I use or-tools to solve problems such as picking up products from EC warehouses.
What I want to do is very similar to the cvrp_reload sample. https://github.com/google/or-tools/blob/stable/ortools/constraint_solver/samples/cvrp_reload.py The difference from the sample is that instead of bringing the load back to the depot, the picked up load is placed at a specific delivery point.
I changed the source of cvrp_reload while looking at the Pickup & Delivery sample in the guide. (Assuming that Node.1 to 5 are the delivery points)
data['pickups_deliveries'] = [
[6, 1], [7, 1],
[8, 1], [9, 1],
[10, 2], [11, 2],
[12, 2], [13, 3],
[14, 3], [15, 3],
[16, 4], [17, 4],
[18, 4], [19, 5],
[20, 5], [21, 5]
]
data['demands'] = \
[0, # depot
-_capacity, # unload depot_first
-_capacity, # unload depot_second
-_capacity, # unload depot_third
-_capacity, # unload depot_fourth
-_capacity, # unload depot_fifth
4, 4, # 1, 2 changed from original
4, 4, # 3, 4 changed from original
4, 4, # 5, 6 changed from original
4, 4, # 7, 8 changed from original
4, 4, # 9,10 changed from original
4, 4, # 11,12 changed from original
4, 4, # 13, 14 changed from original
4, 4] # 15, 16 changed from original
data['time_windows'] = \
[(0, 0), # depot
(0, 1500), # unload depot_first. changed from original
(0, 1500), # unload depot_second. changed from original
(0, 1500), # unload depot_third. changed from original
(0, 1500), # unload depot_fourth. changed from original
(0, 1500), # unload depot_fifth. changed from original
(0, 750), (0, 750), # 1, 2 changed from original
(0, 750), (0, 750), # 3, 4 changed from original
(0, 750), (0, 750), # 5, 6 changed from original
(0, 750), (0, 750), # 7, 8 changed from original
(0, 750), (0, 750), # 9, 10 changed from original
(750, 1500), (750, 1500), # 11, 12 changed from original
(750, 1500), (750, 1500), # 13, 14 changed from original
(750, 1500), (750, 1500)] # 15, 16 changed from original
for request in data['pickups_deliveries']:
pickup_index = manager.NodeToIndex(request[0])
delivery_index = manager.NodeToIndex(request[1])
routing.AddPickupAndDelivery(pickup_index, delivery_index)
routing.solver().Add(
routing.VehicleVar(pickup_index) == routing.VehicleVar(
delivery_index))
routing.solver().Add(
distance_dimension.CumulVar(pickup_index) <=
distance_dimension.CumulVar(delivery_index))
Running this code will drop all nodes. However, limiting the list of P&Ds to just a few gives a solution.
data['pickups_deliveries'] = [
[6, 1], [7, 1],
[8, 1], [9, 1],
[10, 2], [11, 2],
# [12, 2], [13, 3],
# [14, 3], [15, 3],
# [16, 4], [17, 4],
# [18, 4], [19, 5],
# [20, 5], [21, 5]
]
Please give me a hint where my method is wrong.
This is the entire code. Thanks for reading my question.
from functools import partial
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
# Locations in block unit
_locations = [
(4, 4), # depot
(4, 4), # unload depot_first
(4, 4), # unload depot_second
(4, 4), # unload depot_third
(4, 4), # unload depot_fourth
(4, 4), # unload depot_fifth
(2, 0),
(8, 0), # locations to visit
(0, 1),
(1, 1),
(5, 2),
(7, 2),
(3, 3),
(6, 3),
(5, 5),
(8, 5),
(1, 6),
(2, 6),
(3, 7),
(6, 7),
(0, 8),
(7, 8)
]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
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
4, 4, # 1, 2 changed from original
4, 4, # 3, 4 changed from original
4, 4, # 5, 6 changed from original
4, 4, # 7, 8 changed from original
4, 4, # 9,10 changed from original
4, 4, # 11,12 changed from original
4, 4, # 13, 14 changed from original
4, 4] # 15, 16 changed from original
data['time_per_demand_unit'] = 5 # 5 minutes/unit
data['time_windows'] = \
[(0, 0), # depot
(0, 1500), # unload depot_first. changed from original
(0, 1500), # unload depot_second. changed from original
(0, 1500), # unload depot_third. changed from original
(0, 1500), # unload depot_fourth. changed from original
(0, 1500), # unload depot_fifth. changed from original
(0, 750), (0, 750), # 1, 2 changed from original
(0, 750), (0, 750), # 3, 4 changed from original
(0, 750), (0, 750), # 5, 6 changed from original
(0, 750), (0, 750), # 7, 8 changed from original
(0, 750), (0, 750), # 9, 10 changed from original
(750, 1500), (750, 1500), # 11, 12 changed from original
(750, 1500), (750, 1500), # 13, 14 changed from original
(750, 1500), (750, 1500)] # 15, 16 changed from original
#added p&d list.##############################
data['pickups_deliveries'] = [
[6, 1], [7, 1],
[8, 1], [9, 1],
[10, 2], [11, 2],
[12, 2], [13, 3],
[14, 3], [15, 3],
[16, 4], [17, 4],
[18, 4], [19, 5],
[20, 5], [21, 5]
]
##############################################
data['num_vehicles'] = 3
data['vehicle_capacity'] = _capacity
data['vehicle_max_distance'] = 10_000
data['vehicle_max_time'] = 1_500
data[
'vehicle_speed'] = 5 * 60 / 3.6 # Travel speed: 5km/h to convert in m/min
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
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] = (manhattan_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)
# Define Transportation Requests. #######################################
for request in data['pickups_deliveries']:
pickup_index = manager.NodeToIndex(request[0])
delivery_index = manager.NodeToIndex(request[1])
routing.AddPickupAndDelivery(pickup_index, delivery_index)
routing.solver().Add(
routing.VehicleVar(pickup_index) == routing.VehicleVar(
delivery_index))
routing.solver().Add(
distance_dimension.CumulVar(pickup_index) <=
distance_dimension.CumulVar(delivery_index))
##########################################################################
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 = manhattan_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.PATH_CHEAPEST_ARC) # pylint: disable=no-member
search_parameters.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
search_parameters.time_limit.FromSeconds(3)
# 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()
I got advice comments from Mizux and fixed the code.
I have a pair of Pickup and Delivery points and map the same Delivery points to the same coordinates. Thanks to Mizux for the great advice.
from functools import partial
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
# Locations in block unit
_locations = [
(4, 4), # depot
(7, 3), (5, 5), #1,2 pickup_loc, delivery_loc
(6, 6), (5, 5), #3,4 pickup_loc, delivery_loc
(3, 4), (5, 5), #5,6 pickup_loc, delivery_loc
(3, 5), (3, 3), #7,8 pickup_loc, delivery_loc
(2, 7), (3, 3), #9,10 pickup_loc, delivery_loc
(6, 3), (3, 3), #11,12 pickup_loc, delivery_loc
(8, 5), (7, 7), #13,14 pickup_loc, delivery_loc
(2, 6), (7, 7), #15,16 pickup_loc, delivery_loc
(6, 7), (7, 7), #17,18 pickup_loc, delivery_loc
(7, 8), (8, 8), #19,20 pickup_loc, delivery_loc
(5, 7), (8, 8), #20,21 pickup_loc, delivery_loc
]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
data['num_locations'] = len(data['locations'])
data['demands'] = \
[0, # depot
4, -4, # 1, 2 load, unload
4, -4, # 3, 4 load, unload
4, -4, # 5, 6 load, unload
4, -4, # 7, 8 load, unload
4, -4, # 9, 10 load, unload
4, -4, # 11, 12 load, unload
4, -4, # 13, 14 load, unload
4, -4, # 15, 16 load, unload
4, -4, # 17, 18 load, unload
4, -4, # 19, 20 load, unload
4, -4] # 21, 22 load, unload
data['time_per_demand_unit'] = 5 # 5 minutes/unit
data['time_windows'] = \
[(0, 0), # depot
(0, 1500), (0, 750), # 1, 2 specify delivery time
(0, 1500), (0, 750), # 3, 4 specify delivery time
(0, 1500), (0, 750), # 5, 6 specify delivery time
(0, 1500), (0, 750), # 7, 8 specify delivery time
(0, 1500), (0, 750), # 9, 10 specify delivery time
(0, 1500), (0, 750), # 11, 12 specify delivery time
(0, 1500), (750, 1500), # 13, 14 specify delivery time
(0, 1500), (750, 1500), # 15, 16 specify delivery time
(0, 1500), (750, 1500), # 17, 18 specify delivery time
(0, 1500), (750, 1500), # 19, 20 specify delivery time
(0, 1500), (750, 1500)] # 21, 22 specify delivery time
#added p&d list.##############################
data['pickups_deliveries'] = [
[1, 2], [3, 4],
[5, 6], [7, 8],
[9, 10], [11, 12],
[13, 14], [15, 16],
[17, 18], [19, 20],
[21, 22]
]
##############################################
data['num_vehicles'] = 3
data['vehicle_capacity'] = _capacity
data['vehicle_max_distance'] = 10_000
data['vehicle_max_time'] = 1_500
data[
'vehicle_speed'] = 5 * 60 / 3.6 # Travel speed: 5km/h to convert in m/min
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
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
# commented out for unused duplicate depot######################
# 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] = (manhattan_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)
# Define Transportation Requests. #######################################
for request in data['pickups_deliveries']:
pickup_index = manager.NodeToIndex(request[0])
delivery_index = manager.NodeToIndex(request[1])
routing.AddPickupAndDelivery(pickup_index, delivery_index)
routing.solver().Add(
routing.VehicleVar(pickup_index) == routing.VehicleVar(
delivery_index))
routing.solver().Add(
distance_dimension.CumulVar(pickup_index) <=
distance_dimension.CumulVar(delivery_index))
##########################################################################
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. (Pickup & Delivery node)
for node in range(1, 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 = manhattan_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.PATH_CHEAPEST_ARC) # pylint: disable=no-member
search_parameters.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
search_parameters.time_limit.FromSeconds(3)
# 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()