def main() -> None:
"""
Entry point of the program.
"""
# Parse command line arguments
args = parse_args()
# Instantiate the data problem
data = load_data_model(args.path)
# Create the Routing Index Manager and Routing Model
manager = RoutingIndexManager(data["num_locations"], data["num_vehicles"],
data["depot"])
routing = RoutingModel(manager)
# Define weights of edges
weight_callback_index = routing.RegisterTransitCallback(
create_weight_callback(manager, data))
routing.SetArcCostEvaluatorOfAllVehicles(weight_callback_index)
# Add capacity constraints
demand_callback = create_demand_callback(manager, data)
demand_callback_index = routing.RegisterUnaryTransitCallback(
demand_callback)
add_capacity_constraints(routing, manager, data, demand_callback_index)
# Add time window constraints
time_callback_index = routing.RegisterTransitCallback(
create_time_callback(manager, data))
add_time_window_constraints(routing, manager, data, time_callback_index)
# Set first solution heuristic (cheapest addition)
search_params = DefaultRoutingSearchParameters()
search_params.first_solution_strategy = FirstSolutionStrategy.PATH_CHEAPEST_ARC
if args.gls:
search_params.local_search_metaheuristic = (
LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
# NOTE: Since Guided Local Search could take a very long time, we set a
# reasonable time limit
search_params.time_limit.seconds = 30
if args.verbose:
search_params.log_search = True
# Solve the problem
assignment = routing.SolveWithParameters(search_params)
if not assignment:
print("No solution found.")
return
# Print the solution
print_solution(data, routing, manager, assignment)
# Export network and route graphs
if args.export_network_graph:
draw_network_graph(args.export_network_graph, data)
if args.export_route_graph:
draw_route_graph(args.export_route_graph, data, routing, manager,
assignment)
def solve(self, indices_to_visit: List[int] = None) -> Dict[str, Any]:
"""Finds the optimal order of facilities to minimize distance.
Parameters
----------
indices_to_visit : List[int]
The list of indices corresponding to the desired facilities to visit
Returns
-------
Dict[str, Any]
Soltution dictionary with keys:
- 'objective', set to objective value (minified distance)
- 'order', instructions for the order of facilities to visit
"""
if indices_to_visit is None:
indices_to_visit = list(range(len(self.matrix)))
# make sure home location is in the listed, and that the list is sorted
if self.home_index not in indices_to_visit:
indices_to_visit.append(self.home_index)
indices_to_visit.sort()
data = self._create_data_model(indices_to_visit)
# create routing index manager
manager = RoutingIndexManager(len(data['distance_matrix']),
data['num_vehicles'], data['home'])
# create routing model
routing = RoutingModel(manager)
def distance_callback(from_index, to_index):
# returns distance between two nodes
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
dist = data['distance_matrix'][from_node][to_node]
return dist
transit_callback_index = routing.RegisterTransitCallback(
distance_callback)
# define cost of each arc
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# set first solution heuristic
search_params = pywrapcp.DefaultRoutingSearchParameters()
search_params.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
# solve problem
assignment = routing.SolveWithParameters(search_params)
return self._extract_solution(manager, routing, assignment,
indices_to_visit)
def _solve(self, routing: RoutingModel,
search_parameters: RoutingSearchParameters,
initial_routes) -> Assignment:
if initial_routes is None:
solution = routing.SolveWithParameters(search_parameters)
else:
routing.CloseModelWithParameters(search_parameters)
initial_solution = routing.ReadAssignmentFromRoutes(
initial_routes, True)
solution = routing.SolveFromAssignmentWithParameters(
initial_solution, search_parameters)
return solution
def main():
"""
Entry point of the program.
"""
# Parse command line arguments
args = parse_args()
#Crafted data :
coordinates = [[0.848307, 0.32357132], [0.53917086, 0.862489],
[0.28499496, 0.7470896], [0.66891015, 0.07388902],
[0.6514969, 0.4077109], [0.782356, 0.28167558],
[0.2601446, 0.9196638], [0.3402847, 0.62080956],
[0.3920853, 0.8653455], [0.79114413, 0.09456837],
[0.11343169, 0.73570406]]
Distance_matrix = list()
for j in range(len(coordinates)):
l = list()
for i in range(len(coordinates)):
l.append(distance(coordinates[j], coordinates[i]))
Distance_matrix.append(l)
data = dict()
data['weights'] = Distance_matrix
data['service_times'] = [0, 1, 0.5, 1, 1, 0.5, 2, 2, 2, 0.5, 2]
data['demands'] = [
0, 0.25, 0.2, 0.1, 0.2, 0.35, 0.15, 0.3, 0.15, 0.15, 0.3
]
data['time_windows'] = [[0, 9], [0, 5], [5, 9], [0, 9], [0, 9], [0, 9],
[0, 5], [5, 9], [5, 9], [0, 5], [0, 9]]
data['vehicle_capacities'] = [1, 1, 1]
data['depot'] = 0
data['num_locations'] = 11
data['num_vehicles'] = 3
# Instantiate the data problem
#data = load_data_model(args.path)
# Create the Routing Index Manager and Routing Model
manager = RoutingIndexManager(data['num_locations'], data['num_vehicles'],
data['depot'])
routing = RoutingModel(manager)
# Define weight of each edge
weight_callback_index = routing.RegisterTransitCallback(
create_weight_callback(manager, data))
routing.SetArcCostEvaluatorOfAllVehicles(weight_callback_index)
# Add capacity constraints
demand_callback = create_demand_callback(manager, data)
demand_callback_index = routing.RegisterUnaryTransitCallback(
demand_callback)
add_capacity_constraints(routing, manager, data, demand_callback_index)
# Add time window constraints
time_callback_index = routing.RegisterTransitCallback(
create_time_callback(manager, data))
add_time_window_constraints(routing, manager, data, time_callback_index)
# Set first solution heuristic (cheapest addition)
search_params = DefaultRoutingSearchParameters()
# pylint: disable=no-member
search_params.first_solution_strategy = FirstSolutionStrategy.PATH_CHEAPEST_ARC
if args.gls:
# pylint: disable=no-member
search_params.local_search_metaheuristic = LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
# NOTE: Since Guided Local Search could take a very long time, we set a reasonable time
# limit
search_params.time_limit.seconds = 30
if args.verbose:
search_params.log_search = True
# Solve the problem
assignment = routing.SolveWithParameters(search_params)
if not assignment:
print('No solution found.')
return
# Print the solution
print_solution(Distance_matrix, data, routing, manager, assignment)
# Draw network and route graphs
"""if args.graph:
For other options, see the link below
https://developers.google.com/optimization/routing/routing_options#first_sol_options
"""
search_params = DefaultRoutingSearchParameters()
search_params.first_solution_strategy = FirstSolutionStrategy.PATH_CHEAPEST_ARC
if args.gls:
search_params.local_search_metaheuristic = LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
# NOTE: Since Guided Local Search could take a very long time, we set a reasonable time limit
search_params.time_limit.seconds = 30
if args.verbose:
search_params.log_search = True
# Solve the problem
assignment = routing.SolveWithParameters(search_params)
if routing.status() == 1 and assignment:
t2 = time()
# Print the solution
best_score = None
best_score = print_solution(data, routing, manager, assignment)
print(f'Measured Execute time: {round(t2-t1, 6)}sec')
if args.write_csv is not None:
with open(args.write_csv, 'a') as f:
f.write(f'{t2-t1},{best_score/1e4}\n')
# Draw network and route graphs
if args.graph:
from visualize import draw_network_graph, draw_route_graph
# draw_network_graph(data)
