\n",
      "ULD 80253_25671660 assigned to BD BD NML-3 at 25671680\n",
      "ULD 80255_25676745 assigned to BD BD NRML-1 at 25676750\n",
      "ULD 80262_25672590 assigned to BD BD NRML-1 at 25672610\n",
      "ULD 80265_25675320 assigned to BD BD NRML-1 at 25675330\n",
      "ULD 80271_25671210 assigned to BD BD NRML-1 at 25671230\n",
      "ULD 80274_25674480 assigned to BD BD NRML-1 at 25674490\n",
      "ULD 80277_25675740 assigned to BD BD NRML-1 at 25675750\n",
      "ULD 80283_25679820 assigned to BD BD NRML-1 at 25679830\n",
      "ULD 80286_25672470 assigned to BD BD NRML-1 at 25672490\n",
      "ULD 80295_25673695 assigned to BD BD NRML-1 at 25673710\n",
      "ULD 80304_25674310 assigned to BD BD NRML-4 at 25674328\n",
      "ULD 80306_25674135 assigned to BD BD NRML-1 at 25674150\n",
      "ULD 80308_25671320 assigned to BD BD NRML-1 at 25671330\n",
      "ULD 80310_25672740 assigned to BD B BD NRML-1 at 25672774\n",
      "ULD 80316_25672620 assigned to BD BD NRML-1 at 25672630\n",
      "ULD 76901_25672765 assigned to BD BD NML-3 at 25672780\n",
      "ULD 92832_25671560 assigned to BD BD NML-3 at 25671580\n",
      "ULD 55237_25670820 assigned to BD BD NML-3 at 25670840\n",
      "ULD 51225_25672120 assigned to BD BD NML-3 at 25672140\n",
      "ULD 59252_25671665 assigned to BD BD NML-3 at 25671680\n",
      "ULD 59257_25672055 assigned to BD BD NML-3 at 25672080\n",
      "ULD 59281_25674500 assigned to BD BD NML-3 at 25674520\n",
      "ULD 59286_25671765 assigned to BD BD NML-3 at 25671780\n",
      "ULD 59286_25673205 assigned to BD BD NML-3 at 25673220\n",
      "ULD 49807_25673265 assigned to BD BD NML-3 at 25673280\n",
      "ULD 80179_25671805 assigned to BD BD NML-3 at 25671820\n",
      "ULD 80226_25672095 assigned to BD BD NML-3 at 25672120\n",
      "ULD 80249_25672610 assigned to BD BD NML-3 at 25672640\n"
     ]
    }
   ],
   "source": [
    "for i in uld_uniqueid_list:\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    #print(i, \"in\", BreakDownZoneName_list1)\n",
    "    for j in BreakDownZoneName_list1:\n",
    "        #print(int(i),str(j))\n",
    "        new_check = df_merge1[(df_merge1.unique_id==i) & (df_merge1.BreakDownZoneName==j)]\n",
    "        shipment_arrival_time_minutes_loop = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "        shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "        for k in range((min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']):\n",
    "            if x[i,j,k].x > 0.5:\n",
    "                print(\"ULD\", i, \"assigned to BD\", j, \"at\", k)\n",
    "                #print(k)\n",
    "            #if(i == '51228_25671360'):\n",
    "                #print(x)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# visualize the optimal assignment\n",
    "for i in uld_uniqueid_list:\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    for j in BreakDownZoneName_list1:\n",
    "        #print(int(i),str(j))\n",
    "        new_check = df_merge1[(df_merge1.unique_id==i) & (df_merge1.BreakDownZoneName==j)]\n",
    "        shipment_arrival_time_minutes_loop = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "        shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "        for k in range((min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']):\n",
    "            if x[i,j,k].x > 0.5:\n",
    "                #print(k)\n",
    "                #print(\"DZ\", i, \"assigned to BD\", j, \"at\", (shipment_arrival_time_minutes_loop+k))\n",
    "                plt.plot(k, j, \"o\")\n",
    "                #plt.annotate(i, (k,j))\n",
    "# for j in range(m):    \n",
    "#     plt.plot(1, j, \"o\")\n",
    "# for i in range(n):\n",
    "#     for j in range(m):\n",
    "#         if x[i,j].x > 0.5:\n",
    "#             plt.plot([0,1], [i,j], \"k\")# todo\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shipment 1 assigned to WS 1 at 25674015\n",
      "Shipment 7 assigned to WS 1 at 25677405\n",
      "Shipment 21 assigned to WS 1 at 25673540\n",
      "Shipment 30 assigned to WS 1 at 25677620\n",
      "Shipment 36 assigned to WS 1 at 25673640\n",
      "Shipment 37 assigned to WS 1 at 25673640\n",
      "Shipment 38 assigned to WS 1 at 25673640\n",
      "Shipment 48 assigned to WS 1 at 25675545\n",
      "Shipment 49 assigned to WS 1 at 25675545\n",
      "Shipment 50 assigned to WS 1 at 25675545\n",
      "Shipment 66 assigned to WS 1 at 25674510\n",
      "Shipment 74 assigned to WS 1 at 25673670\n",
      "Shipment 75 assigned to WS 1 at 25673670\n",
      "Shipment 82 assigned to WS 1 at 25676290\n",
      "Shipment 85 assigned to WS 1 at 25677090\n",
      "Shipment 89 assigned to WS 1 at 25674800\n",
      "Shipment 90 assigned to WS 1 at 25674800\n",
      "Shipment 91 assigned to WS 1 at 25674800\n",
      "Shipment 92 assigned to WS 1 at 25674800\n",
      "Shipment 93 assigned to WS 1 at 25674800\n",
      "Shipment 105 assigned to WS 3 at 25677470\n",
      "Shipment 106 assigned to WS 2 at 25677470\n",
      "Shipment 121 assigned to WS 1 at 25673405\n",
      "Shipment 2 assigned to WS 1 at 25674015\n",
      "Shipment 77 assigned to WS 1 at 25673670\n",
      "Shipment 109 assigned to WS 3 at 25677470\n",
      "Shipment 110 assigned to WS 1 at 25677470\n",
      "Shipment 3 assigned to WS 1 at 25674015\n",
      "Shipment 541 assigned to WS 1 at 25675805\n",
      "Shipment 4 assigned to WS 1 at 25674015\n",
      "Shipment 5 assigned to WS 1 at 25674015\n",
      "Shipment 6 assigned to WS 1 at 25674015\n",
      "Shipment 8 assigned to WS 2 at 25677405\n",
      "Shipment 454 assigned to WS 1 at 25674090\n",
      "Shipment 9 assigned to WS 2 at 25677405\n",
      "Shipment 24 assigned to WS 1 at 25673540\n",
      "Shipment 112 assigned to WS 3 at 25677470\n",
      "Shipment 243 assigned to WS 1 at 25681259\n",
      "Shipment 486 assigned to WS 1 at 25673415\n",
      "Shipment 10 assigned to WS 3 at 25677405\n",
      "Shipment 11 assigned to WS 3 at 25677405\n",
      "Shipment 12 assigned to WS 1 at 25677405\n",
      "Shipment 13 assigned to WS 3 at 25677405\n",
      "Shipment 95 assigned to WS 1 at 25674800\n",
      "Shipment 262 assigned to WS 2 at 25672560\n",
      "Shipment 263 assigned to WS 1 at 25672560\n",
      "Shipment 264 assigned to WS 3 at 25672560\n",
      "Shipment 625 assigned to WS 1 at 25672625\n",
      "Shipment 14 assigned to WS 1 at 25677405\n",
      "Shipment 15 assigned to WS 1 at 25677405\n",
      "Shipment 16 assigned to WS 2 at 25677405\n",
      "Shipment 17 assigned to WS 2 at 25677405\n",
      "Shipment 18 assigned to WS 2 at 25677405\n",
      "Shipment 19 assigned to WS 2 at 25677405\n",
      "Shipment 20 assigned to WS 1 at 25677405\n",
      "Shipment 22 assigned to WS 1 at 25673540\n",
      "Shipment 218 assigned to WS 1 at 25672560\n",
      "Shipment 219 assigned to WS 1 at 25672560\n",
      "Shipment 220 assigned to WS 1 at 25672560\n",
      "Shipment 221 assigned to WS 1 at 25672560\n",
      "Shipment 222 assigned to WS 1 at 25672560\n",
      "Shipment 223 assigned to WS 2 at 25672560\n",
      "Shipment 419 assigned to WS 3 at 25675844\n",
      "Shipment 427 assigned to WS 3 at 25674980\n",
      "Shipment 428 assigned to WS 1 at 25674390\n",
      "Shipment 438 assigned to WS 2 at 25674060\n",
      "Shipment 23 assigned to WS 1 at 25673540\n",
      "Shipment 31 assigned to WS 1 at 25677620\n",
      "Shipment 225 assigned to WS 1 at 25672560\n",
      "Shipment 226 assigned to WS 2 at 25672560\n",
      "Shipment 227 assigned to WS 3 at 25672560\n",
      "Shipment 228 assigned to WS 3 at 25672560\n",
      "Shipment 229 assigned to WS 3 at 25672560\n",
      "Shipment 230 assigned to WS 2 at 25672560\n",
      "Shipment 231 assigned to WS 3 at 25672560\n",
      "Shipment 232 assigned to WS 2 at 25672560\n",
      "Shipment 233 assigned to WS 1 at 25672560\n",
      "Shipment 440 assigned to WS 1 at 25674370\n",
      "Shipment 444 assigned to WS 1 at 25673385\n",
      "Shipment 446 assigned to WS 1 at 25674955\n",
      "Shipment 452 assigned to WS 1 at 25674090\n",
      "Shipment 25 assigned to WS 1 at 25673540\n",
      "Shipment 26 assigned to WS 1 at 25673540\n",
      "Shipment 27 assigned to WS 1 at 25673540\n",
      "Shipment 28 assigned to WS 1 at 25673540\n",
      "Shipment 29 assigned to WS 1 at 25673540\n",
      "Shipment 32 assigned to WS 1 at 25677620\n",
      "Shipment 56 assigned to WS 1 at 25675545\n",
      "Shipment 286 assigned to WS 3 at 25672560\n",
      "Shipment 287 assigned to WS 1 at 25672560\n",
      "Shipment 632 assigned to WS 2 at 25673670\n",
      "Shipment 643 assigned to WS 1 at 25674610\n",
      "Shipment 645 assigned to WS 1 at 25680890\n",
      "Shipment 647 assigned to WS 1 at 25672635\n",
      "Shipment 648 assigned to WS 1 at 25672635\n",
      "Shipment 33 assigned to WS 1 at 25677620\n",
      "Shipment 34 assigned to WS 1 at 25677620\n",
      "Shipment 35 assigned to WS 1 at 25677620\n",
      "Shipment 370 assigned to WS 3 at 25674059\n",
      "Shipment 371 assigned to WS 1 at 25674059\n",
      "Shipment 372 assigned to WS 3 at 25674059\n",
      "Shipment 373 assigned to WS 2 at 25674059\n",
      "Shipment 39 assigned to WS 1 at 25673640\n",
      "Shipment 40 assigned to WS 1 at 25673640\n",
      "Shipment 41 assigned to WS 1 at 25673640\n",
      "Shipment 51 assigned to WS 1 at 25675545\n",
      "Shipment 174 assigned to WS 3 at 25673470\n",
      "Shipment 184 assigned to WS 1 at 25673485\n",
      "Shipment 185 assigned to WS 1 at 25673485\n",
      "Shipment 195 assigned to WS 1 at 25676000\n",
      "Shipment 429 assigned to WS 1 at 25674390\n",
      "Shipment 430 assigned to WS 1 at 25674390\n",
      "Shipment 453 assigned to WS 1 at 25674090\n",
      "Shipment 466 assigned to WS 1 at 25672730\n",
      "Shipment 468 assigned to WS 1 at 25673510\n",
      "Shipment 470 assigned to WS 2 at 25673510\n",
      "Shipment 42 assigned to WS 1 at 25673640\n",
      "Shipment 43 assigned to WS 1 at 25673640\n",
      "Shipment 44 assigned to WS 1 at 25673640\n",
      "Shipment 347 assigned to WS 1 at 25672560\n",
      "Shipment 45 assigned to WS 1 at 25673640\n",
      "Shipment 46 assigned to WS 1 at 25673640\n",
      "Shipment 47 assigned to WS 1 at 25673640\n",
      "Shipment 52 assigned to WS 1 at 25675545\n",
      "Shipment 53 assigned to WS 1 at 25675545\n",
      "Shipment 76 assigned to WS 1 at 25673670\n",
      "Shipment 107 assigned to WS 2 at 25677470\n",
      "Shipment 108 assigned to WS 3 at 25677470\n",
      "Shipment 143 assigned to WS 1 at 25675050\n",
      "Shipment 176 assigned to WS 3 at 25673470\n",
      "Shipment 455 assigned to WS 1 at 25674090\n",
      "Shipment 472 assigned to WS 2 at 25674040\n",
      "Shipment 54 assigned to WS 1 at 25675545\n",
      "Shipment 55 assigned to WS 1 at 25675545\n",
      "Shipment 447 assigned to WS 1 at 25674955\n",
      "Shipment 448 assigned to WS 1 at 25674955\n",
      "Shipment 57 assigned to WS 1 at 25675545\n",
      "Shipment 58 assigned to WS 1 at 25675545\n",
      "Shipment 59 assigned to WS 1 at 25675545\n",
      "Shipment 60 assigned to WS 1 at 25675545\n",
      "Shipment 61 assigned to WS 1 at 25675545\n",
      "Shipment 62 assigned to WS 1 at 25675545\n",
      "Shipment 63 assigned to WS 1 at 25675545\n",
      "Shipment 64 assigned to WS 1 at 25675545\n",
      "Shipment 65 assigned to WS 1 at 25675545\n",
      "Shipment 337 assigned to WS 1 at 25681200\n",
      "Shipment 338 assigned to WS 1 at 25681200\n",
      "Shipment 339 assigned to WS 2 at 25681259\n",
      "Shipment 67 assigned to WS 1 at 25674510\n",
      "Shipment 509 assigned to WS 2 at 25675154\n",
      "Shipment 68 assigned to WS 1 at 25674510\n",
      "Shipment 252 assigned to WS 3 at 25672560\n",
      "Shipment 556 assigned to WS 1 at 25673605\n",
      "Shipment 69 assigned to WS 1 at 25674510\n",
      "Shipment 383 assigned to WS 1 at 25673870\n",
      "Shipment 70 assigned to WS 1 at 25674510\n",
      "Shipment 71 assigned to WS 1 at 25674510\n",
      "Shipment 72 assigned to WS 1 at 25674510\n",
      "Shipment 73 assigned to WS 1 at 25674510\n",
      "Shipment 78 assigned to WS 1 at 25673670\n",
      "Shipment 79 assigned to WS 1 at 25673670\n",
      "Shipment 80 assigned to WS 1 at 25673670\n",
      "Shipment 81 assigned to WS 1 at 25673670\n",
      "Shipment 304 assigned to WS 3 at 25681200\n",
      "Shipment 305 assigned to WS 3 at 25681200\n",
      "Shipment 306 assigned to WS 2 at 25681200\n",
      "Shipment 307 assigned to WS 1 at 25681200\n",
      "Shipment 308 assigned to WS 1 at 25681200\n",
      "Shipment 83 assigned to WS 1 at 25676290\n",
      "Shipment 368 assigned to WS 3 at 25681259\n",
      "Shipment 84 assigned to WS 1 at 25676290\n",
      "Shipment 86 assigned to WS 2 at 25677090\n",
      "Shipment 151 assigned to WS 1 at 25673820\n",
      "Shipment 274 assigned to WS 3 at 25672560\n",
      "Shipment 547 assigned to WS 3 at 25673518\n",
      "Shipment 87 assigned to WS 1 at 25677090\n",
      "Shipment 88 assigned to WS 1 at 25677090\n",
      "Shipment 451 assigned to WS 1 at 25674955\n",
      "Shipment 94 assigned to WS 1 at 25674800\n",
      "Shipment 111 assigned to WS 1 at 25677470\n",
      "Shipment 149 assigned to WS 3 at 25673820\n",
      "Shipment 96 assigned to WS 1 at 25674800\n",
      "Shipment 570 assigned to WS 1 at 25674040\n",
      "Shipment 631 assigned to WS 2 at 25673670\n",
      "Shipment 97 assigned to WS 1 at 25674800\n",
      "Shipment 98 assigned to WS 1 at 25674800\n",
      "Shipment 99 assigned to WS 1 at 25674800\n",
      "Shipment 100 assigned to WS 1 at 25674800\n",
      "Shipment 101 assigned to WS 1 at 25674800\n",
      "Shipment 102 assigned to WS 1 at 25674800\n",
      "Shipment 103 assigned to WS 1 at 25674800\n",
      "Shipment 104 assigned to WS 1 at 25674800\n",
      "Shipment 330 assigned to WS 3 at 25674059\n",
      "Shipment 331 assigned to WS 2 at 25674059\n",
      "Shipment 113 assigned to WS 2 at 25677470\n",
      "Shipment 334 assigned to WS 3 at 25681200\n",
      "Shipment 114 assigned to WS 3 at 25677470\n",
      "Shipment 115 assigned to WS 1 at 25677470\n",
      "Shipment 116 assigned to WS 2 at 25677470\n",
      "Shipment 117 assigned to WS 2 at 25677470\n",
      "Shipment 118 assigned to WS 3 at 25677470\n",
      "Shipment 119 assigned to WS 2 at 25677470\n",
      "Shipment 120 assigned to WS 1 at 25677470\n",
      "Shipment 375 assigned to WS 1 at 25681200\n",
      "Shipment 376 assigned to WS 2 at 25681200\n",
      "Shipment 122 assigned to WS 1 at 25673405\n",
      "Shipment 515 assigned to WS 3 at 25675154\n",
      "Shipment 568 assigned to WS 1 at 25674040\n",
      "Shipment 576 assigned to WS 1 at 25673525\n",
      "Shipment 123 assigned to WS 1 at 25673405\n",
      "Shipment 357 assigned to WS 2 at 25674059\n",
      "Shipment 124 assigned to WS 1 at 25673405\n",
      "Shipment 125 assigned to WS 1 at 25674545\n",
      "Shipment 128 assigned to WS 2 at 25676275\n",
      "Shipment 129 assigned to WS 1 at 25676275\n",
      "Shipment 130 assigned to WS 1 at 25676275\n",
      "Shipment 131 assigned to WS 1 at 25676275\n",
      "Shipment 132 assigned to WS 1 at 25676275\n",
      "Shipment 142 assigned to WS 1 at 25675050\n",
      "Shipment 146 assigned to WS 1 at 25673820\n",
      "Shipment 158 assigned to WS 1 at 25674695\n",
      "Shipment 160 assigned to WS 3 at 25675095\n",
      "Shipment 171 assigned to WS 1 at 25671970\n",
      "Shipment 173 assigned to WS 3 at 25673470\n",
      "Shipment 181 assigned to WS 1 at 25673405\n",
      "Shipment 126 assigned to WS 2 at 25674545\n",
      "Shipment 127 assigned to WS 2 at 25674545\n",
      "Shipment 133 assigned to WS 1 at 25676275\n",
      "Shipment 134 assigned to WS 1 at 25676275\n",
      "Shipment 280 assigned to WS 1 at 25672560\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shipment 281 assigned to WS 1 at 25672560\n",
      "Shipment 384 assigned to WS 1 at 25673870\n",
      "Shipment 135 assigned to WS 1 at 25676275\n",
      "Shipment 136 assigned to WS 1 at 25676275\n",
      "Shipment 137 assigned to WS 1 at 25676275\n",
      "Shipment 138 assigned to WS 2 at 25676275\n",
      "Shipment 139 assigned to WS 1 at 25676275\n",
      "Shipment 140 assigned to WS 1 at 25676275\n",
      "Shipment 141 assigned to WS 1 at 25676275\n",
      "Shipment 144 assigned to WS 1 at 25675050\n",
      "Shipment 145 assigned to WS 1 at 25675050\n",
      "Shipment 340 assigned to WS 3 at 25681200\n",
      "Shipment 341 assigned to WS 2 at 25681200\n",
      "Shipment 147 assigned to WS 3 at 25673820\n",
      "Shipment 236 assigned to WS 1 at 25672560\n",
      "Shipment 237 assigned to WS 3 at 25672560\n",
      "Shipment 238 assigned to WS 3 at 25672560\n",
      "Shipment 148 assigned to WS 1 at 25673820\n",
      "Shipment 175 assigned to WS 3 at 25673470\n",
      "Shipment 186 assigned to WS 1 at 25673485\n",
      "Shipment 150 assigned to WS 3 at 25673820\n",
      "Shipment 152 assigned to WS 1 at 25673820\n",
      "Shipment 153 assigned to WS 3 at 25673820\n",
      "Shipment 155 assigned to WS 3 at 25673820\n",
      "Shipment 294 assigned to WS 2 at 25674059\n",
      "Shipment 295 assigned to WS 2 at 25674059\n",
      "Shipment 154 assigned to WS 2 at 25673820\n",
      "Shipment 156 assigned to WS 3 at 25673820\n",
      "Shipment 157 assigned to WS 1 at 25673820\n",
      "Shipment 159 assigned to WS 1 at 25674695\n",
      "Shipment 420 assigned to WS 3 at 25675844\n",
      "Shipment 421 assigned to WS 3 at 25675844\n",
      "Shipment 161 assigned to WS 1 at 25675095\n",
      "Shipment 250 assigned to WS 3 at 25672560\n",
      "Shipment 162 assigned to WS 2 at 25675095\n",
      "Shipment 540 assigned to WS 1 at 25675805\n",
      "Shipment 163 assigned to WS 3 at 25675095\n",
      "Shipment 164 assigned to WS 2 at 25675095\n",
      "Shipment 165 assigned to WS 1 at 25675095\n",
      "Shipment 166 assigned to WS 2 at 25675095\n",
      "Shipment 167 assigned to WS 2 at 25675095\n",
      "Shipment 297 assigned to WS 2 at 25681200\n",
      "Shipment 168 assigned to WS 1 at 25675095\n",
      "Shipment 169 assigned to WS 2 at 25675095\n",
      "Shipment 367 assigned to WS 2 at 25674059\n",
      "Shipment 170 assigned to WS 2 at 25675095\n",
      "Shipment 172 assigned to WS 1 at 25671970\n",
      "Shipment 177 assigned to WS 3 at 25673470\n",
      "Shipment 178 assigned to WS 3 at 25673470\n",
      "Shipment 353 assigned to WS 2 at 25674059\n",
      "Shipment 179 assigned to WS 3 at 25673470\n",
      "Shipment 180 assigned to WS 3 at 25673470\n",
      "Shipment 182 assigned to WS 1 at 25673405\n",
      "Shipment 183 assigned to WS 1 at 25673485\n",
      "Shipment 191 assigned to WS 1 at 25675230\n",
      "Shipment 193 assigned to WS 1 at 25676000\n",
      "Shipment 194 assigned to WS 1 at 25676000\n",
      "Shipment 202 assigned to WS 1 at 25674525\n",
      "Shipment 203 assigned to WS 1 at 25674525\n",
      "Shipment 206 assigned to WS 3 at 25672560\n",
      "Shipment 207 assigned to WS 1 at 25672560\n",
      "Shipment 208 assigned to WS 2 at 25672560\n",
      "Shipment 209 assigned to WS 1 at 25672560\n",
      "Shipment 210 assigned to WS 3 at 25672560\n",
      "Shipment 211 assigned to WS 1 at 25672560\n",
      "Shipment 212 assigned to WS 1 at 25672560\n",
      "Shipment 213 assigned to WS 1 at 25672560\n",
      "Shipment 214 assigned to WS 1 at 25672560\n",
      "Shipment 215 assigned to WS 3 at 25672616\n",
      "Shipment 381 assigned to WS 1 at 25673870\n",
      "Shipment 187 assigned to WS 1 at 25673485\n",
      "Shipment 188 assigned to WS 1 at 25673485\n",
      "Shipment 189 assigned to WS 1 at 25673485\n",
      "Shipment 355 assigned to WS 2 at 25674059\n",
      "Shipment 356 assigned to WS 2 at 25674059\n",
      "Shipment 190 assigned to WS 1 at 25673485\n",
      "Shipment 192 assigned to WS 1 at 25675230\n",
      "Shipment 336 assigned to WS 1 at 25681200\n",
      "Shipment 196 assigned to WS 1 at 25676000\n",
      "Shipment 197 assigned to WS 1 at 25676000\n",
      "Shipment 198 assigned to WS 1 at 25676000\n",
      "Shipment 199 assigned to WS 1 at 25676000\n",
      "Shipment 200 assigned to WS 1 at 25676000\n",
      "Shipment 201 assigned to WS 1 at 25676000\n",
      "Shipment 204 assigned to WS 1 at 25674525\n",
      "Shipment 205 assigned to WS 1 at 25674525\n",
      "Shipment 327 assigned to WS 3 at 25681200\n",
      "Shipment 216 assigned to WS 3 at 25674059\n",
      "Shipment 389 assigned to WS 2 at 25673899\n",
      "Shipment 217 assigned to WS 2 at 25674059\n",
      "Shipment 390 assigned to WS 3 at 25673899\n",
      "Shipment 224 assigned to WS 3 at 25681200\n",
      "Shipment 391 assigned to WS 3 at 25673899\n",
      "Shipment 234 assigned to WS 1 at 25681253\n",
      "Shipment 235 assigned to WS 1 at 25681200\n",
      "Shipment 396 assigned to WS 1 at 25673899\n",
      "Shipment 397 assigned to WS 3 at 25673899\n",
      "Shipment 239 assigned to WS 2 at 25681200\n",
      "Shipment 240 assigned to WS 3 at 25681200\n",
      "Shipment 241 assigned to WS 2 at 25681200\n",
      "Shipment 474 assigned to WS 3 at 25673994\n",
      "Shipment 475 assigned to WS 2 at 25673994\n",
      "Shipment 476 assigned to WS 1 at 25673994\n",
      "Shipment 477 assigned to WS 3 at 25673994\n",
      "Shipment 242 assigned to WS 3 at 25672560\n",
      "Shipment 482 assigned to WS 1 at 25674150\n",
      "Shipment 244 assigned to WS 3 at 25672560\n",
      "Shipment 245 assigned to WS 1 at 25672560\n",
      "Shipment 246 assigned to WS 2 at 25672560\n",
      "Shipment 247 assigned to WS 3 at 25672560\n",
      "Shipment 431 assigned to WS 1 at 25674390\n",
      "Shipment 432 assigned to WS 1 at 25674390\n",
      "Shipment 248 assigned to WS 2 at 25672560\n",
      "Shipment 545 assigned to WS 3 at 25673518\n",
      "Shipment 249 assigned to WS 1 at 25672560\n",
      "Shipment 251 assigned to WS 1 at 25672560\n",
      "Shipment 253 assigned to WS 3 at 25672619\n",
      "Shipment 254 assigned to WS 3 at 25672560\n",
      "Shipment 483 assigned to WS 1 at 25674150\n",
      "Shipment 255 assigned to WS 2 at 25672560\n",
      "Shipment 560 assigned to WS 1 at 25674405\n",
      "Shipment 256 assigned to WS 1 at 25681253\n",
      "Shipment 587 assigned to WS 1 at 25674309\n",
      "Shipment 257 assigned to WS 3 at 25681200\n",
      "Shipment 258 assigned to WS 3 at 25681259\n",
      "Shipment 259 assigned to WS 3 at 25674059\n",
      "Shipment 260 assigned to WS 3 at 25672560\n",
      "Shipment 569 assigned to WS 1 at 25674040\n",
      "Shipment 261 assigned to WS 1 at 25674059\n",
      "Shipment 618 assigned to WS 2 at 25679859\n",
      "Shipment 265 assigned to WS 1 at 25672560\n",
      "Shipment 266 assigned to WS 1 at 25672619\n",
      "Shipment 267 assigned to WS 1 at 25674059\n",
      "Shipment 268 assigned to WS 3 at 25674059\n",
      "Shipment 269 assigned to WS 2 at 25681200\n",
      "Shipment 270 assigned to WS 1 at 25681253\n",
      "Shipment 271 assigned to WS 3 at 25681200\n",
      "Shipment 272 assigned to WS 3 at 25681200\n",
      "Shipment 273 assigned to WS 2 at 25681200\n",
      "Shipment 275 assigned to WS 1 at 25681259\n",
      "Shipment 276 assigned to WS 3 at 25672560\n",
      "Shipment 277 assigned to WS 1 at 25672560\n",
      "Shipment 571 assigned to WS 1 at 25674040\n",
      "Shipment 610 assigned to WS 2 at 25673220\n",
      "Shipment 637 assigned to WS 1 at 25673170\n",
      "Shipment 639 assigned to WS 1 at 25676470\n",
      "Shipment 641 assigned to WS 1 at 25674855\n",
      "Shipment 278 assigned to WS 2 at 25674059\n",
      "Shipment 279 assigned to WS 2 at 25674059\n",
      "Shipment 595 assigned to WS 3 at 25674855\n",
      "Shipment 596 assigned to WS 3 at 25674855\n",
      "Shipment 282 assigned to WS 1 at 25672560\n",
      "Shipment 283 assigned to WS 3 at 25674059\n",
      "Shipment 284 assigned to WS 1 at 25681200\n",
      "Shipment 597 assigned to WS 3 at 25674855\n",
      "Shipment 285 assigned to WS 1 at 25681200\n",
      "Shipment 598 assigned to WS 3 at 25674855\n",
      "Shipment 599 assigned to WS 3 at 25674855\n",
      "Shipment 600 assigned to WS 3 at 25674855\n",
      "Shipment 288 assigned to WS 3 at 25672616\n",
      "Shipment 289 assigned to WS 1 at 25672560\n",
      "Shipment 290 assigned to WS 3 at 25674059\n",
      "Shipment 655 assigned to WS 1 at 25674913\n",
      "Shipment 656 assigned to WS 2 at 25674855\n",
      "Shipment 291 assigned to WS 3 at 25672560\n",
      "Shipment 292 assigned to WS 1 at 25672560\n",
      "Shipment 293 assigned to WS 2 at 25672560\n",
      "Shipment 665 assigned to WS 1 at 25676150\n",
      "Shipment 296 assigned to WS 2 at 25681200\n",
      "Shipment 385 assigned to WS 1 at 25673870\n",
      "Shipment 386 assigned to WS 1 at 25673870\n",
      "Shipment 387 assigned to WS 1 at 25673870\n",
      "Shipment 298 assigned to WS 3 at 25674059\n",
      "Shipment 299 assigned to WS 1 at 25674059\n",
      "Shipment 300 assigned to WS 3 at 25674059\n",
      "Shipment 301 assigned to WS 1 at 25674059\n",
      "Shipment 302 assigned to WS 2 at 25674059\n",
      "Shipment 572 assigned to WS 1 at 25674040\n",
      "Shipment 573 assigned to WS 1 at 25674040\n",
      "Shipment 574 assigned to WS 1 at 25674040\n",
      "Shipment 575 assigned to WS 1 at 25674040\n",
      "Shipment 303 assigned to WS 3 at 25672560\n",
      "Shipment 458 assigned to WS 1 at 25674090\n",
      "Shipment 459 assigned to WS 1 at 25674090\n",
      "Shipment 309 assigned to WS 2 at 25674059\n",
      "Shipment 310 assigned to WS 1 at 25674059\n",
      "Shipment 411 assigned to WS 1 at 25673899\n",
      "Shipment 311 assigned to WS 2 at 25681200\n",
      "Shipment 649 assigned to WS 1 at 25672635\n",
      "Shipment 650 assigned to WS 1 at 25672635\n",
      "Shipment 312 assigned to WS 3 at 25674059\n",
      "Shipment 313 assigned to WS 1 at 25681200\n",
      "Shipment 542 assigned to WS 1 at 25675805\n",
      "Shipment 543 assigned to WS 1 at 25675805\n",
      "Shipment 314 assigned to WS 1 at 25672560\n",
      "Shipment 566 assigned to WS 2 at 25674970\n",
      "Shipment 315 assigned to WS 2 at 25674059\n",
      "Shipment 316 assigned to WS 3 at 25674059\n",
      "Shipment 317 assigned to WS 1 at 25674059\n",
      "Shipment 318 assigned to WS 3 at 25674059\n",
      "Shipment 319 assigned to WS 1 at 25674059\n",
      "Shipment 424 assigned to WS 1 at 25675844\n",
      "Shipment 425 assigned to WS 2 at 25675844\n",
      "Shipment 522 assigned to WS 2 at 25675154\n",
      "Shipment 320 assigned to WS 1 at 25681200\n",
      "Shipment 321 assigned to WS 1 at 25681259\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shipment 322 assigned to WS 2 at 25674059\n",
      "Shipment 323 assigned to WS 2 at 25674059\n",
      "Shipment 484 assigned to WS 1 at 25674150\n",
      "Shipment 485 assigned to WS 1 at 25674150\n",
      "Shipment 324 assigned to WS 1 at 25674059\n",
      "Shipment 487 assigned to WS 1 at 25673415\n",
      "Shipment 325 assigned to WS 1 at 25681200\n",
      "Shipment 326 assigned to WS 2 at 25681200\n",
      "Shipment 328 assigned to WS 1 at 25672560\n",
      "Shipment 414 assigned to WS 2 at 25673899\n",
      "Shipment 329 assigned to WS 2 at 25674059\n",
      "Shipment 449 assigned to WS 1 at 25674955\n",
      "Shipment 450 assigned to WS 1 at 25674955\n",
      "Shipment 332 assigned to WS 1 at 25674059\n",
      "Shipment 333 assigned to WS 2 at 25674059\n",
      "Shipment 335 assigned to WS 3 at 25672616\n",
      "Shipment 342 assigned to WS 1 at 25681253\n",
      "Shipment 343 assigned to WS 3 at 25681200\n",
      "Shipment 344 assigned to WS 3 at 25681200\n",
      "Shipment 345 assigned to WS 1 at 25681259\n",
      "Shipment 346 assigned to WS 2 at 25674059\n",
      "Shipment 348 assigned to WS 3 at 25672560\n",
      "Shipment 663 assigned to WS 2 at 25674855\n",
      "Shipment 349 assigned to WS 1 at 25672560\n",
      "Shipment 350 assigned to WS 2 at 25681200\n",
      "Shipment 351 assigned to WS 3 at 25674059\n",
      "Shipment 460 assigned to WS 1 at 25674090\n",
      "Shipment 461 assigned to WS 1 at 25674090\n",
      "Shipment 462 assigned to WS 1 at 25674090\n",
      "Shipment 463 assigned to WS 1 at 25674090\n",
      "Shipment 352 assigned to WS 3 at 25672560\n",
      "Shipment 354 assigned to WS 3 at 25674059\n",
      "Shipment 577 assigned to WS 1 at 25673525\n",
      "Shipment 358 assigned to WS 1 at 25674059\n",
      "Shipment 359 assigned to WS 3 at 25672560\n",
      "Shipment 360 assigned to WS 1 at 25681200\n",
      "Shipment 361 assigned to WS 2 at 25674059\n",
      "Shipment 644 assigned to WS 1 at 25674610\n",
      "Shipment 362 assigned to WS 3 at 25672560\n",
      "Shipment 363 assigned to WS 1 at 25672619\n",
      "Shipment 364 assigned to WS 1 at 25681200\n",
      "Shipment 365 assigned to WS 2 at 25681200\n",
      "Shipment 366 assigned to WS 3 at 25681200\n",
      "Shipment 497 assigned to WS 3 at 25674919\n",
      "Shipment 604 assigned to WS 3 at 25674855\n",
      "Shipment 369 assigned to WS 1 at 25674059\n",
      "Shipment 552 assigned to WS 3 at 25673518\n",
      "Shipment 374 assigned to WS 1 at 25681200\n",
      "Shipment 567 assigned to WS 2 at 25674970\n",
      "Shipment 377 assigned to WS 1 at 25672560\n",
      "Shipment 613 assigned to WS 2 at 25673220\n",
      "Shipment 378 assigned to WS 2 at 25681259\n",
      "Shipment 379 assigned to WS 2 at 25681200\n",
      "Shipment 380 assigned to WS 3 at 25681200\n",
      "Shipment 544 assigned to WS 1 at 25675805\n",
      "Shipment 382 assigned to WS 1 at 25673870\n",
      "Shipment 388 assigned to WS 2 at 25673899\n",
      "Shipment 392 assigned to WS 1 at 25673899\n",
      "Shipment 393 assigned to WS 1 at 25673899\n",
      "Shipment 394 assigned to WS 3 at 25673899\n",
      "Shipment 395 assigned to WS 3 at 25673899\n",
      "Shipment 398 assigned to WS 3 at 25673899\n",
      "Shipment 399 assigned to WS 3 at 25673899\n",
      "Shipment 400 assigned to WS 2 at 25673899\n",
      "Shipment 401 assigned to WS 3 at 25673899\n",
      "Shipment 402 assigned to WS 1 at 25673899\n",
      "Shipment 493 assigned to WS 3 at 25674919\n",
      "Shipment 619 assigned to WS 3 at 25679859\n",
      "Shipment 403 assigned to WS 2 at 25673899\n",
      "Shipment 404 assigned to WS 2 at 25673899\n",
      "Shipment 405 assigned to WS 2 at 25673899\n",
      "Shipment 406 assigned to WS 1 at 25673899\n",
      "Shipment 478 assigned to WS 2 at 25673994\n",
      "Shipment 518 assigned to WS 3 at 25675154\n",
      "Shipment 407 assigned to WS 3 at 25673899\n",
      "Shipment 520 assigned to WS 1 at 25675154\n",
      "Shipment 521 assigned to WS 3 at 25675154\n",
      "Shipment 554 assigned to WS 1 at 25674195\n",
      "Shipment 601 assigned to WS 3 at 25674855\n",
      "Shipment 660 assigned to WS 2 at 25674855\n",
      "Shipment 408 assigned to WS 1 at 25673899\n",
      "Shipment 409 assigned to WS 3 at 25673899\n",
      "Shipment 410 assigned to WS 1 at 25673899\n",
      "Shipment 412 assigned to WS 1 at 25673899\n",
      "Shipment 413 assigned to WS 3 at 25673899\n",
      "Shipment 415 assigned to WS 1 at 25673899\n",
      "Shipment 480 assigned to WS 2 at 25673994\n",
      "Shipment 416 assigned to WS 2 at 25673899\n",
      "Shipment 417 assigned to WS 2 at 25673899\n",
      "Shipment 418 assigned to WS 3 at 25673899\n",
      "Shipment 654 assigned to WS 1 at 25675695\n",
      "Shipment 422 assigned to WS 2 at 25675844\n",
      "Shipment 423 assigned to WS 1 at 25675844\n",
      "Shipment 426 assigned to WS 1 at 25675844\n",
      "Shipment 433 assigned to WS 1 at 25674390\n",
      "Shipment 434 assigned to WS 1 at 25674390\n",
      "Shipment 435 assigned to WS 1 at 25674390\n",
      "Shipment 436 assigned to WS 1 at 25674390\n",
      "Shipment 437 assigned to WS 1 at 25674390\n",
      "Shipment 439 assigned to WS 2 at 25674060\n",
      "Shipment 441 assigned to WS 1 at 25674370\n",
      "Shipment 442 assigned to WS 1 at 25674370\n",
      "Shipment 443 assigned to WS 1 at 25674370\n",
      "Shipment 445 assigned to WS 1 at 25673385\n",
      "Shipment 456 assigned to WS 1 at 25674090\n",
      "Shipment 457 assigned to WS 1 at 25674090\n",
      "Shipment 464 assigned to WS 1 at 25674090\n",
      "Shipment 465 assigned to WS 1 at 25674090\n",
      "Shipment 467 assigned to WS 1 at 25672730\n",
      "Shipment 469 assigned to WS 1 at 25673510\n",
      "Shipment 471 assigned to WS 2 at 25673510\n",
      "Shipment 473 assigned to WS 2 at 25674040\n",
      "Shipment 479 assigned to WS 3 at 25673994\n",
      "Shipment 589 assigned to WS 3 at 25674309\n",
      "Shipment 481 assigned to WS 3 at 25673994\n",
      "Shipment 488 assigned to WS 2 at 25674919\n",
      "Shipment 489 assigned to WS 3 at 25674919\n",
      "Shipment 490 assigned to WS 2 at 25674919\n",
      "Shipment 491 assigned to WS 2 at 25674919\n",
      "Shipment 492 assigned to WS 3 at 25674919\n",
      "Shipment 494 assigned to WS 3 at 25674919\n",
      "Shipment 495 assigned to WS 3 at 25674919\n",
      "Shipment 496 assigned to WS 2 at 25674919\n",
      "Shipment 611 assigned to WS 2 at 25673220\n",
      "Shipment 498 assigned to WS 1 at 25673415\n",
      "Shipment 500 assigned to WS 1 at 25673630\n",
      "Shipment 501 assigned to WS 1 at 25673630\n",
      "Shipment 499 assigned to WS 1 at 25673415\n",
      "Shipment 502 assigned to WS 1 at 25673630\n",
      "Shipment 503 assigned to WS 1 at 25673630\n",
      "Shipment 504 assigned to WS 2 at 25675154\n",
      "Shipment 505 assigned to WS 2 at 25675154\n",
      "Shipment 533 assigned to WS 1 at 25674495\n",
      "Shipment 536 assigned to WS 1 at 25674060\n",
      "Shipment 506 assigned to WS 1 at 25675154\n",
      "Shipment 507 assigned to WS 3 at 25675154\n",
      "Shipment 508 assigned to WS 3 at 25675154\n",
      "Shipment 539 assigned to WS 1 at 25675805\n",
      "Shipment 510 assigned to WS 1 at 25675154\n",
      "Shipment 511 assigned to WS 3 at 25675154\n",
      "Shipment 512 assigned to WS 2 at 25675154\n",
      "Shipment 513 assigned to WS 3 at 25675154\n",
      "Shipment 553 assigned to WS 1 at 25674195\n",
      "Shipment 514 assigned to WS 1 at 25675154\n",
      "Shipment 516 assigned to WS 3 at 25675154\n",
      "Shipment 517 assigned to WS 3 at 25675154\n",
      "Shipment 519 assigned to WS 1 at 25675154\n",
      "Shipment 523 assigned to WS 3 at 25675154\n",
      "Shipment 524 assigned to WS 1 at 25675154\n",
      "Shipment 525 assigned to WS 2 at 25675154\n",
      "Shipment 664 assigned to WS 2 at 25674855\n",
      "Shipment 526 assigned to WS 3 at 25675154\n",
      "Shipment 527 assigned to WS 2 at 25675154\n",
      "Shipment 528 assigned to WS 3 at 25675154\n",
      "Shipment 529 assigned to WS 3 at 25675154\n",
      "Shipment 530 assigned to WS 2 at 25675154\n",
      "Shipment 531 assigned to WS 1 at 25675154\n",
      "Shipment 555 assigned to WS 1 at 25674195\n",
      "Shipment 532 assigned to WS 3 at 25675154\n",
      "Shipment 605 assigned to WS 3 at 25674855\n",
      "Shipment 534 assigned to WS 1 at 25674495\n",
      "Shipment 535 assigned to WS 1 at 25674495\n",
      "Shipment 537 assigned to WS 3 at 25674060\n",
      "Shipment 538 assigned to WS 1 at 25674118\n",
      "Shipment 546 assigned to WS 3 at 25673518\n",
      "Shipment 557 assigned to WS 1 at 25673605\n",
      "Shipment 578 assigned to WS 1 at 25673350\n",
      "Shipment 582 assigned to WS 1 at 25673930\n",
      "Shipment 548 assigned to WS 3 at 25673518\n",
      "Shipment 549 assigned to WS 3 at 25673518\n",
      "Shipment 550 assigned to WS 3 at 25673518\n",
      "Shipment 551 assigned to WS 3 at 25673518\n",
      "Shipment 558 assigned to WS 1 at 25673605\n",
      "Shipment 559 assigned to WS 1 at 25673605\n",
      "Shipment 561 assigned to WS 1 at 25674405\n",
      "Shipment 667 assigned to WS 1 at 25672085\n",
      "Shipment 562 assigned to WS 1 at 25674405\n",
      "Shipment 563 assigned to WS 1 at 25674405\n",
      "Shipment 564 assigned to WS 1 at 25674405\n",
      "Shipment 565 assigned to WS 2 at 25674970\n",
      "Shipment 579 assigned to WS 1 at 25673350\n",
      "Shipment 580 assigned to WS 1 at 25673350\n",
      "Shipment 581 assigned to WS 1 at 25673350\n",
      "Shipment 583 assigned to WS 1 at 25673930\n",
      "Shipment 584 assigned to WS 1 at 25676340\n",
      "Shipment 585 assigned to WS 1 at 25676340\n",
      "Shipment 586 assigned to WS 1 at 25676340\n",
      "Shipment 588 assigned to WS 1 at 25674309\n",
      "Shipment 590 assigned to WS 2 at 25674309\n",
      "Shipment 607 assigned to WS 3 at 25674855\n",
      "Shipment 591 assigned to WS 1 at 25674970\n",
      "Shipment 592 assigned to WS 1 at 25674970\n",
      "Shipment 593 assigned to WS 1 at 25674855\n",
      "Shipment 594 assigned to WS 1 at 25674855\n",
      "Shipment 602 assigned to WS 3 at 25674855\n",
      "Shipment 603 assigned to WS 1 at 25674855\n",
      "Shipment 606 assigned to WS 3 at 25674855\n",
      "Shipment 608 assigned to WS 2 at 25673220\n",
      "Shipment 609 assigned to WS 2 at 25673220\n",
      "Shipment 612 assigned to WS 2 at 25673220\n",
      "Shipment 614 assigned to WS 1 at 25679859\n",
      "Shipment 615 assigned to WS 1 at 25679859\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shipment 616 assigned to WS 2 at 25679859\n",
      "Shipment 617 assigned to WS 2 at 25679859\n",
      "Shipment 620 assigned to WS 3 at 25679859\n",
      "Shipment 621 assigned to WS 2 at 25679859\n",
      "Shipment 622 assigned to WS 3 at 25679859\n",
      "Shipment 623 assigned to WS 1 at 25679859\n",
      "Shipment 624 assigned to WS 3 at 25679859\n",
      "Shipment 626 assigned to WS 1 at 25672625\n",
      "Shipment 627 assigned to WS 2 at 25673270\n",
      "Shipment 628 assigned to WS 2 at 25673270\n",
      "Shipment 629 assigned to WS 2 at 25673270\n",
      "Shipment 630 assigned to WS 1 at 25673270\n",
      "Shipment 633 assigned to WS 2 at 25673670\n",
      "Shipment 634 assigned to WS 2 at 25673670\n",
      "Shipment 635 assigned to WS 1 at 25674400\n",
      "Shipment 636 assigned to WS 1 at 25674400\n",
      "Shipment 638 assigned to WS 1 at 25673170\n",
      "Shipment 640 assigned to WS 1 at 25676470\n",
      "Shipment 642 assigned to WS 1 at 25674855\n",
      "Shipment 646 assigned to WS 1 at 25680890\n",
      "Shipment 651 assigned to WS 1 at 25675695\n",
      "Shipment 652 assigned to WS 1 at 25675695\n",
      "Shipment 653 assigned to WS 1 at 25675695\n",
      "Shipment 668 assigned to WS 1 at 25672085\n",
      "Shipment 657 assigned to WS 2 at 25674855\n",
      "Shipment 658 assigned to WS 2 at 25674855\n",
      "Shipment 659 assigned to WS 2 at 25674855\n",
      "Shipment 661 assigned to WS 2 at 25674913\n",
      "Shipment 662 assigned to WS 2 at 25674855\n",
      "Shipment 666 assigned to WS 1 at 25676150\n"
     ]
    }
   ],
   "source": [
    "for i in ArrivalULDNumber_list:\n",
    "    shipment_list = list(sample_shipments_bu[sample_shipments_bu.ArrivalULDNumber==i]['shipment_ID'])\n",
    "    #print(shipment_list)\n",
    "    for l in shipment_list:\n",
    "        #print(int(i),str(j))\n",
    "        number_of_ws = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "        for m in range(1,number_of_ws+1):\n",
    "            for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop):\n",
    "                if z[l,m,t].x > 0.5:\n",
    "                    print(\"Shipment\", l, \"assigned to WS\", m, \"at\", t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "for i in ArrivalULDNumber_list:\n",
    "    shipment_list = list(sample_shipments_bu[sample_shipments_bu.ArrivalULDNumber==i]['shipment_ID'])\n",
    "    #print(shipment_list)\n",
    "    for l in shipment_list:\n",
    "        #print(int(i),str(j))\n",
    "        number_of_ws = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "        bu_zone_name = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Name_x'].unique()[0]\n",
    "        flight_number = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['FlightNumber'].unique()[0]\n",
    "        for m in range(1,number_of_ws+1):\n",
    "            for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop):\n",
    "                if z[l,m,t].x > 0.5:\n",
    "                    k = str(bu_zone_name +\"-WS-\" + str(m))\n",
    "                    #print(\"Shipment\", l, \"assigned to\", bu_zone_name,\"WS\",m, \"at\", t)\n",
    "                    plt.plot(t, k, \"o\")\n",
    "                    #plt.annotate(l, (t,k))\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################################################################################################################################\n",
    "#################################################################################################################################\n",
    "#################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_x = []\n",
    "for i in uld_uniqueid_list:\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    #print(i, \"in\", BreakDownZoneName_list1)\n",
    "    for j in BreakDownZoneName_list1:\n",
    "        #print(int(i),str(j))\n",
    "        new_check = df_merge1[(df_merge1.unique_id==i) & (df_merge1.BreakDownZoneName==j)]\n",
    "        shipment_arrival_time_minutes_loop = list(new_check['Shipment_arrival_time_minutes'].unique())[0]\n",
    "        shipment_arrival_duation_minutes_loop = list(new_check['one_hour_duration'].unique())[0] #list(new_check['Shipment_duration_minutes'].unique())[0]\n",
    "        for k in range((min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))), (min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())) + (math.ceil((shipment_arrival_time_minutes_loop-min(list(df_merge1[df_merge1.BreakDownZoneName==j]['Shipment_arrival_time_minutes'].unique())))/(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))*(df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']))) +shipment_arrival_duation_minutes_loop, df_merge1[df_merge1.BreakDownZoneName==j].iloc[0]['handling_m']):\n",
    "            if x[i,j,k].x > 0.5:\n",
    "                df_final_x.append((i, j, k))\n",
    "\n",
    "\n",
    "df_final_x1 = pd.DataFrame(df_final_x, columns=('unique_id', 'bd_name', 'assigned_time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>unique_id</th>\n",
       "      <th>bd_name</th>\n",
       "      <th>assigned_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21231_25671250</td>\n",
       "      <td>BD NRML-4</td>\n",
       "      <td>25671312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21231_25671250</td>\n",
       "      <td>BD NML-3</td>\n",
       "      <td>25671280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24321_25671250</td>\n",
       "      <td>BD NRML-1</td>\n",
       "      <td>25671270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42309_25671550</td>\n",
       "      <td>BD NRML-1</td>\n",
       "      <td>25671570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49809_25673105</td>\n",
       "      <td>BD NRML-4</td>\n",
       "      <td>25673119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        unique_id    bd_name  assigned_time\n",
       "0  21231_25671250  BD NRML-4       25671312\n",
       "1  21231_25671250   BD NML-3       25671280\n",
       "2  24321_25671250  BD NRML-1       25671270\n",
       "3  42309_25671550  BD NRML-1       25671570\n",
       "4  49809_25673105  BD NRML-4       25673119"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_x1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_x1.to_csv(r'bd_assigned_time_output.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_z = []\n",
    "for i in ArrivalULDNumber_list:\n",
    "    shipment_list = list(sample_shipments_bu[sample_shipments_bu.ArrivalULDNumber==i]['shipment_ID'])\n",
    "    #print(shipment_list)\n",
    "    for l in shipment_list:\n",
    "        #print(int(i),str(j))\n",
    "        number_of_ws = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "        shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "        bu_zone_name = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['Name_x'].unique()[0]\n",
    "        flight_number = sample_shipments_bu[(sample_shipments_bu.ArrivalULDNumber==i) & (sample_shipments_bu.shipment_ID==l)]['FlightNumber'].unique()[0]\n",
    "        for m in range(1,number_of_ws+1):\n",
    "            for t in range(shipment_departure_time_minutes_loop-60, shipment_departure_time_minutes_loop):\n",
    "                if z[l,m,t].x > 0.5:\n",
    "                    #k = str(bu_zone_name +\"-WS-\" + str(m))\n",
    "                    df_final_z.append((l,bu_zone_name ,m, t))\n",
    "\n",
    "df_final_z = pd.DataFrame(df_final_z, columns=('shipment_id', 'bu_name', 'ws_number', 'buildup_time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>shipment_id</th>\n",
       "      <th>bu_name</th>\n",
       "      <th>ws_number</th>\n",
       "      <th>buildup_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BU FT-1</td>\n",
       "      <td>1</td>\n",
       "      <td>25674015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>BU FT-1</td>\n",
       "      <td>1</td>\n",
       "      <td>25677405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>1</td>\n",
       "      <td>25673540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>1</td>\n",
       "      <td>25677620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>1</td>\n",
       "      <td>25673640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   shipment_id    bu_name  ws_number  buildup_time\n",
       "0            1    BU FT-1          1      25674015\n",
       "1            7    BU FT-1          1      25677405\n",
       "2           21    BU FT-2          1      25673540\n",
       "3           30    BU FT-2          1      25677620\n",
       "4           36  BU NRML-1          1      25673640"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_x1.to_csv(r'bu_workstation_time_output.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "25674075\n",
      "6\n",
      "25673700\n",
      "7\n",
      "25673700\n",
      "3\n",
      "25673600\n",
      "4\n",
      "25677680\n",
      "8\n",
      "25675605\n",
      "9\n",
      "25675605\n",
      "10\n",
      "25675605\n",
      "2\n",
      "25677465\n",
      "5\n",
      "25673700\n",
      "11\n",
      "25674570\n"
     ]
    }
   ],
   "source": [
    "for i in uld_uniqueid_list:\n",
    "    BreakDownZoneName_list1 = list(df_merge1[df_merge1.unique_id==i]['BreakDownZoneName'])\n",
    "    shipment_date_list = list(sample_shipments_bu[sample_shipments_bu.unique_id==i]['departure_date'].unique())\n",
    "    for d in shipment_date_list:\n",
    "        shipment_list = list(sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.departure_date==d)]['shipment_ID'])\n",
    "        #print(shipment_list)\n",
    "        for l in shipment_list:\n",
    "            number_of_ws = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Count'].unique()[0]\n",
    "            shipment_departure_time_minutes_loop = sample_shipments_bu[(sample_shipments_bu.unique_id==i) & (sample_shipments_bu.shipment_ID==l)]['Shipment_departure_time_minutes'].unique()[0]\n",
    "            print(l)\n",
    "            print(shipment_departure_time_minutes_loop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>ProductName</th>\n",
       "      <th>Priority</th>\n",
       "      <th>DropZone</th>\n",
       "      <th>ArrivalULDNumber</th>\n",
       "      <th>Pieces</th>\n",
       "      <th>Weight</th>\n",
       "      <th>TotalWeight</th>\n",
       "      <th>FlightNumberDeparture</th>\n",
       "      <th>Shipment_arrival_date_time</th>\n",
       "      <th>Shipment_departure_date_time</th>\n",
       "      <th>Shipment_duration</th>\n",
       "      <th>build_up_arrival_time</th>\n",
       "      <th>build_up_finish_time</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>BU Zone</th>\n",
       "      <th>TransportationDistance</th>\n",
       "      <th>Name_x</th>\n",
       "      <th>HandlingTimePerULD</th>\n",
       "      <th>TransportationTimeToWH</th>\n",
       "      <th>Name_y</th>\n",
       "      <th>Count</th>\n",
       "      <th>handling_ULD_h</th>\n",
       "      <th>handling_ULD_m</th>\n",
       "      <th>handling_ULD_s</th>\n",
       "      <th>transport_WH_h</th>\n",
       "      <th>transport_WH_m</th>\n",
       "      <th>transport_WH_s</th>\n",
       "      <th>ULD_handling_h</th>\n",
       "      <th>ULD_handling_m</th>\n",
       "      <th>ULD_handling_s</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>Shipment_duration_minutes</th>\n",
       "      <th>Shipment_arrival_time_minutes</th>\n",
       "      <th>Shipment_departure_time_minutes</th>\n",
       "      <th>shipment_ID</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NRML</td>\n",
       "      <td>False</td>\n",
       "      <td>DZ NRML-2</td>\n",
       "      <td>21231</td>\n",
       "      <td>1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-10-23 06:10:00</td>\n",
       "      <td>2018-10-24 23:00:00</td>\n",
       "      <td>1 days 16:50:00</td>\n",
       "      <td>2018-10-23 06:10:00</td>\n",
       "      <td>2018-10-24 23:00:00</td>\n",
       "      <td>46</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-23</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>2450</td>\n",
       "      <td>25671250</td>\n",
       "      <td>25673700</td>\n",
       "      <td>5</td>\n",
       "      <td>49815_25674380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NRML</td>\n",
       "      <td>False</td>\n",
       "      <td>DZ NRML-2</td>\n",
       "      <td>21231</td>\n",
       "      <td>3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-10-23 06:10:00</td>\n",
       "      <td>2018-10-24 23:00:00</td>\n",
       "      <td>1 days 16:50:00</td>\n",
       "      <td>2018-10-23 06:10:00</td>\n",
       "      <td>2018-10-24 23:00:00</td>\n",
       "      <td>46</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-23</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>2450</td>\n",
       "      <td>25671250</td>\n",
       "      <td>25673700</td>\n",
       "      <td>6</td>\n",
       "      <td>21231_25671250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NRML</td>\n",
       "      <td>False</td>\n",
       "      <td>DZ NRML-2</td>\n",
       "      <td>21231</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.3</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-10-23 06:10:00</td>\n",
       "      <td>2018-10-24 23:00:00</td>\n",
       "      <td>1 days 16:50:00</td>\n",
       "      <td>2018-10-23 06:10:00</td>\n",
       "      <td>2018-10-24 23:00:00</td>\n",
       "      <td>46</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>BU NRML-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-23</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>2450</td>\n",
       "      <td>25671250</td>\n",
       "      <td>25673700</td>\n",
       "      <td>7</td>\n",
       "      <td>51228_25671360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ProductName  Priority   DropZone  ArrivalULDNumber  Pieces  Weight  TotalWeight FlightNumberDeparture Shipment_arrival_date_time Shipment_departure_date_time Shipment_duration build_up_arrival_time build_up_finish_time  FlightNumber    BU Zone TransportationDistance     Name_x HandlingTimePerULD TransportationTimeToWH     Name_y  Count  handling_ULD_h  handling_ULD_m  handling_ULD_s  transport_WH_h  transport_WH_m  transport_WH_s  ULD_handling_h  ULD_handling_m  ULD_handling_s arrival_date departure_date  Shipment_duration_minutes  Shipment_arrival_time_minutes  Shipment_departure_time_minutes  shipment_ID       unique_id\n",
       "35        NRML     False  DZ NRML-2             21231       1    22.6         22.6                    46        2018-10-23 06:10:00          2018-10-24 23:00:00   1 days 16:50:00   2018-10-23 06:10:00  2018-10-24 23:00:00            46  BU NRML-1               00:20:00  BU NRML-1           00:20:00               00:50:00  BU NRML-1      3               0              20               0               0              50               0               0              20               0   2018-10-23     2018-10-24                       2450                       25671250                         25673700            5  49815_25674380\n",
       "36        NRML     False  DZ NRML-2             21231       3    15.5         15.5                    46        2018-10-23 06:10:00          2018-10-24 23:00:00   1 days 16:50:00   2018-10-23 06:10:00  2018-10-24 23:00:00            46  BU NRML-1               00:20:00  BU NRML-1           00:20:00               00:50:00  BU NRML-1      3               0              20               0               0              50               0               0              20               0   2018-10-23     2018-10-24                       2450                       25671250                         25673700            6  21231_25671250\n",
       "37        NRML     False  DZ NRML-2             21231       1    22.3         22.3                    46        2018-10-23 06:10:00          2018-10-24 23:00:00   1 days 16:50:00   2018-10-23 06:10:00  2018-10-24 23:00:00            46  BU NRML-1               00:20:00  BU NRML-1           00:20:00               00:50:00  BU NRML-1      3               0              20               0               0              50               0               0              20               0   2018-10-23     2018-10-24                       2450                       25671250                         25673700            7  51228_25671360"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_shipments_bu[sample_shipments_bu.Shipment_departure_time_minutes==25673700]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Priority</th>\n",
       "      <th>DropZone</th>\n",
       "      <th>ArrivalULDNumber</th>\n",
       "      <th>Pieces</th>\n",
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       "      <th>Shipment_arrival_date_time</th>\n",
       "      <th>Shipment_departure_date_time</th>\n",
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       "      <th>build_up_arrival_time</th>\n",
       "      <th>build_up_finish_time</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>BU Zone</th>\n",
       "      <th>TransportationDistance</th>\n",
       "      <th>Name_x</th>\n",
       "      <th>HandlingTimePerULD</th>\n",
       "      <th>TransportationTimeToWH</th>\n",
       "      <th>Name_y</th>\n",
       "      <th>Count</th>\n",
       "      <th>handling_ULD_h</th>\n",
       "      <th>handling_ULD_m</th>\n",
       "      <th>handling_ULD_s</th>\n",
       "      <th>transport_WH_h</th>\n",
       "      <th>transport_WH_m</th>\n",
       "      <th>transport_WH_s</th>\n",
       "      <th>ULD_handling_h</th>\n",
       "      <th>ULD_handling_m</th>\n",
       "      <th>ULD_handling_s</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>Shipment_duration_minutes</th>\n",
       "      <th>Shipment_arrival_time_minutes</th>\n",
       "      <th>Shipment_departure_time_minutes</th>\n",
       "      <th>shipment_ID</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>NML</td>\n",
       "      <td>False</td>\n",
       "      <td>DZ NML-1</td>\n",
       "      <td>49815</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-10-25 10:20:00</td>\n",
       "      <td>2018-10-25 20:55:00</td>\n",
       "      <td>10:35:00</td>\n",
       "      <td>2018-10-25 10:20:00</td>\n",
       "      <td>2018-10-25 20:55:00</td>\n",
       "      <td>11</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>00:24:00</td>\n",
       "      <td>00:45:00</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>635</td>\n",
       "      <td>25674380</td>\n",
       "      <td>25675015</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>NRML</td>\n",
       "      <td>False</td>\n",
       "      <td>DZ NRML-1</td>\n",
       "      <td>49815</td>\n",
       "      <td>4</td>\n",
       "      <td>40.1</td>\n",
       "      <td>40.1</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-10-25 10:20:00</td>\n",
       "      <td>2018-10-25 20:55:00</td>\n",
       "      <td>10:35:00</td>\n",
       "      <td>2018-10-25 10:20:00</td>\n",
       "      <td>2018-10-25 20:55:00</td>\n",
       "      <td>11</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>00:24:00</td>\n",
       "      <td>00:45:00</td>\n",
       "      <td>BU FT-2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>635</td>\n",
       "      <td>25674380</td>\n",
       "      <td>25675015</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ProductName  Priority   DropZone  ArrivalULDNumber  Pieces  Weight  TotalWeight FlightNumberDeparture Shipment_arrival_date_time Shipment_departure_date_time Shipment_duration build_up_arrival_time build_up_finish_time  FlightNumber  BU Zone TransportationDistance   Name_x HandlingTimePerULD TransportationTimeToWH   Name_y  Count  handling_ULD_h  handling_ULD_m  handling_ULD_s  transport_WH_h  transport_WH_m  transport_WH_s  ULD_handling_h  ULD_handling_m  ULD_handling_s arrival_date departure_date  Shipment_duration_minutes  Shipment_arrival_time_minutes  Shipment_departure_time_minutes  shipment_ID unique_id\n",
       "448         NML     False   DZ NML-1             49815       1     1.5          1.5                    11        2018-10-25 10:20:00          2018-10-25 20:55:00          10:35:00   2018-10-25 10:20:00  2018-10-25 20:55:00            11  BU FT-2               00:20:00  BU FT-2           00:24:00               00:45:00  BU FT-2      6               0              24               0               0              45               0               0              24               0   2018-10-25     2018-10-25                        635                       25674380                         25675015           40       NaN\n",
       "449        NRML     False  DZ NRML-1             49815       4    40.1         40.1                    11        2018-10-25 10:20:00          2018-10-25 20:55:00          10:35:00   2018-10-25 10:20:00  2018-10-25 20:55:00            11  BU FT-2               00:20:00  BU FT-2           00:24:00               00:45:00  BU FT-2      6               0              24               0               0              45               0               0              24               0   2018-10-25     2018-10-25                        635                       25674380                         25675015           41       NaN"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_shipments_bu[(sample_shipments_bu.Shipment_arrival_time_minutes < 25674419) & (sample_shipments_bu.Shipment_arrival_time_minutes > 25674300)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProductName                        32\n",
       "Priority                           32\n",
       "DropZone                           32\n",
       "ArrivalULDNumber                   32\n",
       "Pieces                             32\n",
       "Weight                             32\n",
       "TotalWeight                        32\n",
       "FlightNumberDeparture              32\n",
       "Shipment_arrival_date_time         32\n",
       "Shipment_departure_date_time       32\n",
       "Shipment_duration                  32\n",
       "build_up_arrival_time              32\n",
       "build_up_finish_time               32\n",
       "FlightNumber                       32\n",
       "BU Zone                            32\n",
       "TransportationDistance             32\n",
       "Name_x                             32\n",
       "HandlingTimePerULD                 32\n",
       "TransportationTimeToWH             32\n",
       "Name_y                             32\n",
       "Count                              32\n",
       "handling_ULD_h                     32\n",
       "handling_ULD_m                     32\n",
       "handling_ULD_s                     32\n",
       "transport_WH_h                     32\n",
       "transport_WH_m                     32\n",
       "transport_WH_s                     32\n",
       "ULD_handling_h                     32\n",
       "ULD_handling_m                     32\n",
       "ULD_handling_s                     32\n",
       "arrival_date                       32\n",
       "departure_date                     32\n",
       "Shipment_duration_minutes          32\n",
       "Shipment_arrival_time_minutes      32\n",
       "Shipment_departure_time_minutes    32\n",
       "shipment_ID                        32\n",
       "unique_id                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_shipments_bu[sample_shipments_bu.unique_id.isna()].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C15 1\n",
      "C23 1\n",
      "C39 1\n",
      "C60 1\n",
      "C72 1\n",
      "C102 1\n",
      "C112 1\n",
      "C132 1\n",
      "C161 1\n",
      "C170 1\n",
      "C195 1\n",
      "C205 1\n",
      "C230 1\n",
      "C232 1\n",
      "C532 1\n",
      "C712 1\n",
      "C892 1\n",
      "C1072 1\n",
      "C1252 1\n",
      "C1432 1\n",
      "C1612 1\n",
      "C1972 1\n",
      "C2272 1\n",
      "C2452 1\n",
      "C2632 1\n",
      "C2932 1\n",
      "C3172 1\n",
      "C3352 1\n",
      "C3532 1\n",
      "C3712 1\n",
      "C3892 1\n",
      "C4072 1\n",
      "C4252 1\n",
      "C4432 1\n",
      "C4612 1\n",
      "C4912 1\n",
      "C4972 1\n",
      "C5212 1\n",
      "C5572 1\n",
      "C5872 1\n",
      "C6112 1\n",
      "C6412 1\n",
      "C6712 1\n",
      "C6952 1\n",
      "C7192 1\n",
      "C7492 1\n",
      "C7792 1\n",
      "C8092 1\n",
      "C8452 1\n",
      "C8812 1\n",
      "C9112 1\n",
      "C9412 1\n",
      "C9592 1\n",
      "C9892 1\n",
      "C10192 1\n",
      "C10552 1\n",
      "C10912 1\n",
      "C11212 1\n",
      "C11632 1\n",
      "C11932 1\n",
      "C12232 1\n",
      "C12412 1\n",
      "C12592 1\n",
      "C12772 1\n",
      "C13072 1\n",
      "C13132 1\n",
      "C13312 1\n",
      "C13492 1\n",
      "C13672 1\n",
      "C13912 1\n",
      "C14272 1\n",
      "C14572 1\n",
      "C14872 1\n",
      "C15172 1\n",
      "C15412 1\n",
      "C15772 1\n",
      "C16132 1\n",
      "C16372 1\n"
     ]
    }
   ],
   "source": [
    "for v in model.getVars():\n",
    "    if(v.x > 0.5):\n",
    "        print('%s %g' % (v.varName, v.x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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