python - Google OR-TOOLS VRP 以前的 OR-TOOLS 分配问题的问题
问题描述
我试图解决一个两步问题，在第一个问题中，我运行一个分配模型，该模型计算优化节点之间的提货和交付弧的最佳选项，因为并非所有车辆都可以运输相同的产品和其他复杂问题. 第一个模型的结果是在第二个 VRP 模型中作为数据['pickups_deliveries'] 的输入的弧。下一个代码是一个简单的示例，其中代码有效，但节点不能同时是交付节点和取货节点。这是我需要解决的。
"""Capacited Vehicles Routing Problem (CVRP)."""
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp
def create_data_model():
"""Stores the data for the problem."""
data = {}
data['distance_matrix'] = [
[
0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,
468, 776, 662
],
[
548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,
1016, 868, 1210
],
[
776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,
1130, 788, 1552, 754
],
[
696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,
1164, 560, 1358
],
[
582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,
1050, 674, 1244
],
[
274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,
514, 1050, 708
],
[
502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,
514, 1278, 480
],
[
194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,
662, 742, 856
],
[
308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,
320, 1084, 514
],
[
194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,
274, 810, 468
],
[
536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,
730, 388, 1152, 354
],
[
502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,
308, 650, 274, 844
],
[
388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,
536, 388, 730
],
[
354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,
342, 422, 536
],
[
468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,
342, 0, 764, 194
],
[
776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,
388, 422, 764, 0, 798
],
[
662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,
536, 194, 798, 0
],
]
data['pickups_deliveries'] = [
[1, 6],
[2, 10],
[4, 3],
[5, 9],
[7, 8],
[15, 11],
[13, 12],
[16, 14]
]
data['demands'] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
data['vehicle_capacities'] = [1, 1, 1, 1, 1, 1, 1, 1, 1]
data['num_vehicles'] = 9
data['depot'] = 0
return data
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 = 'Route for vehicle {}:\n'.format(vehicle_id)
route_distance = 0
route_load = 0
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
route_load += data['demands'][node_index]
plan_output += ' {0} Load({1}) -> '.format(node_index, route_load)
previous_index = index
index = solution.Value(routing.NextVar(index))
route_distance += routing.GetArcCostForVehicle(
previous_index, index, vehicle_id)
plan_output += ' {0} Load({1})\n'.format(manager.IndexToNode(index),
route_load)
plan_output += 'Distance of the route: {}m\n'.format(route_distance)
plan_output += 'Load of the route: {}\n'.format(route_load)
print(plan_output)
total_distance += route_distance
total_load += route_load
print('Total distance of all routes: {}m'.format(total_distance))
print('Total load of all routes: {}'.format(total_load))
def main():
"""Entry point of the program."""
# Instantiate the data problem.
# [START data]
data = create_data_model()
# [END data]
# Create the routing index manager.
# [START index_manager]
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
data['num_vehicles'], data['depot'])
# [END index_manager]
# Create Routing Model.
# [START routing_model]
routing = pywrapcp.RoutingModel(manager)
# [END routing_model]
# Define cost of each arc.
# [START arc_cost]
def distance_callback(from_index, to_index):
"""Returns the manhattan 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)
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# [END arc_cost]
# Add Distance constraint.
# [START distance_constraint]
dimension_name = 'Distance'
routing.AddDimension(
transit_callback_index,
0, # no slack
3000, # vehicle maximum travel distance
True, # start cumul to zero
dimension_name)
distance_dimension = routing.GetDimensionOrDie(dimension_name)
distance_dimension.SetGlobalSpanCostCoefficient(100)
# [END distance_constraint]
# Define Transportation Requests.
# [START pickup_delivery_constraint]
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))
routing.SetPickupAndDeliveryPolicyOfAllVehicles(
pywrapcp.RoutingModel.PICKUP_AND_DELIVERY_FIFO)
# [END pickup_delivery_constraint]
# 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()
Route for vehicle 0:
0 Load(1) -> 4 Load(2) -> 3 Load(3) -> 5 Load(4) -> 9 Load(5) -> 0 Load(5)
Distance of the route: 1780m
Load of the route: 5
Route for vehicle 1:
0 Load(1) -> 2 Load(2) -> 10 Load(3) -> 0 Load(3)
Distance of the route: 1712m
Load of the route: 3
Route for vehicle 2:
0 Load(1) -> 0 Load(1)
Distance of the route: 0m
Load of the route: 1
Route for vehicle 3:
0 Load(1) -> 0 Load(1)
Distance of the route: 0m
Load of the route: 1
Route for vehicle 4:
0 Load(1) -> 0 Load(1)
Distance of the route: 0m
Load of the route: 1
Route for vehicle 5:
0 Load(1) -> 0 Load(1)
Distance of the route: 0m
Load of the route: 1
Route for vehicle 6:
0 Load(1) -> 1 Load(2) -> 6 Load(3) -> 0 Load(3)
Distance of the route: 1780m
Load of the route: 3
Route for vehicle 7:
0 Load(1) -> 7 Load(2) -> 8 Load(3) -> 16 Load(4) -> 14 Load(5) -> 0 Load(5)
Distance of the route: 1712m
Load of the route: 5
Route for vehicle 8:
0 Load(1) -> 13 Load(2) -> 12 Load(3) -> 15 Load(4) -> 11 Load(5) -> 0 Load(5)
Distance of the route: 1712m
Load of the route: 5
此代码适用于简单的图形分配，其中每个拾取节点只是一个拾取节点，每个交付节点只是一个交付节点。但是如果想要一个节点被拾取和交付，我想我可以将它添加为另一个图，例如，使节点 14，前交付节点，也是弧 [14,13] 的一个拾取节点。我以为我可以通过将其添加到数据 ['pickups_deliveries'] 来强制一辆车行驶 16->14->13->12，但 python 崩溃并停止工作。
data['pickups_deliveries'] = [
[1, 6],
[2, 10],
[4, 3],
[5, 9],
[7, 8],
[15, 11],
[13, 12],
[16, 14],
[14,13] ## Added
]
我想要做的主要是能够添加图表，其中一个节点可以是一个拾取节点，而另一个节点可以是一个交付节点。
感谢和抱歉的扩展。
解决方案
您必须复制节点并相应地调整您的传输回调。
然后，您可以在后处理解决方案分配时合并节点 ID。
另一种方法是破解传输回调以在那里进行映射，因此您必须重新计算新的传输矩阵。
例如，为节点 13 和 14 创建一个重复的节点 17 和 18。这样您就可以添加新的 P&D 对[18, 17]
在您的中转回调中：
def distance_callback(from_index, to_index):
"""Returns the manhattan distance between the two nodes."""
# Convert from routing variable Index to distance matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
# rebind 17 or 18 to 13 or 14 respectively
if from_node in [17, 18]:
from_node = from_node - 4
to_node = manager.IndexToNode(to_index)
# rebind 17 or 18 to 13 or 14 respectively
if to_node in [17, 18]:
to_node = to_node - 4
return data['distance_matrix'][from_node][to_node]
也改变
# [START index_manager]
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']) + 2,
data['num_vehicles'], data['depot'])
# [END index_manager]
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