Как добавить максимальную длину пути для самого большого маршрута в задаче маршрутизации транспортных средств - Google O ⇐ Python

[Anonymous]

Я решаю задачу маршрутизации транспортных средств с использованием инструментов OR (Python). Я просто хочу добавить ограничение, то есть верхний предел длины наибольшего маршрута. Может кто-нибудь, пожалуйста, дайте мне знать, как я могу это сделать в приведенном ниже коде.
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

def print_solution(data, manager, routing, solution):
"""Prints solution on console."""
print(f"Objective: {solution.ObjectiveValue()}")
total_distance = 0
total_load = 0
for vehicle_id in range(data["num_vehicles"]):
index = routing.Start(vehicle_id)
plan_output = f"Route for vehicle {vehicle_id}:\n"
route_distance = 0
route_load = 0
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
route_load += data["demands"][node_index]
plan_output += f" {node_index} Load({route_load}) -> "
previous_index = index
index = solution.Value(routing.NextVar(index))
route_distance += routing.GetArcCostForVehicle(
previous_index, index, vehicle_id
)
plan_output += f" {manager.IndexToNode(index)} Load({route_load})\n"
plan_output += f"Distance of the route: {route_distance}m\n"
plan_output += f"Load of the route: {route_load}\n"
print(plan_output)
total_distance += route_distance
total_load += route_load
print(f"Total distance of all routes: {total_distance}m")
print(f"Total load of all routes: {total_load}")

def main():
"""Solve the CVRP problem."""
# Instantiate the data problem.
data = create_data_model()

# Create the routing index manager.
manager = pywrapcp.RoutingIndexManager(
len(data["distance_matrix"]), data["num_vehicles"], data["depot"]
)

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

# Create and register a transit callback.
def distance_callback(from_index, to_index):
"""Returns the distance between the two nodes."""
# Convert from routing variable Index to distance matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data["distance_matrix"][from_node][to_node]

transit_callback_index = routing.RegisterTransitCallback(distance_callback)

# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)

# Add Capacity constraint.
def demand_callback(from_index):
"""Returns the demand of the node."""
# Convert from routing variable Index to demands NodeIndex.
from_node = manager.IndexToNode(from_index)
return data["demands"][from_node]

demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)
routing.AddDimensionWithVehicleCapacity(
demand_callback_index,
0, # null capacity slack
data["vehicle_capacities"], # vehicle maximum capacities
True, # start cumul to zero
"Capacity",
)

# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
)
search_parameters.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
)
search_parameters.time_limit.FromSeconds(1)

# Solve the problem.
solution = routing.SolveWithParameters(search_parameters)

# Print solution on console.
if solution:
print_solution(data, manager, routing, solution)

if __name__ == "__main__":
main()


Подробнее здесь: https://stackoverflow.com/questions/788 ... uting-prob