ViveK-PothinA/cvrp_reload_1.py

## cvrp_reload_1.py
#!/usr/bin/env python
# This Python file uses the following encoding: utf-8
# Copyright 2015 Tin Arm Engineering AB
# Copyright 2018 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Capacitated Vehicle Routing Problem (CVRP).

   This is a sample using the routing library python wrapper to solve a CVRP
   problem while allowing multiple trips, i.e., vehicles can return to a depot
   to reset their load ("reload").

   A description of the CVRP problem can be found here:
   http://en.wikipedia.org/wiki/Vehicle_routing_problem.

   Distances are in meters.

   In order to implement multiple trips, new nodes are introduced at the same
   locations of the original depots. These additional nodes can be dropped
   from the schedule at 0 cost.

   The max_slack parameter associated to the capacity constraints of all nodes
   can be set to be the maximum of the vehicles' capacities, rather than 0 like
   in a traditional CVRP. Slack is required since before a solution is found,
   it is not known how much capacity will be transferred at the new nodes. For
   all the other (original) nodes, the slack is then re-set to 0.

   The above two considerations are implemented in `add_capacity_constraints()`.

   Last, it is useful to set a large distance between the initial depot and the
   new nodes introduced, to avoid schedules having spurious transits through
   those new nodes unless it's necessary to reload. This consideration is taken
   into account in `create_distance_evaluator()`.
"""


from functools import partial
import math

from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2


###########################
# Problem Data Definition #
###########################
def create_data_model():
    """Stores the data for the problem"""
    data = {}
    _capacity = 15
    data['locations'] = [
      (13.0631889, 77.44637), # depot
      (13.0631889, 77.44637), # unload depot_first
      (13.0631889, 77.44637), # unload depot_second
      (13.0631889, 77.44637), # unload depot_third
      (13.0631889, 77.44637), # unload depot_fourth
      (13.0631889, 77.44637), # unload depot_fifth
      (13.053150040354716, 77.54204884698993), #1
      (12.838937782758835, 77.50531602323832), #2
      (12.784447514250903, 77.75280704152361)] #3
    data['num_locations'] = len(data['locations'])
    data['demands'] = \
          [0, # depot
           -_capacity, # unload depot_first
           -_capacity, # unload depot_second
           -_capacity, # unload depot_third,
           -_capacity, # unload depot_fourth
           -_capacity, # unload depot_fifth
           3, 3, # 1, 2
           3] # 3
    data['time_per_demand_unit'] = 5  # 5 minutes/unit
    data['time_windows'] = [
        (0, 0),  # depot
        (0, 1000), # unload depot_first
           (0, 1000), # unload depot_second
           (0, 1000), # unload depot_third
           (0, 1000), # unload depot_fourth
           (0, 1000), # unload depot_fifth
        (60, 120),  # 1
        (60, 120),  # 2
        (60, 120),  # 3
    ]
    data['num_vehicles'] = 10
    data['vehicle_capacity'] = _capacity
    data['vehicle_max_distance'] = 10_000
    data['vehicle_max_time'] = 1_500
    data['vehicle_speed'] = 25 * 60 / 3.6  # Travel speed: 25km/h to convert in m/min
    data['depot'] = 0
    # print(data['locations'])
    return data


#######################
# Problem Constraints #
#######################
def gps_distance(position_1, position_2):
  lat1 = position_1[0]
  lat2 = position_2[0]
  lon1 = position_1[1]
  lon2 = position_2[1]

  R = 6378.137; # Radius of earth in KM
  dLat = lat2 * math.pi / 180 - lat1 * math.pi / 180;
  dLon = lon2 * math.pi / 180 - lon1 * math.pi / 180;
  a = math.sin(dLat/2) * math.sin(dLat/2) + math.cos(lat1 * math.pi / 180) * math.cos(lat2 * math.pi / 180) * math.sin(dLon/2) * math.sin(dLon/2);
  c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a));
  d = R * c * 1000; # in meters
  return d

def create_distance_evaluator(data):
    """Creates callback to return distance between points."""
    _distances = {}
    # precompute distance between location to have distance callback in O(1)
    for from_node in range(data['num_locations']):
        _distances[from_node] = {}
        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _distances[from_node][to_node] = 0
            # Forbid start/end/reload node to be consecutive.
            elif from_node in range(6) and to_node in range(6):
                _distances[from_node][to_node] = data['vehicle_max_distance']
            else:
                _distances[from_node][to_node] = (gps_distance(
                    data['locations'][from_node], data['locations'][to_node]))

    def distance_evaluator(manager, from_node, to_node):
        """Returns the manhattan distance between the two nodes"""
        return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
            to_node)]

    return distance_evaluator


def add_distance_dimension(routing, manager, data, distance_evaluator_index):
    """Add Global Span constraint"""
    distance = 'Distance'
    routing.AddDimension(
        distance_evaluator_index,
        0,  # null slack
        data['vehicle_max_distance'],  # maximum distance per vehicle
        True,  # start cumul to zero
        distance)
    distance_dimension = routing.GetDimensionOrDie(distance)
    # Try to minimize the max distance among vehicles.
    # /!\ It doesn't mean the standard deviation is minimized
    distance_dimension.SetGlobalSpanCostCoefficient(100)


def create_demand_evaluator(data):
    """Creates callback to get demands at each location."""
    _demands = data['demands']

    def demand_evaluator(manager, from_node):
        """Returns the demand of the current node"""
        return _demands[manager.IndexToNode(from_node)]

    return demand_evaluator


def add_capacity_constraints(routing, manager, data, demand_evaluator_index):
    """Adds capacity constraint"""
    vehicle_capacity = data['vehicle_capacity']
    capacity = 'Capacity'
    routing.AddDimension(
        demand_evaluator_index,
        vehicle_capacity,
        vehicle_capacity,
        True,  # start cumul to zero
        capacity)

    # Add Slack for reseting to zero unload depot nodes.
    # e.g. vehicle with load 10/15 arrives at node 1 (depot unload)
    # so we have CumulVar = 10(current load) + -15(unload) + 5(slack) = 0.
    capacity_dimension = routing.GetDimensionOrDie(capacity)
    # Allow to drop reloading nodes with zero cost.
    for node in [1, 2, 3, 4, 5]:
        node_index = manager.NodeToIndex(node)
        routing.AddDisjunction([node_index], 0)

    # Allow to drop regular node with a cost.
    for node in range(6, len(data['demands'])):
        node_index = manager.NodeToIndex(node)
        capacity_dimension.SlackVar(node_index).SetValue(0)
        routing.AddDisjunction([node_index], 100_000)


def create_time_evaluator(data):
    """Creates callback to get total times between locations."""

    def service_time(data, node):
        """Gets the service time for the specified location."""
        return abs(data['demands'][node]) * data['time_per_demand_unit']

    def travel_time(data, from_node, to_node):
        """Gets the travel times between two locations."""
        if from_node == to_node:
            travel_time = 0
        else:
            travel_time = gps_distance(
                    data['locations'][from_node], data['locations'][to_node]) / data['vehicle_speed']
        return travel_time

    _total_time = {}
    # precompute total time to have time callback in O(1)
    for from_node in range(data['num_locations']):
        _total_time[from_node] = {}
        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _total_time[from_node][to_node] = 0
            else:
                _total_time[from_node][to_node] = int(
                    service_time(data, from_node) + travel_time(
                        data, from_node, to_node))

    def time_evaluator(manager, from_node, to_node):
        """Returns the total time between the two nodes"""
        return _total_time[manager.IndexToNode(from_node)][manager.IndexToNode(
            to_node)]

    return time_evaluator


def add_time_window_constraints(routing, manager, data, time_evaluator):
    """Add Time windows constraint"""
    time = 'Time'
    max_time = data['vehicle_max_time']
    routing.AddDimension(
        time_evaluator,
        max_time,  # allow waiting time
        max_time,  # maximum time per vehicle
        False,  # don't force start cumul to zero since we are giving TW to start nodes
        time)
    time_dimension = routing.GetDimensionOrDie(time)
    # Add time window constraints for each location except depot
    # and 'copy' the slack var in the solution object (aka Assignment) to print it
    for location_idx, time_window in enumerate(data['time_windows']):
        if location_idx == 0:
            continue
        index = manager.NodeToIndex(location_idx)
        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
        routing.AddToAssignment(time_dimension.SlackVar(index))
    # Add time window constraints for each vehicle start node
    # and 'copy' the slack var in the solution object (aka Assignment) to print it
    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],
                                                data['time_windows'][0][1])
        routing.AddToAssignment(time_dimension.SlackVar(index))
        # Warning: Slack var is not defined for vehicle's end node
        #routing.AddToAssignment(time_dimension.SlackVar(self.routing.End(vehicle_id)))


###########
# Printer #
###########
def print_solution(data, manager, routing, assignment):  # pylint:disable=too-many-locals
    """Prints assignment on console"""
    print(f'Objective: {assignment.ObjectiveValue()}')
    total_distance = 0
    total_load = 0
    total_time = 0
    capacity_dimension = routing.GetDimensionOrDie('Capacity')
    time_dimension = routing.GetDimensionOrDie('Time')
    dropped = []
    for order in range(6, routing.nodes()):
        index = manager.NodeToIndex(order)
        if assignment.Value(routing.NextVar(index)) == index:
            dropped.append(order)
    print(f'dropped orders: {dropped}')
    for reload in range(1, 6):
        index = manager.NodeToIndex(reload)
        if assignment.Value(routing.NextVar(index)) == index:
            dropped.append(reload)
    print(f'dropped reload stations: {dropped}')

    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        plan_output = f'Route for vehicle {vehicle_id}:\n'
        distance = 0
        while not routing.IsEnd(index):
            load_var = capacity_dimension.CumulVar(index)
            time_var = time_dimension.CumulVar(index)
            plan_output += ' {0} Load({1}) Time({2},{3}) ->'.format(
                manager.IndexToNode(index),
                assignment.Value(load_var),
                assignment.Min(time_var), assignment.Max(time_var))
            previous_index = index
            index = assignment.Value(routing.NextVar(index))
            distance += routing.GetArcCostForVehicle(previous_index, index,
                                                     vehicle_id)
        load_var = capacity_dimension.CumulVar(index)
        time_var = time_dimension.CumulVar(index)
        plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
            manager.IndexToNode(index),
            assignment.Value(load_var),
            assignment.Min(time_var), assignment.Max(time_var))
        plan_output += f'Distance of the route: {distance}m\n'
        plan_output += f'Load of the route: {assignment.Value(load_var)}\n'
        plan_output += f'Time of the route: {assignment.Value(time_var)}min\n'
        print(plan_output)
        total_distance += distance
        total_load += assignment.Value(load_var)
        total_time += assignment.Value(time_var)
    print('Total Distance of all routes: {}m'.format(total_distance))
    print('Total Load of all routes: {}'.format(total_load))
    print('Total Time of all routes: {}min'.format(total_time))


########
# Main #
########
def main():
    """Entry point of the program"""
    # Instantiate the data problem.
    data = create_data_model()

    # Create the routing index manager
    manager = pywrapcp.RoutingIndexManager(data['num_locations'],
                                           data['num_vehicles'], data['depot'])

    # Create Routing Model
    routing = pywrapcp.RoutingModel(manager)

    # Define weight of each edge
    distance_evaluator_index = routing.RegisterTransitCallback(
        partial(create_distance_evaluator(data), manager))
    routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)

    # Add Distance constraint to minimize the longuest route
    add_distance_dimension(routing, manager, data, distance_evaluator_index)

    # Add Capacity constraint
    demand_evaluator_index = routing.RegisterUnaryTransitCallback(
        partial(create_demand_evaluator(data), manager))
    add_capacity_constraints(routing, manager, data, demand_evaluator_index)

    # Add Time Window constraint
    time_evaluator_index = routing.RegisterTransitCallback(
        partial(create_time_evaluator(data), manager))
    add_time_window_constraints(routing, manager, data, time_evaluator_index)

    # Setting first solution heuristic (cheapest addition).
    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.AUTOMATIC)  # pylint: disable=no-member
    search_parameters.local_search_metaheuristic = (
        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
    search_parameters.time_limit.FromSeconds(60)

    # Solve the problem.
    solution = routing.SolveWithParameters(search_parameters)
    if solution:
        print_solution(data, manager, routing, solution)
    else:
        print("No solution found !")


if __name__ == '__main__':
    main()
	#!/usr/bin/env python
	# This Python file uses the following encoding: utf-8
	# Copyright 2015 Tin Arm Engineering AB
	# Copyright 2018 Google LLC
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Capacitated Vehicle Routing Problem (CVRP).

	This is a sample using the routing library python wrapper to solve a CVRP
	problem while allowing multiple trips, i.e., vehicles can return to a depot
	to reset their load ("reload").

	A description of the CVRP problem can be found here:
	http://en.wikipedia.org/wiki/Vehicle_routing_problem.

	Distances are in meters.

	In order to implement multiple trips, new nodes are introduced at the same
	locations of the original depots. These additional nodes can be dropped
	from the schedule at 0 cost.

	The max_slack parameter associated to the capacity constraints of all nodes
	can be set to be the maximum of the vehicles' capacities, rather than 0 like
	in a traditional CVRP. Slack is required since before a solution is found,
	it is not known how much capacity will be transferred at the new nodes. For
	all the other (original) nodes, the slack is then re-set to 0.

	The above two considerations are implemented in `add_capacity_constraints()`.

	Last, it is useful to set a large distance between the initial depot and the
	new nodes introduced, to avoid schedules having spurious transits through
	those new nodes unless it's necessary to reload. This consideration is taken
	into account in `create_distance_evaluator()`.
	"""


	from functools import partial
	import math

	from ortools.constraint_solver import pywrapcp
	from ortools.constraint_solver import routing_enums_pb2


	###########################
	# Problem Data Definition #
	###########################
	def create_data_model():
	"""Stores the data for the problem"""
	data = {}
	_capacity = 15
	data['locations'] = [
	(13.0631889, 77.44637), # depot
	(13.0631889, 77.44637), # unload depot_first
	(13.0631889, 77.44637), # unload depot_second
	(13.0631889, 77.44637), # unload depot_third
	(13.0631889, 77.44637), # unload depot_fourth
	(13.0631889, 77.44637), # unload depot_fifth
	(13.053150040354716, 77.54204884698993), #1
	(12.838937782758835, 77.50531602323832), #2
	(12.784447514250903, 77.75280704152361)] #3
	data['num_locations'] = len(data['locations'])
	data['demands'] = \
	[0, # depot
	-_capacity, # unload depot_first
	-_capacity, # unload depot_second
	-_capacity, # unload depot_third,
	-_capacity, # unload depot_fourth
	-_capacity, # unload depot_fifth
	3, 3, # 1, 2
	3] # 3
	data['time_per_demand_unit'] = 5 # 5 minutes/unit
	data['time_windows'] = [
	(0, 0), # depot
	(0, 1000), # unload depot_first
	(0, 1000), # unload depot_second
	(0, 1000), # unload depot_third
	(0, 1000), # unload depot_fourth
	(0, 1000), # unload depot_fifth
	(60, 120), # 1
	(60, 120), # 2
	(60, 120), # 3
	]
	data['num_vehicles'] = 10
	data['vehicle_capacity'] = _capacity
	data['vehicle_max_distance'] = 10_000
	data['vehicle_max_time'] = 1_500
	data['vehicle_speed'] = 25 * 60 / 3.6 # Travel speed: 25km/h to convert in m/min
	data['depot'] = 0
	# print(data['locations'])
	return data


	#######################
	# Problem Constraints #
	#######################
	def gps_distance(position_1, position_2):
	lat1 = position_1[0]
	lat2 = position_2[0]
	lon1 = position_1[1]
	lon2 = position_2[1]

	R = 6378.137; # Radius of earth in KM
	dLat = lat2 * math.pi / 180 - lat1 * math.pi / 180;
	dLon = lon2 * math.pi / 180 - lon1 * math.pi / 180;
	a = math.sin(dLat/2) * math.sin(dLat/2) + math.cos(lat1 * math.pi / 180) * math.cos(lat2 * math.pi / 180) * math.sin(dLon/2) * math.sin(dLon/2);
	c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a));
	d = R * c * 1000; # in meters
	return d

	def create_distance_evaluator(data):
	"""Creates callback to return distance between points."""
	_distances = {}
	# precompute distance between location to have distance callback in O(1)
	for from_node in range(data['num_locations']):
	_distances[from_node] = {}
	for to_node in range(data['num_locations']):
	if from_node == to_node:
	_distances[from_node][to_node] = 0
	# Forbid start/end/reload node to be consecutive.
	elif from_node in range(6) and to_node in range(6):
	_distances[from_node][to_node] = data['vehicle_max_distance']
	else:
	_distances[from_node][to_node] = (gps_distance(
	data['locations'][from_node], data['locations'][to_node]))

	def distance_evaluator(manager, from_node, to_node):
	"""Returns the manhattan distance between the two nodes"""
	return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
	to_node)]

	return distance_evaluator


	def add_distance_dimension(routing, manager, data, distance_evaluator_index):
	"""Add Global Span constraint"""
	distance = 'Distance'
	routing.AddDimension(
	distance_evaluator_index,
	0, # null slack
	data['vehicle_max_distance'], # maximum distance per vehicle
	True, # start cumul to zero
	distance)
	distance_dimension = routing.GetDimensionOrDie(distance)
	# Try to minimize the max distance among vehicles.
	# /!\ It doesn't mean the standard deviation is minimized
	distance_dimension.SetGlobalSpanCostCoefficient(100)


	def create_demand_evaluator(data):
	"""Creates callback to get demands at each location."""
	_demands = data['demands']

	def demand_evaluator(manager, from_node):
	"""Returns the demand of the current node"""
	return _demands[manager.IndexToNode(from_node)]

	return demand_evaluator


	def add_capacity_constraints(routing, manager, data, demand_evaluator_index):
	"""Adds capacity constraint"""
	vehicle_capacity = data['vehicle_capacity']
	capacity = 'Capacity'
	routing.AddDimension(
	demand_evaluator_index,
	vehicle_capacity,
	vehicle_capacity,
	True, # start cumul to zero
	capacity)

	# Add Slack for reseting to zero unload depot nodes.
	# e.g. vehicle with load 10/15 arrives at node 1 (depot unload)
	# so we have CumulVar = 10(current load) + -15(unload) + 5(slack) = 0.
	capacity_dimension = routing.GetDimensionOrDie(capacity)
	# Allow to drop reloading nodes with zero cost.
	for node in [1, 2, 3, 4, 5]:
	node_index = manager.NodeToIndex(node)
	routing.AddDisjunction([node_index], 0)

	# Allow to drop regular node with a cost.
	for node in range(6, len(data['demands'])):
	node_index = manager.NodeToIndex(node)
	capacity_dimension.SlackVar(node_index).SetValue(0)
	routing.AddDisjunction([node_index], 100_000)


	def create_time_evaluator(data):
	"""Creates callback to get total times between locations."""

	def service_time(data, node):
	"""Gets the service time for the specified location."""
	return abs(data['demands'][node]) * data['time_per_demand_unit']

	def travel_time(data, from_node, to_node):
	"""Gets the travel times between two locations."""
	if from_node == to_node:
	travel_time = 0
	else:
	travel_time = gps_distance(
	data['locations'][from_node], data['locations'][to_node]) / data['vehicle_speed']
	return travel_time

	_total_time = {}
	# precompute total time to have time callback in O(1)
	for from_node in range(data['num_locations']):
	_total_time[from_node] = {}
	for to_node in range(data['num_locations']):
	if from_node == to_node:
	_total_time[from_node][to_node] = 0
	else:
	_total_time[from_node][to_node] = int(
	service_time(data, from_node) + travel_time(
	data, from_node, to_node))

	def time_evaluator(manager, from_node, to_node):
	"""Returns the total time between the two nodes"""
	return _total_time[manager.IndexToNode(from_node)][manager.IndexToNode(
	to_node)]

	return time_evaluator


	def add_time_window_constraints(routing, manager, data, time_evaluator):
	"""Add Time windows constraint"""
	time = 'Time'
	max_time = data['vehicle_max_time']
	routing.AddDimension(
	time_evaluator,
	max_time, # allow waiting time
	max_time, # maximum time per vehicle
	False, # don't force start cumul to zero since we are giving TW to start nodes
	time)
	time_dimension = routing.GetDimensionOrDie(time)
	# Add time window constraints for each location except depot
	# and 'copy' the slack var in the solution object (aka Assignment) to print it
	for location_idx, time_window in enumerate(data['time_windows']):
	if location_idx == 0:
	continue
	index = manager.NodeToIndex(location_idx)
	time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
	routing.AddToAssignment(time_dimension.SlackVar(index))
	# Add time window constraints for each vehicle start node
	# and 'copy' the slack var in the solution object (aka Assignment) to print it
	for vehicle_id in range(data['num_vehicles']):
	index = routing.Start(vehicle_id)
	time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],
	data['time_windows'][0][1])
	routing.AddToAssignment(time_dimension.SlackVar(index))
	# Warning: Slack var is not defined for vehicle's end node
	#routing.AddToAssignment(time_dimension.SlackVar(self.routing.End(vehicle_id)))


	###########
	# Printer #
	###########
	def print_solution(data, manager, routing, assignment): # pylint:disable=too-many-locals
	"""Prints assignment on console"""
	print(f'Objective: {assignment.ObjectiveValue()}')
	total_distance = 0
	total_load = 0
	total_time = 0
	capacity_dimension = routing.GetDimensionOrDie('Capacity')
	time_dimension = routing.GetDimensionOrDie('Time')
	dropped = []
	for order in range(6, routing.nodes()):
	index = manager.NodeToIndex(order)
	if assignment.Value(routing.NextVar(index)) == index:
	dropped.append(order)
	print(f'dropped orders: {dropped}')
	for reload in range(1, 6):
	index = manager.NodeToIndex(reload)
	if assignment.Value(routing.NextVar(index)) == index:
	dropped.append(reload)
	print(f'dropped reload stations: {dropped}')

	for vehicle_id in range(data['num_vehicles']):
	index = routing.Start(vehicle_id)
	plan_output = f'Route for vehicle {vehicle_id}:\n'
	distance = 0
	while not routing.IsEnd(index):
	load_var = capacity_dimension.CumulVar(index)
	time_var = time_dimension.CumulVar(index)
	plan_output += ' {0} Load({1}) Time({2},{3}) ->'.format(
	manager.IndexToNode(index),
	assignment.Value(load_var),
	assignment.Min(time_var), assignment.Max(time_var))
	previous_index = index
	index = assignment.Value(routing.NextVar(index))
	distance += routing.GetArcCostForVehicle(previous_index, index,
	vehicle_id)
	load_var = capacity_dimension.CumulVar(index)
	time_var = time_dimension.CumulVar(index)
	plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
	manager.IndexToNode(index),
	assignment.Value(load_var),
	assignment.Min(time_var), assignment.Max(time_var))
	plan_output += f'Distance of the route: {distance}m\n'
	plan_output += f'Load of the route: {assignment.Value(load_var)}\n'
	plan_output += f'Time of the route: {assignment.Value(time_var)}min\n'
	print(plan_output)
	total_distance += distance
	total_load += assignment.Value(load_var)
	total_time += assignment.Value(time_var)
	print('Total Distance of all routes: {}m'.format(total_distance))
	print('Total Load of all routes: {}'.format(total_load))
	print('Total Time of all routes: {}min'.format(total_time))


	########
	# Main #
	########
	def main():
	"""Entry point of the program"""
	# Instantiate the data problem.
	data = create_data_model()

	# Create the routing index manager
	manager = pywrapcp.RoutingIndexManager(data['num_locations'],
	data['num_vehicles'], data['depot'])

	# Create Routing Model
	routing = pywrapcp.RoutingModel(manager)

	# Define weight of each edge
	distance_evaluator_index = routing.RegisterTransitCallback(
	partial(create_distance_evaluator(data), manager))
	routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)

	# Add Distance constraint to minimize the longuest route
	add_distance_dimension(routing, manager, data, distance_evaluator_index)

	# Add Capacity constraint
	demand_evaluator_index = routing.RegisterUnaryTransitCallback(
	partial(create_demand_evaluator(data), manager))
	add_capacity_constraints(routing, manager, data, demand_evaluator_index)

	# Add Time Window constraint
	time_evaluator_index = routing.RegisterTransitCallback(
	partial(create_time_evaluator(data), manager))
	add_time_window_constraints(routing, manager, data, time_evaluator_index)

	# Setting first solution heuristic (cheapest addition).
	search_parameters = pywrapcp.DefaultRoutingSearchParameters()
	search_parameters.first_solution_strategy = (
	routing_enums_pb2.FirstSolutionStrategy.AUTOMATIC) # pylint: disable=no-member
	search_parameters.local_search_metaheuristic = (
	routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
	search_parameters.time_limit.FromSeconds(60)

	# Solve the problem.
	solution = routing.SolveWithParameters(search_parameters)
	if solution:
	print_solution(data, manager, routing, solution)
	else:
	print("No solution found !")


	if __name__ == '__main__':
	main()