## Imports

```python
import sys
import os
import pandas as pd
import time
import math
import random
import itertools
```

## Generating Solutions

```python
class Generate_Routes():

    def __init__(self, number_of_nodes, max_distance_travel, nodes_time_window, time_between_nodes, distance_between_nodes, unloading_time):

        self.time_window = []
        self.pruned_routes = []
        self.unloading_time  = []
        self.travel_time = []
        self.travel_distance = []
        self.number_of_nodes = number_of_nodes
        self.max_distance_travel = max_distance_travel

        self.unloading_time_path = unloading_time
        self.node_time_window_path = nodes_time_window
        self.time_between_nodes_path = time_between_nodes
        self.distance_between_nodes_path = distance_between_nodes

        self.set_travel_time()
        self.set_time_windows()
        self.set_unloading_time()
        self.set_travel_distance()
        self.run()

    '''
    Some Important Variables
    '''

    # Time Window
    def set_time_windows(self):
        nodes_time_window  = self.node_time_window_path
        with open(nodes_time_window, 'r') as f:
            for line in f:
                window_start, window_end = map(int,line.rstrip().split())
                self.time_window.append((window_start, window_end))
            f.close()

    # Unloading Time
    def set_unloading_time(self):
        with open(self.unloading_time_path,'r') as f:
            self.unloading_time  = list(map(int, f.readline().split()))

    # Distance Traveled
    def set_travel_distance(self):
        distance_data        = pd.read_csv(self.distance_between_nodes_path)
        distance_data        = distance_data.iloc[:self.number_of_nodes, 1:self.number_of_nodes+1]
        travel_distance      = distance_data.values.tolist()
        self.travel_distance = travel_distance

    # Travel Time
    def set_travel_time(self):
        travel_time_data = pd.read_csv(self.time_between_nodes_path)
        travel_time_data = travel_time_data.iloc[:self.number_of_nodes, 1:self.number_of_nodes+1]
        travel_time      = travel_time_data.values.tolist()
        travel_time      = [list(map(int, x)) for x in travel_time] #Converting to integer
        self.travel_time = travel_time

    '''

    Algorithm Functions

    '''

    def generateAllTours(self):
        ''' Generate tours '''
        nodes = [i for i in range(1, self.number_of_nodes)]
        tours_with_2_stops = list(itertools.permutations(nodes, 2))

        return tours_with_2_stops

    def isFeasibleTimeWindow(self, hub, stop_1, stop_2):

        '''
        Time_window[hub] + travel_time[hub][stop1]
        Time_window[stop1] travel_time[stop1][stop2] + time_window[stop2]
        '''

        ### Starting from hub, traveling to stop-1, unloading before the end of time window at stop-1
        if(self.time_window[hub][0] + self.travel_time[hub][stop_1] + self.unloading_time[stop_1] > self.time_window[stop_1][1]):
            return False

        ### Starting after unloading at stop-1, traveling to stop-2,
        ### unloading before the end of time window at stop-2
        elif(self.time_window[stop_1][0] + self.unloading_time[stop_1] + self.travel_time[stop_1][stop_2]
             + self.unloading_time[stop_2] > self.time_window[stop_2][1]):
            return False

        #Add more conditions if needed
        return True

    def pruneBasedOnTimeWindows(self, tours):
        hub = 0
        for tour in tours:
            stop_1, stop_2 = tour

            if self.isFeasibleTimeWindow(hub, stop_1, stop_2):
                self.pruned_routes.append([hub, stop_1, stop_2])
        return

    def isFeasibleDistance(self, hub, stop_1, stop_2):
        ''' Distance travelled by truck during tour should be less than max allowed distance'''

        if(self.distance_travel[hub][stop_1] + self.distance_travel[stop_1][stop_2] > self.max_distance_travel):
            return False
        return True

    def pruneBasedOnDistance(self):
        tours = self.pruned_routes
        for tour in tours:
            hub, stop_1, stop_2 = tour

            if self.isFeasibleDistance:
                continue
            else:
                self.pruned_routes.remove([hub, stop_1, stop_2])
            return

    def run(self):
        two_stops = self.generateAllTours()
        self.pruneBasedOnTimeWindows(two_stops)
        self.pruneBasedOnDistance()
        print("All Routes Generated")
```

### Printing to file and formatting it

```python
def print_routes_to_file(pruned_routes, pruned_routes_path):
    with open(pruned_routes_path, "w") as f:
        for route in pruned_routes:
            hub, stop_1, stop_2 = route
            f.write(str(hub) + " " + str(stop_1) + " " + str(stop_2) + "\n")
        f.close()

def format_obtained_routes(number_of_stations, final_routing_table_path, pruned_routes):

    #Table Header
    with open(final_routing_table_path, 'w') as f:
        f.write("Routes" + "," + "Stop_1" + "," + "Stop_2" + "\n")

        for i in range(number_of_stations):
            f.write(str(i) + "," + "S" + str(i) + "\n")
        f.close()

    with open(pruned_routes, 'r') as f:
        current_station = number_of_stations #just a counter

        for line in f:
            hub, stop_1, stop_2 = line.split()
            route_string = "S{},S{}\n".format(stop_1, stop_2)

            with open(final_routing_table_path, 'a') as f2:
                f2.write(str(current_station) + "," + route_string)
                current_station += 1
                f2.close()
        f.close()
```

## Main Function

Set paths and change variables here, then execute the next cell

```python
def main():

    nodes_time_window      = "data/nodes_time_window.txt"
    time_between_nodes     = "data/site_time.csv"
    distance_between_nodes = "data/Distance_Matrix_File.csv"
    unloading_time_path    = "data/unloading_time.txt"

    pruned_routes_path       = "data/pruned_routes.txt"
    final_routing_table_path = "data/final_routing_table.csv"
    demand_constraints_path  = "data/demand_delivery_contraint.csv"

    number_of_nodes = 19
    max_distance_travel = 1000000

    #Generate solution
    sol = Generate_Routes(number_of_nodes, max_distance_travel, nodes_time_window, time_between_nodes, distance_between_nodes, unloading_time_path)

    #Printing to file and formatting
    print_routes_to_file(sol.pruned_routes, pruned_routes_path)

    #Format data for Inventory Rotuing
    format_obtained_routes(sol.number_of_nodes - 1, final_routing_table_path, pruned_routes_path)

    '''
    Files created:
        - data/pruned_routes.txt
        - data/final_routing_table.csv
    '''

    # Inventory Routing Related
    # routes = pd.read_csv(final_routing_table_path).to_numpy()
    # demand = pd.read_csv(demand_constraints_path).to_numpy()

```

### Calling the main function

```python
main()
```

    All Routes Generated

```python
number_of_nodes      = 19
distance_data        = pd.read_csv("data/Distance_Matrix_File.csv")
distance_data        = distance_data.iloc[:number_of_nodes, :number_of_nodes]
travel_distance      = distance_data.values.tolist()
```

```python
distance_of_route = []
for stop in range(number_of_nodes):
    distance_of_route.append(travel_distance[0][stop])

with open("data/pruned_routes.txt", 'r') as f:
    for line in f:
        hub, stop_1, stop_2 = list(map(int,line.split()))
        route_distance = travel_distance[hub][stop_1] + travel_distance[stop_1][stop_2]
        distance_of_route.append(route_distance)
#         print(hub, stop_1, stop_2, route_distance)
```

```python
def cost_of_route(distance_of_route, multi_stop_route_factor = 1.5 ,cost_per_mile = 0.01 ,fixed_cost = 1):

    '''Variables'''
    total_cost = []

    for route in range(len(distance_of_route)):
        total_cost.append(fixed_cost + (distance_of_route[route] * cost_per_mile))

        if(route > number_of_nodes):
            total_cost[route] *= multi_stop_route_factor
    return total_cost

route_cost = cost_of_route(distance_of_route)
```

```python

```

# Inventory Routing Formulation

Decision Variable:

\begin{equation}
\begin{array}{11}
\ Y_{i,k} & \forall i \in R, k \in WD
\end{array}
\end{equation}
\begin{equation}
\begin{array}{11}
\ X_{i,j,k} & \forall i \in R, j \in S, k \in WD
\end{array}
\end{equation}

Objective Function:

\begin{equation}
\begin{array}{ll}
\text{minimise} &  \sum_{i \in R}  \sum_{k \in W} Y_{i,k}  c_{i,k}\\
\end{array}
\end{equation}

Subject to:
\
Respecting Demand Constraints

\begin{equation}
\begin{array}{11}
\sum_{i \in R} \sum_{k \in W}  X_{i,j,k} m_{i,j} = d_j & \forall j \in S
\end{array}
\end{equation}

\
Respecting Delivery Acceptance Constraints

\begin{equation}
\begin{array}{11}
\sum_{i \in R} X_{i,j,k} m_{i,j} \le a_j & \forall j \in S, k \in W
\end{array}
\end{equation}

\
Respecting Truck Constraints

\begin{equation}
\begin{array}{11}
\sum_{j \in S} X_{i,j,k}m_{i,j} \le C_iY_{i,k} & \forall i \in R, k \in W
\end{array}
\end{equation}

\
General Representations

\
Sets
* R: Set of routes
* S: Set of stores
* W: Set of days in a week

\
Parameters
\
$
\begin{equation}
\begin{array}{11}
d_j & \text{: Weekly demand of store j,} & j \in S
\end{array}
\end{equation}
$

$
\begin{equation}
\begin{array}{11}
a_j & \text{: Delivery acceptance limits for store j,} & j \in S
\end{array}
\end{equation}
$

$
\begin{equation}
\begin{array}{11}
m_{i,j} & \text{: Dummy variable to indicate 1 if store j is along route i and 0 otherwise,} & i \in R, j \in S
\end{array}
\end{equation}
$

$
\begin{equation}
\begin{array}{11}
c_{i,k} & \text{: Cost of  truck on each route i on day k }
\end{array}
\end{equation}
$

$
\begin{equation}
\begin{array}{11}
C_{i} & \text{: Full capacity of truck on route i on each day  }
\end{array}
\end{equation}
$

\
Variables
$
\begin{equation}
\begin{array}{11}
X_{i,j,k} & \text{: Units on each route i to be delivered to a store j on day k} \\
Y_{i,k} & \text{: If route i is used on day k}
\end{array}
\end{equation}
$

\
Entities
* i: Route in R
* j: Store in S
* k: Day in W

```python
#Importing Packages

from gurobipy import *
from math import sqrt
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import xlrd
```

```python
#Importing Data

routes = pd.read_csv("data/final_routing_table.csv").to_numpy()
print(routes)

demand = pd.read_csv("data/demand_delivery_contraint.csv").to_numpy()
print(demand)

```

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    [['S0' 0.0 1000]
     ['S1' 377.6032 83]
     ['S2' 157.0348 51]
     ['S3' 231.5846 64]
     ['S4' 18.4834 43]
     ['S5' 263.6946 76]
     ['S6' 263.9988 57]
     ['S7' 510.0628 118]
     ['S8' 404.1336 122]
     ['S9' 305.0736 65]
     ['S10' 230.3314 43]
     ['S11' 289.6608 74]
     ['S12' 227.0632 55]
     ['S13' 322.6028 101]
     ['S14' 103.2694 100]
     ['S15' 40.1726 100]
     ['S16' 107.32799999999999 100]
     ['S17' 118.7706 100]
     ['S18' 26.2652 100]]

```python
#Demand
d = demand[:,1]

#Delivery constraints
dc = demand[:,2]

#Route matrix
#Store names
snames = demand[:,0]

count = 1
num_routes = len(routes)
num_stores = len(demand)
mat = np.zeros(num_routes)
for i in snames:
    routestore1 = (routes[:,1] == str(i)) * 1
    routestore2 = (routes[:,2] == str(i)) * 1
    mainroutestore = routestore1 + routestore2
    mat = np.row_stack((mat, mainroutestore))
    #mat = np.concatenate((mat, mainroutestore),axis=1)

mat = np.array(mat, dtype='int16')[1:,:].transpose((1,0))

print(mat)

```

    [[1 0 0 ... 0 0 0]
     [0 1 0 ... 0 0 0]
     [0 0 1 ... 0 0 0]
     ...
     [0 0 0 ... 0 0 1]
     [0 0 0 ... 1 0 1]
     [0 0 0 ... 0 1 1]]

```python
#Shipping costs
c = np.full((num_routes, num_stores), 1000)
c

#Supply constraints (truck capacity)
t = np.full((num_routes,7), 30)
t
```

    array([[30, 30, 30, ..., 30, 30, 30],
           [30, 30, 30, ..., 30, 30, 30],
           [30, 30, 30, ..., 30, 30, 30],
           ...,
           [30, 30, 30, ..., 30, 30, 30],
           [30, 30, 30, ..., 30, 30, 30],
           [30, 30, 30, ..., 30, 30, 30]])

```python
#Transportation Problem
m4 = Model('transportation')

#Variables
#Variables are in proportion
var = m4.addVars(num_routes,num_stores,7)
yvar = m4.addVars(num_routes,7,vtype = GRB.BINARY)

#Objective
m4.setObjective(sum(yvar[i,k] * route_cost[i] for i in range(num_routes) for k in range(7)), GRB.MINIMIZE)

#Weekly Demand
for j in range(num_stores):
    m4.addConstr(sum(var[i,j,k]*mat[i,j] for i in range(num_routes) for k in range(7)) == d[j])

#Delivery Constraints
for j in range(num_stores):
    for k in range(7):
        m4.addConstr(sum(var[i,j,k]*mat[i,j] for i in range(num_routes)) <= dc[j]*sum(yvar[i,k]*mat[i,j] for i in range(num_routes)))

#Supply constraint
for i in range(num_routes):
    for k in range(7):
        m4.addConstr(sum(var[i,j,k]*mat[i,j] for j in range(num_stores)) <= t[i,k]*yvar[i,k])

#Solving the optimization problem
m4.optimize()

```

    Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
    Thread count: 2 physical cores, 4 logical processors, using up to 4 threads
    Optimize a model with 2329 rows, 43540 columns and 19089 nonzeros
    Model fingerprint: 0xb39931df
    Variable types: 41363 continuous, 2177 integer (2177 binary)
    Coefficient statistics:
      Matrix range     [1e+00, 1e+03]
      Objective range  [1e+00, 4e+03]
      Bounds range     [1e+00, 1e+00]
      RHS range        [2e+01, 5e+02]
    Found heuristic solution: objective 86501.235000
    Presolve removed 127 rows and 37149 columns
    Presolve time: 0.07s
    Presolved: 2202 rows, 6391 columns, 11382 nonzeros
    Variable types: 4221 continuous, 2170 integer (2170 binary)

    Root relaxation: objective 1.891175e+04, 3963 iterations, 0.04 seconds

        Nodes    |    Current Node    |     Objective Bounds      |     Work
     Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

         0     0 18911.7521    0   18 86501.2350 18911.7521  78.1%     -    0s
    H    0     0                    20245.446000 18911.7521  6.59%     -    0s
    H    0     0                    20089.842000 18911.7521  5.86%     -    0s
    H    0     0                    19707.063000 18911.7521  4.04%     -    0s
         0     0 19043.0925    0   27 19707.0630 19043.0925  3.37%     -    0s
    H    0     0                    19469.982500 19043.0925  2.19%     -    0s
         0     0 19043.0925    0   23 19469.9825 19043.0925  2.19%     -    0s
         0     0 19043.0925    0   18 19469.9825 19043.0925  2.19%     -    0s
         0     0 19043.0925    0   27 19469.9825 19043.0925  2.19%     -    0s
    H    0     0                    19393.978500 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   29 19393.9785 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   32 19393.9785 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   23 19393.9785 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   18 19393.9785 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   27 19393.9785 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   27 19393.9785 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   34 19393.9785 19043.0925  1.81%     -    1s
         0     0 19043.0925    0   23 19393.9785 19043.0925  1.81%     -    1s
         0     2 19043.0925    0   23 19393.9785 19043.0925  1.81%     -    2s
    H   27    30                    19386.004500 19089.0470  1.53%   9.0    2s
    H   28    30                    19378.202000 19089.0470  1.49%   8.7    2s
    H  148   128                    19373.869500 19089.0470  1.47%   5.7    3s
    H  185   146                    19371.445500 19089.0470  1.46%   5.1    3s
    H  441   274                    19364.111000 19105.2876  1.34%   5.9    4s
       475   292     cutoff   19      19364.1110 19105.2876  1.34%   5.9    5s
    H  801   468                    19362.183500 19167.0629  1.01%   9.6    7s
    H  805   448                    19360.479500 19167.7791  1.00%   9.5    7s
    H  942   478                    19360.029500 19178.1939  0.94%  10.2    8s
    H 1000   462                    19360.029492 19178.1939  0.94%  10.0    9s
      1137   490 19341.0747   59    6 19360.0295 19178.1939  0.94%  10.7   10s

    Cutting planes:
      Gomory: 7
      Implied bound: 7
      MIR: 75
      Flow cover: 85
      Relax-and-lift: 7

    Explored 1530 nodes (27086 simplex iterations) in 12.19 seconds
    Thread count was 4 (of 4 available processors)

    Solution count 10: 19360 19360 19360.5 ... 19394

    Solve interrupted
    Best objective 1.936002949312e+04, best bound 1.917936685079e+04, gap 0.9332%

```python
sum(yvar[i,2].getAttr("x")*mat for i in range(num_routes))

```

    array([[19.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0., 19.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0., 19., ...,  0.,  0.,  0.],
           ...,
           [ 0.,  0.,  0., ...,  0.,  0., 19.],
           [ 0.,  0.,  0., ..., 19.,  0., 19.],
           [ 0.,  0.,  0., ...,  0., 19., 19.]])

```python

#Printing the optimal solutions obtained
print("Optimal Solutions:")
for i, val in var.items():
    if val.getAttr("x") != 0:
        print("Number of units from route %g to store %g on day %g:\t %g " %(i[0]+1, i[1]+1, i[2]+1, val.getAttr("x")))

#Printing y
for i, val in yvar.items():
    print("Run route %g on day %g:\t %g " %(i[0]+1, i[1]+1, val.getAttr("x")))

print(yvar)

for i in range(num_routes):
    for k in range(7):
        print(yvar[i,k].getAttr("x"))

```

    Optimal Solutions:
    Number of units from route 3 to store 3 on day 1:	 7.0348
    Number of units from route 3 to store 3 on day 2:	 30
    Number of units from route 3 to store 3 on day 3:	 30
    Number of units from route 3 to store 3 on day 4:	 30
    Number of units from route 3 to store 3 on day 5:	 30
    Number of units from route 3 to store 3 on day 7:	 30
    Number of units from route 4 to store 4 on day 1:	 30
    Number of units from route 4 to store 4 on day 2:	 30
    Number of units from route 4 to store 4 on day 3:	 30
    Number of units from route 4 to store 4 on day 4:	 30
    Number of units from route 4 to store 4 on day 5:	 30
    Number of units from route 4 to store 4 on day 6:	 30
    Number of units from route 4 to store 4 on day 7:	 30
    Number of units from route 5 to store 5 on day 6:	 18.0586
    Number of units from route 6 to store 6 on day 1:	 30
    Number of units from route 6 to store 6 on day 2:	 30
    Number of units from route 6 to store 6 on day 3:	 30
    Number of units from route 6 to store 6 on day 4:	 30
    Number of units from route 6 to store 6 on day 5:	 30
    Number of units from route 6 to store 6 on day 6:	 30
    Number of units from route 6 to store 6 on day 7:	 30
    Number of units from route 7 to store 7 on day 1:	 30
    Number of units from route 7 to store 7 on day 2:	 30
    Number of units from route 7 to store 7 on day 3:	 30
    Number of units from route 7 to store 7 on day 4:	 30
    Number of units from route 7 to store 7 on day 5:	 30
    Number of units from route 7 to store 7 on day 6:	 30
    Number of units from route 7 to store 7 on day 7:	 30
    Number of units from route 8 to store 8 on day 1:	 30
    Number of units from route 8 to store 8 on day 2:	 30
    Number of units from route 8 to store 8 on day 3:	 30
    Number of units from route 8 to store 8 on day 4:	 30
    Number of units from route 8 to store 8 on day 5:	 30
    Number of units from route 8 to store 8 on day 6:	 30
    Number of units from route 8 to store 8 on day 7:	 30
    Number of units from route 9 to store 9 on day 1:	 30
    Number of units from route 9 to store 9 on day 2:	 30
    Number of units from route 9 to store 9 on day 3:	 30
    Number of units from route 9 to store 9 on day 4:	 30
    Number of units from route 9 to store 9 on day 5:	 30
    Number of units from route 9 to store 9 on day 6:	 30
    Number of units from route 9 to store 9 on day 7:	 30
    Number of units from route 11 to store 11 on day 1:	 30
    Number of units from route 11 to store 11 on day 2:	 30
    Number of units from route 11 to store 11 on day 3:	 30
    Number of units from route 11 to store 11 on day 4:	 30
    Number of units from route 11 to store 11 on day 5:	 30
    Number of units from route 11 to store 11 on day 6:	 30
    Number of units from route 11 to store 11 on day 7:	 30
    Number of units from route 12 to store 12 on day 1:	 30
    Number of units from route 12 to store 12 on day 2:	 30
    Number of units from route 12 to store 12 on day 3:	 30
    Number of units from route 12 to store 12 on day 4:	 30
    Number of units from route 12 to store 12 on day 5:	 30
    Number of units from route 12 to store 12 on day 6:	 30
    Number of units from route 12 to store 12 on day 7:	 30
    Number of units from route 13 to store 13 on day 1:	 30
    Number of units from route 13 to store 13 on day 2:	 30
    Number of units from route 13 to store 13 on day 3:	 30
    Number of units from route 13 to store 13 on day 4:	 30
    Number of units from route 13 to store 13 on day 5:	 30
    Number of units from route 13 to store 13 on day 6:	 30
    Number of units from route 13 to store 13 on day 7:	 30
    Number of units from route 14 to store 14 on day 1:	 30
    Number of units from route 14 to store 14 on day 2:	 30
    Number of units from route 14 to store 14 on day 3:	 30
    Number of units from route 14 to store 14 on day 4:	 30
    Number of units from route 14 to store 14 on day 5:	 30
    Number of units from route 14 to store 14 on day 6:	 30
    Number of units from route 14 to store 14 on day 7:	 30
    Number of units from route 15 to store 15 on day 1:	 30
    Number of units from route 15 to store 15 on day 3:	 30
    Number of units from route 15 to store 15 on day 4:	 13.2694
    Number of units from route 15 to store 15 on day 6:	 30
    Number of units from route 16 to store 16 on day 1:	 30
    Number of units from route 17 to store 17 on day 1:	 30
    Number of units from route 17 to store 17 on day 5:	 30
    Number of units from route 17 to store 17 on day 6:	 30
    Number of units from route 18 to store 18 on day 2:	 30
    Number of units from route 18 to store 18 on day 4:	 28.7706
    Number of units from route 18 to store 18 on day 6:	 30
    Number of units from route 18 to store 18 on day 7:	 30
    Number of units from route 54 to store 4 on day 3:	 21.5846
    Number of units from route 54 to store 6 on day 3:	 8.4154
    Number of units from route 86 to store 5 on day 7:	 0.4248
    Number of units from route 86 to store 6 on day 7:	 29.5752
    Number of units from route 90 to store 6 on day 3:	 6.0354
    Number of units from route 90 to store 10 on day 1:	 30
    Number of units from route 90 to store 10 on day 3:	 23.9646
    Number of units from route 90 to store 10 on day 5:	 30
    Number of units from route 91 to store 6 on day 5:	 9.6686
    Number of units from route 91 to store 11 on day 5:	 20.3314
    Number of units from route 99 to store 2 on day 1:	 30
    Number of units from route 99 to store 2 on day 2:	 30
    Number of units from route 99 to store 2 on day 3:	 30
    Number of units from route 99 to store 2 on day 4:	 30
    Number of units from route 99 to store 2 on day 5:	 30
    Number of units from route 99 to store 2 on day 6:	 30
    Number of units from route 99 to store 2 on day 7:	 30
    Number of units from route 106 to store 7 on day 2:	 1.2878
    Number of units from route 106 to store 10 on day 1:	 30
    Number of units from route 106 to store 10 on day 2:	 28.7122
    Number of units from route 106 to store 10 on day 3:	 30
    Number of units from route 106 to store 10 on day 4:	 30
    Number of units from route 106 to store 10 on day 5:	 30
    Number of units from route 106 to store 10 on day 6:	 30
    Number of units from route 106 to store 10 on day 7:	 30
    Number of units from route 113 to store 7 on day 5:	 22.711
    Number of units from route 113 to store 17 on day 5:	 7.289
    Number of units from route 135 to store 9 on day 1:	 30
    Number of units from route 135 to store 9 on day 4:	 30
    Number of units from route 135 to store 9 on day 5:	 30
    Number of units from route 135 to store 9 on day 6:	 30
    Number of units from route 135 to store 9 on day 7:	 30
    Number of units from route 137 to store 8 on day 1:	 30
    Number of units from route 137 to store 8 on day 2:	 30
    Number of units from route 137 to store 8 on day 3:	 30
    Number of units from route 137 to store 8 on day 4:	 30
    Number of units from route 137 to store 8 on day 5:	 5.8274
    Number of units from route 137 to store 8 on day 6:	 30
    Number of units from route 137 to store 8 on day 7:	 30
    Number of units from route 137 to store 9 on day 5:	 24.1726
    Number of units from route 145 to store 9 on day 5:	 19.961
    Number of units from route 145 to store 17 on day 5:	 10.039
    Number of units from route 147 to store 2 on day 1:	 30
    Number of units from route 147 to store 2 on day 2:	 30
    Number of units from route 147 to store 2 on day 3:	 17.6032
    Number of units from route 147 to store 2 on day 4:	 30
    Number of units from route 147 to store 2 on day 5:	 30
    Number of units from route 147 to store 2 on day 7:	 30
    Number of units from route 147 to store 10 on day 3:	 12.3968
    Number of units from route 190 to store 12 on day 1:	 30
    Number of units from route 190 to store 12 on day 2:	 30
    Number of units from route 190 to store 12 on day 5:	 19.6608
    Number of units from route 190 to store 14 on day 5:	 10.3392
    Number of units from route 206 to store 13 on day 2:	 17.0632
    Number of units from route 206 to store 14 on day 2:	 12.9368
    Number of units from route 250 to store 8 on day 1:	 24.2354
    Number of units from route 250 to store 8 on day 3:	 30
    Number of units from route 250 to store 8 on day 5:	 30
    Number of units from route 250 to store 8 on day 6:	 30
    Number of units from route 250 to store 16 on day 1:	 5.7646
    Number of units from route 256 to store 14 on day 1:	 29.3268
    Number of units from route 256 to store 14 on day 2:	 30
    Number of units from route 256 to store 14 on day 5:	 30
    Number of units from route 256 to store 16 on day 1:	 0.6732
    Number of units from route 260 to store 16 on day 3:	 3.7348
    Number of units from route 260 to store 19 on day 3:	 26.2652
    Number of units from route 283 to store 7 on day 5:	 30
    Run route 1 on day 1:	 0
    Run route 1 on day 2:	 0
    Run route 1 on day 3:	 0
    Run route 1 on day 4:	 0
    Run route 1 on day 5:	 0
    Run route 1 on day 6:	 0
    Run route 1 on day 7:	 0
    Run route 2 on day 1:	 -0
    Run route 2 on day 2:	 -0
    Run route 2 on day 3:	 -0
    Run route 2 on day 4:	 -0
    Run route 2 on day 5:	 -0
    Run route 2 on day 6:	 -0
    Run route 2 on day 7:	 -0
    Run route 3 on day 1:	 1
    Run route 3 on day 2:	 1
    Run route 3 on day 3:	 1
    Run route 3 on day 4:	 1
    Run route 3 on day 5:	 1
    Run route 3 on day 6:	 -0
    Run route 3 on day 7:	 1
    Run route 4 on day 1:	 1
    Run route 4 on day 2:	 1
    Run route 4 on day 3:	 1
    Run route 4 on day 4:	 1
    Run route 4 on day 5:	 1
    Run route 4 on day 6:	 1
    Run route 4 on day 7:	 1
    Run route 5 on day 1:	 -0
    Run route 5 on day 2:	 -0
    Run route 5 on day 3:	 -0
    Run route 5 on day 4:	 -0
    Run route 5 on day 5:	 -0
    Run route 5 on day 6:	 1
    Run route 5 on day 7:	 -0
    Run route 6 on day 1:	 1
    Run route 6 on day 2:	 1
    Run route 6 on day 3:	 1
    Run route 6 on day 4:	 1
    Run route 6 on day 5:	 1
    Run route 6 on day 6:	 1
    Run route 6 on day 7:	 1
    Run route 7 on day 1:	 1
    Run route 7 on day 2:	 1
    Run route 7 on day 3:	 1
    Run route 7 on day 4:	 1
    Run route 7 on day 5:	 1
    Run route 7 on day 6:	 1
    Run route 7 on day 7:	 1
    Run route 8 on day 1:	 1
    Run route 8 on day 2:	 1
    Run route 8 on day 3:	 1
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    Run route 8 on day 6:	 1
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    Run route 9 on day 1:	 1
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    Run route 9 on day 4:	 1
    Run route 9 on day 5:	 1
    Run route 9 on day 6:	 1
    Run route 9 on day 7:	 1
    Run route 10 on day 1:	 -0
    Run route 10 on day 2:	 -0
    Run route 10 on day 3:	 -0
    Run route 10 on day 4:	 -0
    Run route 10 on day 5:	 -0
    Run route 10 on day 6:	 -0
    Run route 10 on day 7:	 -0
    Run route 11 on day 1:	 1
    Run route 11 on day 2:	 1
    Run route 11 on day 3:	 1
    Run route 11 on day 4:	 1
    Run route 11 on day 5:	 1
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    Run route 12 on day 1:	 1
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    Run route 12 on day 3:	 1
    Run route 12 on day 4:	 1
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    Run route 12 on day 6:	 1
    Run route 12 on day 7:	 1
    Run route 13 on day 1:	 1
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    Run route 13 on day 3:	 1
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    Run route 13 on day 5:	 1
    Run route 13 on day 6:	 1
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    Run route 14 on day 1:	 1
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    Run route 14 on day 3:	 1
    Run route 14 on day 4:	 1
    Run route 14 on day 5:	 1
    Run route 14 on day 6:	 1
    Run route 14 on day 7:	 1
    Run route 15 on day 1:	 1
    Run route 15 on day 2:	 -0
    Run route 15 on day 3:	 1
    Run route 15 on day 4:	 1
    Run route 15 on day 5:	 0
    Run route 15 on day 6:	 1
    Run route 15 on day 7:	 -0
    Run route 16 on day 1:	 1
    Run route 16 on day 2:	 0
    Run route 16 on day 3:	 -0
    Run route 16 on day 4:	 -0
    Run route 16 on day 5:	 -0
    Run route 16 on day 6:	 -0
    Run route 16 on day 7:	 -0
    Run route 17 on day 1:	 1
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    Run route 17 on day 4:	 -0
    Run route 17 on day 5:	 1
    Run route 17 on day 6:	 1
    Run route 17 on day 7:	 -0
    Run route 18 on day 1:	 -0
    Run route 18 on day 2:	 1
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    Run route 18 on day 4:	 1
    Run route 18 on day 5:	 -0
    Run route 18 on day 6:	 1
    Run route 18 on day 7:	 1
    Run route 19 on day 1:	 0
    Run route 19 on day 2:	 0
    Run route 19 on day 3:	 0
    Run route 19 on day 4:	 0
    Run route 19 on day 5:	 0
    Run route 19 on day 6:	 0
    Run route 19 on day 7:	 0
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    Run route 20 on day 2:	 0
    Run route 20 on day 3:	 0
    Run route 20 on day 4:	 0
    Run route 20 on day 5:	 0
    Run route 20 on day 6:	 0
    Run route 20 on day 7:	 0
    Run route 21 on day 1:	 0
    Run route 21 on day 2:	 0
    Run route 21 on day 3:	 0
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    Run route 21 on day 5:	 0
    Run route 21 on day 6:	 0
    Run route 21 on day 7:	 0
    Run route 22 on day 1:	 0
    Run route 22 on day 2:	 0
    Run route 22 on day 3:	 0
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    Run route 22 on day 5:	 0
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    Run route 23 on day 1:	 0
    Run route 23 on day 2:	 0
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    Run route 23 on day 6:	 0
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    Run route 24 on day 2:	 0
    Run route 24 on day 3:	 0
    Run route 24 on day 4:	 0
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    Run route 25 on day 1:	 0
    Run route 25 on day 2:	 0
    Run route 25 on day 3:	 0
    Run route 25 on day 4:	 0
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    Run route 25 on day 6:	 0
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    Run route 26 on day 1:	 0
    Run route 26 on day 2:	 0
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    Run route 27 on day 2:	 -0
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    Run route 27 on day 7:	 -0
    Run route 28 on day 1:	 0
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    Run route 310 on day 7:	 0
    Run route 311 on day 1:	 0
    Run route 311 on day 2:	 0
    Run route 311 on day 3:	 0
    Run route 311 on day 4:	 0
    Run route 311 on day 5:	 0
    Run route 311 on day 6:	 0
    Run route 311 on day 7:	 0
    {(0, 0): <gurobi.Var C41363 (value 0.0)>, (0, 1): <gurobi.Var C41364 (value 0.0)>, (0, 2): <gurobi.Var C41365 (value 0.0)>, (0, 3): <gurobi.Var C41366 (value 0.0)>, (0, 4): <gurobi.Var C41367 (value 0.0)>, (0, 5): <gurobi.Var C41368 (value 0.0)>, (0, 6): <gurobi.Var C41369 (value 0.0)>, (1, 0): <gurobi.Var C41370 (value -0.0)>, (1, 1): <gurobi.Var C41371 (value -0.0)>, (1, 2): <gurobi.Var C41372 (value -0.0)>, (1, 3): <gurobi.Var C41373 (value -0.0)>, (1, 4): <gurobi.Var C41374 (value -0.0)>, (1, 5): <gurobi.Var C41375 (value -0.0)>, (1, 6): <gurobi.Var C41376 (value -0.0)>, (2, 0): <gurobi.Var C41377 (value 1.0)>, (2, 1): <gurobi.Var C41378 (value 1.0)>, (2, 2): <gurobi.Var C41379 (value 1.0)>, (2, 3): <gurobi.Var C41380 (value 1.0)>, (2, 4): <gurobi.Var C41381 (value 1.0)>, (2, 5): <gurobi.Var C41382 (value -0.0)>, (2, 6): <gurobi.Var C41383 (value 1.0)>, (3, 0): <gurobi.Var C41384 (value 1.0)>, (3, 1): <gurobi.Var C41385 (value 1.0)>, (3, 2): <gurobi.Var C41386 (value 1.0)>, (3, 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(146, 2): <gurobi.Var C42387 (value 1.0)>, (146, 3): <gurobi.Var C42388 (value 1.0)>, (146, 4): <gurobi.Var C42389 (value 1.0)>, (146, 5): <gurobi.Var C42390 (value -0.0)>, (146, 6): <gurobi.Var C42391 (value 1.0)>, (147, 0): <gurobi.Var C42392 (value -0.0)>, (147, 1): <gurobi.Var C42393 (value -0.0)>, (147, 2): <gurobi.Var C42394 (value -0.0)>, (147, 3): <gurobi.Var C42395 (value -0.0)>, (147, 4): <gurobi.Var C42396 (value -0.0)>, (147, 5): <gurobi.Var C42397 (value -0.0)>, (147, 6): <gurobi.Var C42398 (value -0.0)>, (148, 0): <gurobi.Var C42399 (value 0.0)>, (148, 1): <gurobi.Var C42400 (value 0.0)>, (148, 2): <gurobi.Var C42401 (value 0.0)>, (148, 3): <gurobi.Var C42402 (value 0.0)>, (148, 4): <gurobi.Var C42403 (value 0.0)>, (148, 5): <gurobi.Var C42404 (value 0.0)>, (148, 6): <gurobi.Var C42405 (value 0.0)>, (149, 0): <gurobi.Var C42406 (value 0.0)>, (149, 1): <gurobi.Var C42407 (value 0.0)>, (149, 2): <gurobi.Var C42408 (value 0.0)>, (149, 3): <gurobi.Var C42409 (value 0.0)>, (149, 4): <gurobi.Var C42410 (value 0.0)>, (149, 5): <gurobi.Var C42411 (value 0.0)>, (149, 6): <gurobi.Var C42412 (value 0.0)>, (150, 0): <gurobi.Var C42413 (value 0.0)>, (150, 1): <gurobi.Var C42414 (value 0.0)>, (150, 2): <gurobi.Var C42415 (value 0.0)>, (150, 3): <gurobi.Var C42416 (value 0.0)>, (150, 4): <gurobi.Var C42417 (value 0.0)>, (150, 5): <gurobi.Var C42418 (value 0.0)>, (150, 6): <gurobi.Var C42419 (value 0.0)>, (151, 0): <gurobi.Var C42420 (value 0.0)>, (151, 1): <gurobi.Var C42421 (value 0.0)>, (151, 2): <gurobi.Var C42422 (value 0.0)>, (151, 3): <gurobi.Var C42423 (value 0.0)>, (151, 4): <gurobi.Var C42424 (value 0.0)>, (151, 5): <gurobi.Var C42425 (value 0.0)>, (151, 6): <gurobi.Var C42426 (value 0.0)>, (152, 0): <gurobi.Var C42427 (value 0.0)>, (152, 1): <gurobi.Var C42428 (value 0.0)>, (152, 2): <gurobi.Var C42429 (value 0.0)>, (152, 3): <gurobi.Var C42430 (value 0.0)>, (152, 4): <gurobi.Var C42431 (value 0.0)>, (152, 5): <gurobi.Var C42432 (value 0.0)>, (152, 6): <gurobi.Var C42433 (value 0.0)>, (153, 0): <gurobi.Var C42434 (value 0.0)>, (153, 1): <gurobi.Var C42435 (value 0.0)>, (153, 2): <gurobi.Var C42436 (value 0.0)>, (153, 3): <gurobi.Var C42437 (value 0.0)>, (153, 4): <gurobi.Var C42438 (value 0.0)>, (153, 5): <gurobi.Var C42439 (value 0.0)>, (153, 6): <gurobi.Var C42440 (value 0.0)>, (154, 0): <gurobi.Var C42441 (value -0.0)>, (154, 1): <gurobi.Var C42442 (value -0.0)>, (154, 2): <gurobi.Var C42443 (value -0.0)>, (154, 3): <gurobi.Var C42444 (value -0.0)>, (154, 4): <gurobi.Var C42445 (value -0.0)>, (154, 5): <gurobi.Var C42446 (value -0.0)>, (154, 6): <gurobi.Var C42447 (value -0.0)>, (155, 0): <gurobi.Var C42448 (value 0.0)>, (155, 1): <gurobi.Var C42449 (value 0.0)>, (155, 2): <gurobi.Var C42450 (value 0.0)>, (155, 3): <gurobi.Var C42451 (value 0.0)>, (155, 4): <gurobi.Var C42452 (value 0.0)>, (155, 5): <gurobi.Var C42453 (value 0.0)>, (155, 6): <gurobi.Var C42454 (value 0.0)>, (156, 0): <gurobi.Var C42455 (value 0.0)>, (156, 1): <gurobi.Var C42456 (value 0.0)>, (156, 2): <gurobi.Var C42457 (value 0.0)>, (156, 3): <gurobi.Var C42458 (value 0.0)>, (156, 4): <gurobi.Var C42459 (value 0.0)>, (156, 5): <gurobi.Var C42460 (value 0.0)>, (156, 6): <gurobi.Var C42461 (value 0.0)>, (157, 0): <gurobi.Var C42462 (value 0.0)>, (157, 1): <gurobi.Var C42463 (value 0.0)>, (157, 2): <gurobi.Var C42464 (value 0.0)>, (157, 3): <gurobi.Var C42465 (value 0.0)>, (157, 4): <gurobi.Var C42466 (value 0.0)>, (157, 5): <gurobi.Var C42467 (value 0.0)>, (157, 6): <gurobi.Var C42468 (value 0.0)>, (158, 0): <gurobi.Var C42469 (value 0.0)>, (158, 1): <gurobi.Var C42470 (value 0.0)>, (158, 2): <gurobi.Var C42471 (value 0.0)>, (158, 3): <gurobi.Var C42472 (value 0.0)>, (158, 4): <gurobi.Var C42473 (value 0.0)>, (158, 5): <gurobi.Var C42474 (value 0.0)>, (158, 6): <gurobi.Var C42475 (value 0.0)>, (159, 0): <gurobi.Var C42476 (value 0.0)>, (159, 1): <gurobi.Var C42477 (value 0.0)>, (159, 2): <gurobi.Var C42478 (value 0.0)>, (159, 3): <gurobi.Var C42479 (value 0.0)>, (159, 4): <gurobi.Var C42480 (value 0.0)>, (159, 5): <gurobi.Var C42481 (value 0.0)>, (159, 6): <gurobi.Var C42482 (value 0.0)>, (160, 0): <gurobi.Var C42483 (value 0.0)>, (160, 1): <gurobi.Var C42484 (value 0.0)>, (160, 2): <gurobi.Var C42485 (value 0.0)>, (160, 3): <gurobi.Var C42486 (value 0.0)>, (160, 4): <gurobi.Var C42487 (value 0.0)>, (160, 5): <gurobi.Var C42488 (value 0.0)>, (160, 6): <gurobi.Var C42489 (value 0.0)>, (161, 0): <gurobi.Var C42490 (value 0.0)>, (161, 1): <gurobi.Var C42491 (value 0.0)>, (161, 2): <gurobi.Var C42492 (value 0.0)>, (161, 3): <gurobi.Var C42493 (value 0.0)>, (161, 4): <gurobi.Var C42494 (value 0.0)>, (161, 5): <gurobi.Var C42495 (value 0.0)>, (161, 6): <gurobi.Var C42496 (value 0.0)>, (162, 0): <gurobi.Var C42497 (value -0.0)>, (162, 1): <gurobi.Var C42498 (value -0.0)>, (162, 2): <gurobi.Var C42499 (value -0.0)>, (162, 3): <gurobi.Var C42500 (value -0.0)>, (162, 4): <gurobi.Var C42501 (value -0.0)>, (162, 5): <gurobi.Var C42502 (value -0.0)>, (162, 6): <gurobi.Var C42503 (value -0.0)>, (163, 0): <gurobi.Var C42504 (value 0.0)>, (163, 1): <gurobi.Var C42505 (value 0.0)>, (163, 2): <gurobi.Var C42506 (value 0.0)>, (163, 3): <gurobi.Var C42507 (value 0.0)>, (163, 4): <gurobi.Var C42508 (value 0.0)>, (163, 5): <gurobi.Var C42509 (value 0.0)>, (163, 6): <gurobi.Var C42510 (value 0.0)>, (164, 0): <gurobi.Var C42511 (value -0.0)>, (164, 1): <gurobi.Var C42512 (value -0.0)>, (164, 2): <gurobi.Var C42513 (value -0.0)>, (164, 3): <gurobi.Var C42514 (value -0.0)>, (164, 4): <gurobi.Var C42515 (value -0.0)>, (164, 5): <gurobi.Var C42516 (value -0.0)>, (164, 6): <gurobi.Var C42517 (value -0.0)>, (165, 0): <gurobi.Var C42518 (value 0.0)>, (165, 1): <gurobi.Var C42519 (value 0.0)>, (165, 2): <gurobi.Var C42520 (value 0.0)>, (165, 3): <gurobi.Var C42521 (value 0.0)>, (165, 4): <gurobi.Var C42522 (value 0.0)>, (165, 5): <gurobi.Var C42523 (value 0.0)>, (165, 6): <gurobi.Var C42524 (value 0.0)>, (166, 0): <gurobi.Var C42525 (value 0.0)>, (166, 1): <gurobi.Var C42526 (value 0.0)>, (166, 2): <gurobi.Var C42527 (value 0.0)>, (166, 3): <gurobi.Var C42528 (value 0.0)>, (166, 4): <gurobi.Var C42529 (value 0.0)>, (166, 5): <gurobi.Var C42530 (value 0.0)>, (166, 6): <gurobi.Var C42531 (value 0.0)>, (167, 0): <gurobi.Var C42532 (value 0.0)>, (167, 1): <gurobi.Var C42533 (value 0.0)>, (167, 2): <gurobi.Var C42534 (value 0.0)>, (167, 3): <gurobi.Var C42535 (value 0.0)>, (167, 4): <gurobi.Var C42536 (value 0.0)>, (167, 5): <gurobi.Var C42537 (value 0.0)>, (167, 6): <gurobi.Var C42538 (value 0.0)>, (168, 0): <gurobi.Var C42539 (value 0.0)>, (168, 1): <gurobi.Var C42540 (value 0.0)>, (168, 2): <gurobi.Var C42541 (value 0.0)>, (168, 3): <gurobi.Var C42542 (value 0.0)>, (168, 4): <gurobi.Var C42543 (value 0.0)>, (168, 5): <gurobi.Var C42544 (value 0.0)>, (168, 6): <gurobi.Var C42545 (value 0.0)>, (169, 0): <gurobi.Var C42546 (value 0.0)>, (169, 1): <gurobi.Var C42547 (value 0.0)>, (169, 2): <gurobi.Var C42548 (value 0.0)>, (169, 3): <gurobi.Var C42549 (value 0.0)>, (169, 4): <gurobi.Var C42550 (value 0.0)>, (169, 5): <gurobi.Var C42551 (value 0.0)>, (169, 6): <gurobi.Var C42552 (value 0.0)>, (170, 0): <gurobi.Var C42553 (value 0.0)>, (170, 1): <gurobi.Var C42554 (value 0.0)>, (170, 2): <gurobi.Var C42555 (value 0.0)>, (170, 3): <gurobi.Var C42556 (value 0.0)>, (170, 4): <gurobi.Var C42557 (value 0.0)>, (170, 5): <gurobi.Var C42558 (value 0.0)>, (170, 6): <gurobi.Var C42559 (value 0.0)>, (171, 0): <gurobi.Var C42560 (value 0.0)>, (171, 1): <gurobi.Var C42561 (value 0.0)>, (171, 2): <gurobi.Var C42562 (value 0.0)>, (171, 3): <gurobi.Var C42563 (value 0.0)>, (171, 4): <gurobi.Var C42564 (value 0.0)>, (171, 5): <gurobi.Var C42565 (value 0.0)>, (171, 6): <gurobi.Var C42566 (value 0.0)>, (172, 0): <gurobi.Var C42567 (value -0.0)>, (172, 1): <gurobi.Var C42568 (value -0.0)>, (172, 2): <gurobi.Var C42569 (value -0.0)>, (172, 3): <gurobi.Var C42570 (value -0.0)>, (172, 4): <gurobi.Var C42571 (value -0.0)>, (172, 5): <gurobi.Var C42572 (value -0.0)>, (172, 6): <gurobi.Var C42573 (value -0.0)>, (173, 0): <gurobi.Var C42574 (value -0.0)>, (173, 1): <gurobi.Var C42575 (value -0.0)>, (173, 2): <gurobi.Var C42576 (value -0.0)>, (173, 3): <gurobi.Var C42577 (value -0.0)>, (173, 4): <gurobi.Var C42578 (value -0.0)>, (173, 5): <gurobi.Var C42579 (value -0.0)>, (173, 6): <gurobi.Var C42580 (value -0.0)>, (174, 0): <gurobi.Var C42581 (value -0.0)>, (174, 1): <gurobi.Var C42582 (value -0.0)>, (174, 2): <gurobi.Var C42583 (value -0.0)>, (174, 3): <gurobi.Var C42584 (value -0.0)>, (174, 4): <gurobi.Var C42585 (value -0.0)>, (174, 5): <gurobi.Var C42586 (value -0.0)>, (174, 6): <gurobi.Var C42587 (value -0.0)>, (175, 0): <gurobi.Var C42588 (value 0.0)>, (175, 1): <gurobi.Var C42589 (value 0.0)>, (175, 2): <gurobi.Var C42590 (value 0.0)>, (175, 3): <gurobi.Var C42591 (value 0.0)>, (175, 4): <gurobi.Var C42592 (value 0.0)>, (175, 5): <gurobi.Var C42593 (value 0.0)>, (175, 6): <gurobi.Var C42594 (value 0.0)>, (176, 0): <gurobi.Var C42595 (value 0.0)>, (176, 1): <gurobi.Var C42596 (value 0.0)>, (176, 2): <gurobi.Var C42597 (value 0.0)>, (176, 3): <gurobi.Var C42598 (value 0.0)>, (176, 4): <gurobi.Var C42599 (value 0.0)>, (176, 5): <gurobi.Var C42600 (value 0.0)>, (176, 6): <gurobi.Var C42601 (value 0.0)>, (177, 0): <gurobi.Var C42602 (value 0.0)>, (177, 1): <gurobi.Var C42603 (value 0.0)>, (177, 2): <gurobi.Var C42604 (value 0.0)>, (177, 3): <gurobi.Var C42605 (value 0.0)>, (177, 4): <gurobi.Var C42606 (value 0.0)>, (177, 5): <gurobi.Var C42607 (value 0.0)>, (177, 6): <gurobi.Var C42608 (value 0.0)>, (178, 0): <gurobi.Var C42609 (value 0.0)>, (178, 1): <gurobi.Var C42610 (value 0.0)>, (178, 2): <gurobi.Var C42611 (value 0.0)>, (178, 3): <gurobi.Var C42612 (value 0.0)>, (178, 4): <gurobi.Var C42613 (value 0.0)>, (178, 5): <gurobi.Var C42614 (value 0.0)>, (178, 6): <gurobi.Var C42615 (value 0.0)>, (179, 0): <gurobi.Var C42616 (value 0.0)>, (179, 1): <gurobi.Var C42617 (value 0.0)>, (179, 2): <gurobi.Var C42618 (value 0.0)>, (179, 3): <gurobi.Var C42619 (value 0.0)>, (179, 4): <gurobi.Var C42620 (value 0.0)>, (179, 5): <gurobi.Var C42621 (value 0.0)>, (179, 6): <gurobi.Var C42622 (value 0.0)>, (180, 0): <gurobi.Var C42623 (value -0.0)>, (180, 1): <gurobi.Var C42624 (value -0.0)>, (180, 2): <gurobi.Var C42625 (value -0.0)>, (180, 3): <gurobi.Var C42626 (value -0.0)>, (180, 4): <gurobi.Var C42627 (value -0.0)>, (180, 5): <gurobi.Var C42628 (value -0.0)>, (180, 6): <gurobi.Var C42629 (value -0.0)>, (181, 0): <gurobi.Var C42630 (value -0.0)>, (181, 1): <gurobi.Var C42631 (value -0.0)>, (181, 2): <gurobi.Var C42632 (value -0.0)>, (181, 3): <gurobi.Var C42633 (value -0.0)>, (181, 4): <gurobi.Var C42634 (value -0.0)>, (181, 5): <gurobi.Var C42635 (value -0.0)>, (181, 6): <gurobi.Var C42636 (value -0.0)>, (182, 0): <gurobi.Var C42637 (value -0.0)>, (182, 1): <gurobi.Var C42638 (value -0.0)>, (182, 2): <gurobi.Var C42639 (value -0.0)>, (182, 3): <gurobi.Var C42640 (value 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0.0)>, (185, 6): <gurobi.Var C42664 (value 0.0)>, (186, 0): <gurobi.Var C42665 (value -0.0)>, (186, 1): <gurobi.Var C42666 (value -0.0)>, (186, 2): <gurobi.Var C42667 (value -0.0)>, (186, 3): <gurobi.Var C42668 (value -0.0)>, (186, 4): <gurobi.Var C42669 (value -0.0)>, (186, 5): <gurobi.Var C42670 (value -0.0)>, (186, 6): <gurobi.Var C42671 (value -0.0)>, (187, 0): <gurobi.Var C42672 (value 0.0)>, (187, 1): <gurobi.Var C42673 (value 0.0)>, (187, 2): <gurobi.Var C42674 (value 0.0)>, (187, 3): <gurobi.Var C42675 (value 0.0)>, (187, 4): <gurobi.Var C42676 (value 0.0)>, (187, 5): <gurobi.Var C42677 (value 0.0)>, (187, 6): <gurobi.Var C42678 (value 0.0)>, (188, 0): <gurobi.Var C42679 (value -0.0)>, (188, 1): <gurobi.Var C42680 (value -0.0)>, (188, 2): <gurobi.Var C42681 (value -0.0)>, (188, 3): <gurobi.Var C42682 (value -0.0)>, (188, 4): <gurobi.Var C42683 (value -0.0)>, (188, 5): <gurobi.Var C42684 (value -0.0)>, (188, 6): <gurobi.Var C42685 (value -0.0)>, (189, 0): <gurobi.Var C42686 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C42778 (value 0.0)>, (202, 2): <gurobi.Var C42779 (value 0.0)>, (202, 3): <gurobi.Var C42780 (value 0.0)>, (202, 4): <gurobi.Var C42781 (value 0.0)>, (202, 5): <gurobi.Var C42782 (value 0.0)>, (202, 6): <gurobi.Var C42783 (value 0.0)>, (203, 0): <gurobi.Var C42784 (value 0.0)>, (203, 1): <gurobi.Var C42785 (value 0.0)>, (203, 2): <gurobi.Var C42786 (value 0.0)>, (203, 3): <gurobi.Var C42787 (value 0.0)>, (203, 4): <gurobi.Var C42788 (value 0.0)>, (203, 5): <gurobi.Var C42789 (value 0.0)>, (203, 6): <gurobi.Var C42790 (value 0.0)>, (204, 0): <gurobi.Var C42791 (value -0.0)>, (204, 1): <gurobi.Var C42792 (value -0.0)>, (204, 2): <gurobi.Var C42793 (value -0.0)>, (204, 3): <gurobi.Var C42794 (value -0.0)>, (204, 4): <gurobi.Var C42795 (value -0.0)>, (204, 5): <gurobi.Var C42796 (value -0.0)>, (204, 6): <gurobi.Var C42797 (value -0.0)>, (205, 0): <gurobi.Var C42798 (value -0.0)>, (205, 1): <gurobi.Var C42799 (value 1.0)>, (205, 2): <gurobi.Var C42800 (value -0.0)>, (205, 3): <gurobi.Var C42801 (value -0.0)>, (205, 4): <gurobi.Var C42802 (value -0.0)>, (205, 5): <gurobi.Var C42803 (value -0.0)>, (205, 6): <gurobi.Var C42804 (value -0.0)>, (206, 0): <gurobi.Var C42805 (value -0.0)>, (206, 1): <gurobi.Var C42806 (value -0.0)>, (206, 2): <gurobi.Var C42807 (value -0.0)>, (206, 3): <gurobi.Var C42808 (value -0.0)>, (206, 4): <gurobi.Var C42809 (value -0.0)>, (206, 5): <gurobi.Var C42810 (value -0.0)>, (206, 6): <gurobi.Var C42811 (value -0.0)>, (207, 0): <gurobi.Var C42812 (value 0.0)>, (207, 1): <gurobi.Var C42813 (value 0.0)>, (207, 2): <gurobi.Var C42814 (value 0.0)>, (207, 3): <gurobi.Var C42815 (value 0.0)>, (207, 4): <gurobi.Var C42816 (value 0.0)>, (207, 5): <gurobi.Var C42817 (value 0.0)>, (207, 6): <gurobi.Var C42818 (value 0.0)>, (208, 0): <gurobi.Var C42819 (value 0.0)>, (208, 1): <gurobi.Var C42820 (value 0.0)>, (208, 2): <gurobi.Var C42821 (value 0.0)>, (208, 3): <gurobi.Var C42822 (value 0.0)>, (208, 4): <gurobi.Var C42823 (value 0.0)>, (208, 5): <gurobi.Var C42824 (value 0.0)>, (208, 6): <gurobi.Var C42825 (value 0.0)>, (209, 0): <gurobi.Var C42826 (value 0.0)>, (209, 1): <gurobi.Var C42827 (value 0.0)>, (209, 2): <gurobi.Var C42828 (value 0.0)>, (209, 3): <gurobi.Var C42829 (value 0.0)>, (209, 4): <gurobi.Var C42830 (value 0.0)>, (209, 5): <gurobi.Var C42831 (value 0.0)>, (209, 6): <gurobi.Var C42832 (value 0.0)>, (210, 0): <gurobi.Var C42833 (value 0.0)>, (210, 1): <gurobi.Var C42834 (value 0.0)>, (210, 2): <gurobi.Var C42835 (value 0.0)>, (210, 3): <gurobi.Var C42836 (value 0.0)>, (210, 4): <gurobi.Var C42837 (value 0.0)>, (210, 5): <gurobi.Var C42838 (value 0.0)>, (210, 6): <gurobi.Var C42839 (value 0.0)>, (211, 0): <gurobi.Var C42840 (value 0.0)>, (211, 1): <gurobi.Var C42841 (value 0.0)>, (211, 2): <gurobi.Var C42842 (value 0.0)>, (211, 3): <gurobi.Var C42843 (value 0.0)>, (211, 4): <gurobi.Var C42844 (value 0.0)>, (211, 5): <gurobi.Var C42845 (value 0.0)>, (211, 6): <gurobi.Var C42846 (value 0.0)>, (212, 0): <gurobi.Var C42847 (value -0.0)>, (212, 1): <gurobi.Var C42848 (value -0.0)>, (212, 2): <gurobi.Var C42849 (value -0.0)>, (212, 3): <gurobi.Var C42850 (value -0.0)>, (212, 4): <gurobi.Var C42851 (value -0.0)>, (212, 5): <gurobi.Var C42852 (value -0.0)>, (212, 6): <gurobi.Var C42853 (value -0.0)>, (213, 0): <gurobi.Var C42854 (value 0.0)>, (213, 1): <gurobi.Var C42855 (value 0.0)>, (213, 2): <gurobi.Var C42856 (value 0.0)>, (213, 3): <gurobi.Var C42857 (value 0.0)>, (213, 4): <gurobi.Var C42858 (value 0.0)>, (213, 5): <gurobi.Var C42859 (value 0.0)>, (213, 6): <gurobi.Var C42860 (value 0.0)>, (214, 0): <gurobi.Var C42861 (value 0.0)>, (214, 1): <gurobi.Var C42862 (value 0.0)>, (214, 2): <gurobi.Var C42863 (value 0.0)>, (214, 3): <gurobi.Var C42864 (value 0.0)>, (214, 4): <gurobi.Var C42865 (value 0.0)>, (214, 5): <gurobi.Var C42866 (value 0.0)>, (214, 6): <gurobi.Var C42867 (value 0.0)>, (215, 0): <gurobi.Var C42868 (value 0.0)>, (215, 1): <gurobi.Var C42869 (value 0.0)>, (215, 2): <gurobi.Var C42870 (value 0.0)>, (215, 3): <gurobi.Var C42871 (value 0.0)>, (215, 4): <gurobi.Var C42872 (value 0.0)>, (215, 5): <gurobi.Var C42873 (value 0.0)>, (215, 6): <gurobi.Var C42874 (value 0.0)>, (216, 0): <gurobi.Var C42875 (value 0.0)>, (216, 1): <gurobi.Var C42876 (value 0.0)>, (216, 2): <gurobi.Var C42877 (value 0.0)>, (216, 3): <gurobi.Var C42878 (value 0.0)>, (216, 4): <gurobi.Var C42879 (value 0.0)>, (216, 5): <gurobi.Var C42880 (value 0.0)>, (216, 6): <gurobi.Var C42881 (value 0.0)>, (217, 0): <gurobi.Var C42882 (value 0.0)>, (217, 1): <gurobi.Var C42883 (value 0.0)>, (217, 2): <gurobi.Var C42884 (value 0.0)>, (217, 3): <gurobi.Var C42885 (value 0.0)>, (217, 4): <gurobi.Var C42886 (value 0.0)>, (217, 5): <gurobi.Var C42887 (value 0.0)>, (217, 6): <gurobi.Var C42888 (value 0.0)>, (218, 0): <gurobi.Var C42889 (value 0.0)>, (218, 1): <gurobi.Var C42890 (value 0.0)>, (218, 2): <gurobi.Var C42891 (value 0.0)>, (218, 3): <gurobi.Var C42892 (value 0.0)>, (218, 4): <gurobi.Var C42893 (value 0.0)>, (218, 5): <gurobi.Var C42894 (value 0.0)>, (218, 6): <gurobi.Var C42895 (value 0.0)>, (219, 0): <gurobi.Var C42896 (value 0.0)>, (219, 1): <gurobi.Var C42897 (value 0.0)>, (219, 2): <gurobi.Var C42898 (value 0.0)>, (219, 3): <gurobi.Var C42899 (value 0.0)>, (219, 4): <gurobi.Var C42900 (value 0.0)>, (219, 5): <gurobi.Var C42901 (value 0.0)>, (219, 6): <gurobi.Var C42902 (value 0.0)>, (220, 0): <gurobi.Var C42903 (value -0.0)>, (220, 1): <gurobi.Var C42904 (value -0.0)>, (220, 2): <gurobi.Var C42905 (value -0.0)>, (220, 3): <gurobi.Var C42906 (value -0.0)>, (220, 4): <gurobi.Var C42907 (value -0.0)>, (220, 5): <gurobi.Var C42908 (value -0.0)>, (220, 6): <gurobi.Var C42909 (value -0.0)>, (221, 0): <gurobi.Var C42910 (value -0.0)>, (221, 1): <gurobi.Var C42911 (value -0.0)>, (221, 2): <gurobi.Var C42912 (value -0.0)>, (221, 3): <gurobi.Var C42913 (value -0.0)>, (221, 4): <gurobi.Var C42914 (value -0.0)>, (221, 5): <gurobi.Var C42915 (value -0.0)>, (221, 6): <gurobi.Var C42916 (value -0.0)>, (222, 0): <gurobi.Var C42917 (value -0.0)>, (222, 1): <gurobi.Var C42918 (value -0.0)>, (222, 2): <gurobi.Var C42919 (value -0.0)>, (222, 3): <gurobi.Var C42920 (value -0.0)>, (222, 4): <gurobi.Var C42921 (value -0.0)>, (222, 5): <gurobi.Var C42922 (value -0.0)>, (222, 6): <gurobi.Var C42923 (value -0.0)>, (223, 0): <gurobi.Var C42924 (value 0.0)>, (223, 1): <gurobi.Var C42925 (value 0.0)>, (223, 2): <gurobi.Var C42926 (value 0.0)>, (223, 3): <gurobi.Var C42927 (value 0.0)>, (223, 4): <gurobi.Var C42928 (value 0.0)>, (223, 5): <gurobi.Var C42929 (value 0.0)>, (223, 6): <gurobi.Var C42930 (value 0.0)>, (224, 0): <gurobi.Var C42931 (value 0.0)>, (224, 1): <gurobi.Var C42932 (value 0.0)>, (224, 2): <gurobi.Var C42933 (value 0.0)>, (224, 3): <gurobi.Var C42934 (value 0.0)>, (224, 4): <gurobi.Var C42935 (value 0.0)>, (224, 5): <gurobi.Var C42936 (value 0.0)>, (224, 6): <gurobi.Var C42937 (value 0.0)>, (225, 0): <gurobi.Var C42938 (value 0.0)>, (225, 1): <gurobi.Var C42939 (value 0.0)>, (225, 2): <gurobi.Var C42940 (value 0.0)>, (225, 3): <gurobi.Var C42941 (value 0.0)>, (225, 4): <gurobi.Var C42942 (value 0.0)>, (225, 5): <gurobi.Var C42943 (value 0.0)>, (225, 6): <gurobi.Var C42944 (value 0.0)>, (226, 0): <gurobi.Var C42945 (value 0.0)>, (226, 1): <gurobi.Var C42946 (value 0.0)>, (226, 2): <gurobi.Var C42947 (value 0.0)>, (226, 3): <gurobi.Var C42948 (value 0.0)>, (226, 4): <gurobi.Var C42949 (value 0.0)>, (226, 5): <gurobi.Var C42950 (value 0.0)>, (226, 6): <gurobi.Var C42951 (value 0.0)>, (227, 0): <gurobi.Var C42952 (value -0.0)>, (227, 1): <gurobi.Var C42953 (value -0.0)>, (227, 2): <gurobi.Var C42954 (value -0.0)>, (227, 3): <gurobi.Var C42955 (value -0.0)>, (227, 4): <gurobi.Var C42956 (value -0.0)>, (227, 5): <gurobi.Var C42957 (value -0.0)>, (227, 6): <gurobi.Var C42958 (value -0.0)>, (228, 0): <gurobi.Var C42959 (value 0.0)>, (228, 1): <gurobi.Var C42960 (value 0.0)>, (228, 2): <gurobi.Var C42961 (value 0.0)>, (228, 3): <gurobi.Var C42962 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C42985 (value -0.0)>, (231, 6): <gurobi.Var C42986 (value -0.0)>, (232, 0): <gurobi.Var C42987 (value -0.0)>, (232, 1): <gurobi.Var C42988 (value -0.0)>, (232, 2): <gurobi.Var C42989 (value -0.0)>, (232, 3): <gurobi.Var C42990 (value -0.0)>, (232, 4): <gurobi.Var C42991 (value -0.0)>, (232, 5): <gurobi.Var C42992 (value -0.0)>, (232, 6): <gurobi.Var C42993 (value -0.0)>, (233, 0): <gurobi.Var C42994 (value -0.0)>, (233, 1): <gurobi.Var C42995 (value -0.0)>, (233, 2): <gurobi.Var C42996 (value -0.0)>, (233, 3): <gurobi.Var C42997 (value -0.0)>, (233, 4): <gurobi.Var C42998 (value -0.0)>, (233, 5): <gurobi.Var C42999 (value -0.0)>, (233, 6): <gurobi.Var C43000 (value -0.0)>, (234, 0): <gurobi.Var C43001 (value -0.0)>, (234, 1): <gurobi.Var C43002 (value -0.0)>, (234, 2): <gurobi.Var C43003 (value -0.0)>, (234, 3): <gurobi.Var C43004 (value -0.0)>, (234, 4): <gurobi.Var C43005 (value -0.0)>, (234, 5): <gurobi.Var C43006 (value -0.0)>, (234, 6): <gurobi.Var C43007 (value -0.0)>, (235, 0): 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```python

```

```python

```

# Time Windows Optimisation

Set of optimal routes -> `j`

Number of days -> `k = 7`

Numer of time slots -> `t = 48`

```python
number_of_nodes      = 20

all_routes = []
optimal_routes = []
unloading_time = []
optimal_routes_number = []

d_j1 = []
d_j2 = []

lower_bounds = []
upper_bounds = []

lower_bound_stop_1 = []
lower_bound_stop_2 = []
upper_bound_stop_1 = []
upper_bound_stop_2 = []

lower_bound_hub = []
upper_bound_hub = []

Y_jk = []

```

```python
with open("data/unloading_time.txt", 'r') as f:
    unloading_time = list(map(int, f.readline().split()))

distance_data        = pd.read_csv("data/site_time.csv")
distance_data        = distance_data.iloc[:number_of_nodes, 1:number_of_nodes+1]
travel_distance      = distance_data.values.tolist()
```

- Mapping optimal route number --> actual route number
- Optimal_route_number[n] returns the index corresponding to the n^th optimal route

- `optimal_routes_number[j]` --> `i`

```python
for i in range(num_routes):
    for k in range(7):
        if(yvar[i,k].getAttr("x") != 0):
            optimal_routes_number.append(i)
            break
```

```python
with open("data/pruned_routes.txt", 'r') as f:
    for line in f:
        hub, stop_1, stop_2 = list(map(int,line.split()))
        all_routes.append([hub, stop_1, stop_2])

for index in optimal_routes_number:
    optimal_routes.append(all_routes[index])
```

```python
for hub, stop_1, stop_2 in optimal_routes:
    d_j1.append(travel_distance[hub][stop_1])
    d_j2.append(travel_distance[hub][stop_1] + unloading_time[stop_1] + travel_distance[stop_1][stop_2])
```

```python
for route_number in optimal_routes_number:
    day = []
    for weekday in range(7):
        if(yvar[route_number,weekday].getAttr("x") != 0):
            day.append(1)
        else:
            day.append(0)
    Y_jk.append(day)
```

```python
Y_jk
```

    [[1, 1, 1, 1, 1, 0, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [0, 0, 0, 0, 0, 1, 0],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 0, 1, 1, 0, 1, 0],
     [1, 0, 0, 0, 0, 0, 0],
     [1, 0, 0, 0, 1, 1, 0],
     [0, 1, 0, 1, 0, 1, 1],
     [0, 0, 1, 0, 0, 0, 0],
     [0, 0, 0, 0, 0, 0, 1],
     [1, 0, 1, 0, 1, 0, 0],
     [0, 0, 0, 0, 1, 0, 0],
     [1, 1, 1, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [0, 0, 0, 0, 1, 0, 0],
     [1, 0, 0, 1, 1, 1, 1],
     [1, 1, 1, 1, 1, 1, 1],
     [0, 0, 0, 0, 1, 0, 0],
     [1, 1, 1, 1, 1, 0, 1],
     [1, 1, 0, 0, 1, 0, 0],
     [0, 1, 0, 0, 0, 0, 0],
     [1, 0, 1, 0, 1, 1, 0],
     [1, 1, 0, 0, 1, 0, 0],
     [0, 0, 1, 0, 0, 0, 0],
     [0, 0, 0, 0, 1, 0, 0]]

```python
optimal_routes
```

    [[0, 1, 4],
     [0, 1, 5],
     [0, 1, 6],
     [0, 1, 7],
     [0, 1, 8],
     [0, 1, 9],
     [0, 1, 10],
     [0, 1, 12],
     [0, 1, 13],
     [0, 1, 14],
     [0, 1, 15],
     [0, 1, 16],
     [0, 1, 17],
     [0, 2, 1],
     [0, 2, 3],
     [0, 4, 7],
     [0, 6, 7],
     [0, 6, 11],
     [0, 6, 12],
     [0, 7, 3],
     [0, 7, 11],
     [0, 8, 1],
     [0, 9, 7],
     [0, 9, 10],
     [0, 10, 1],
     [0, 10, 3],
     [0, 12, 15],
     [0, 13, 15],
     [0, 16, 8],
     [0, 16, 14],
     [0, 17, 1],
     [0, 18, 7]]

```python
print(d_j1)
```

    [1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.407175, 1.75544375, 1.75544375, 0.3104875, 0.2923, 0.2923, 0.2923, 0.44930625, 0.44930625, 0.25653125, 0.9347875, 0.9347875, 1.80436875, 1.80436875, 1.6456375, 1.7986375, 0.36789375, 0.36789375, 0.2014375, 7.7930875]

```python
lower_bounds = []
upper_bounds = []

lower_bound_stop_1 = []
lower_bound_stop_2 = []
upper_bound_stop_1 = []
upper_bound_stop_2 = []

lower_bound_hub = []
upper_bound_hub = []

with open("data/nodes_time_window.txt", 'r') as f:
    for line in f:
        lower_bound, upper_bound = map(int, line.split())
        lower_bounds.append(lower_bound)
        upper_bounds.append(upper_bound)

for hub, stop_1, stop_2 in optimal_routes:
    lower_bound_stop_1.append(lower_bounds[stop_1])
    lower_bound_stop_2.append(lower_bounds[stop_2])
    upper_bound_stop_1.append(upper_bounds[stop_1])
    upper_bound_stop_2.append(upper_bounds[stop_2])

    lower_bound_hub.append(lower_bounds[hub])
    upper_bound_hub.append(upper_bounds[hub])
```

```python
print(lower_bound_stop_1)
print(lower_bound_stop_2)
print("\n")
print(upper_bound_stop_1)
print(upper_bound_stop_2)
```

    [35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 17, 17, 17, 17]
    [35, 35, 35, 35, 35, 35, 35, 35, 35, 17, 17, 17, 17, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 17, 17, 35, 17, 35, 35]

    [48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 44, 44, 44, 44]
    [48, 48, 48, 48, 48, 48, 48, 48, 48, 44, 44, 44, 44, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 44, 44, 48, 44, 48, 48]

## Optimisation Formulation
###### Please run all previous code starting here

```python
num_routes = len(optimal_routes)
num_days   = 7
time_slots = 48
```

```python
Y_jk = np.array(Y_jk)

#Time Windows Problem
m0 = Model('Time_Windows')

#Variables
#Variables are in proportion
Z_jkt = m0.addVars(num_routes, num_days, time_slots, vtype = GRB.BINARY)
slack = m0.addVars(num_days, time_slots, vtype = GRB.CONTINUOUS)

#Objective - Minimise Total Slack
m0.setObjective(sum(slack[k,t] for k in range(num_days) for t in range(time_slots)), GRB.MINIMIZE)

#Use a route only once in a day
for j in range(num_routes):
    for k in range(num_days):
        m0.addConstr(sum(Z_jkt[j,k,t] for t in range(time_slots)) == 1)

#Maximum slack allowed contraint
for k in range(num_days):
    for t in range(time_slots):
        m0.addConstr(sum(Z_jkt[j,k,t] for j in range(num_routes)) <= 3 + slack[k,t])

# #Route is chosen zjkt <= yjk
for j in range(num_routes):
    for k in range(num_days):
        for t in range(time_slots):
            m0.addConstr(Z_jkt[j,k,t] <= Y_jk[j, k])

# #Upper Bounds and Lower Bounds
# #-# 1. Hub
for j in range(num_routes):
    for k in range(num_days):
        m0.addConstr(sum(t * Z_jkt[j,k,t] for t in range(time_slots))
                     <= upper_bound_hub[j])

for j in range(num_routes):
    for k in range(num_days):
        m0.addConstr(lower_bound_hub[j] <= sum(t * Z_jkt[j,k,t] for t in range(time_slots))
                     )

#-# 2. Stop_1
for j in range(num_routes):
    for k in range(num_days):
        m0.addConstr(sum(t * Z_jkt[j,k,t] for t in range(time_slots)) + d_j1[j]
                     <= upper_bound_stop_1[j])

for j in range(num_routes):
    for k in range(num_days):
        m0.addConstr(lower_bound_stop_1[j] <= sum(t * Z_jkt[j,k,t] for t in range(time_slots)) + d_j1[j]
                     )
#-# 3. Stop_2
for j in range(num_routes):
    for k in range(num_days):
        m0.addConstr(lower_bound_stop_2[j] <= sum(t * Z_jkt[j,k,t] for t in range(time_slots)) + d_j2[j]
                     )

for j in range(num_routes):
    for k in range(num_days):
        m0.addConstr(sum(t * Z_jkt[j,k,t] for t in range(time_slots)) + d_j2[j]
                     <= upper_bound_stop_2[j])

#Solving the optimization problem
m0.optimize()

#Printing the optimal solutions obtained
# print("Optimal Solutions:")
# for i, val in var.items():
#     if val.getAttr("x") != 0:
#         print("Number of units from route %g to store %g on day %g:\t %g " %(i[0]+1, i[1]+1, i[2]+1, val.getAttr("x")))

# #Printing y
# for i, val in yvar.items():
#     print("Run route %g on day %g:\t %g " %(i[0]+1, i[1]+1, val.getAttr("x")))

# print(yvar)
```

    Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
    Thread count: 2 physical cores, 4 logical processors, using up to 4 threads
    Optimize a model with 12656 rows, 11088 columns and 95760 nonzeros
    Model fingerprint: 0x024c3f09
    Variable types: 336 continuous, 10752 integer (10752 binary)
    Coefficient statistics:
      Matrix range     [1e+00, 5e+01]
      Objective range  [1e+00, 1e+00]
      Bounds range     [1e+00, 1e+00]
      RHS range        [1e+00, 5e+01]
    Presolve removed 6480 rows and 0 columns
    Presolve time: 0.01s

    Explored 0 nodes (0 simplex iterations) in 0.11 seconds
    Thread count was 1 (of 4 available processors)

    Solution count 0

    Model is infeasible
    Best objective -, best bound -, gap -

```python

```