作为OR-Tools库的新手，我无法为我的要求修改现有代码。我需要在收货和送货时增加容量限制，即一个人将按照收货和送货算法中提到的方式运送物品，但是会有一个限制，他可以容纳多少物品。我尝试将代码用于载货和交付代码的容量限制，但没有成功。这是示例代码：
from __future__ import print_function
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['num_vehicles'] = 4
data['depot'] = 0
data['vehicle_capacities'] = [15,15,15,15]
data['demands'] = [0, 1, 1, 3, 6, 3, 6, 8, 8, 1, 2, 1, 2, 6, 6, 8, 8]
return data
def print_solution(data, manager, routing, assignment):
"""Prints assignment on console."""
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 = assignment.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.
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)
# Define cost of each arc.
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)
# 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')
# Add 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)
# 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))
# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION)
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print(assignment)
# Print solution on console.
if assignment:
print("1")
print_solution(data, manager, routing, assignment)
if __name__ == '__main__':
main()
答案 0 :(得分：1)
没有解决方案，因为总需求量大于车辆总容量。需求是70，容量是60。
答案 1 :(得分：1)
对于无法解决此问题的任何人，我将把它留在这里，而不是创建一个新问题并自己编写解决方案。您可能错过了此答案中的某些内容。
什么是 demands 数组？
这是车辆前往该地点时必须携带的单位数量。
什么是 vehicle_capacities 数组？
它是特定车辆可以携带的单位的最大数量。
但是，在这种情况下，我们的“车辆”在到达交货地点时不会增加额外的重量/数量（以单位为单位）。相反，它会释放它从取货地点携带的数量（以单位为单位）。
因此，我们的需求数组将相应更改。
考虑到这是我们的 pickup_deliveries 数组
data['pickups_deliveries'] = [
[1, 6],
[2, 10],
[4, 3],
[5, 9],
[7, 8],
[15, 11],
[13, 12],
[16, 14],
]
我们的需求数组应该类似于：
data['demands'] = [0, 1, 2, -6, 6, 10, -1, 8, -8, -10, -2, -9, -4, 4, -13, 9, 13]
每个交货地点将释放与我们从提货地点提货的数量相同的数量。
当使用具有容量限制的取货和送货或将其与任何其他约束（如时间窗口甚至多个起点）结合使用时，如果我们首先指定仓库，然后取货 1，然后取货 1，取货 2，然后取货 2，这会使工作变得更加容易， 等等。 例如：
data['num_vehicles'] = 4
# put all vehicles at the start of your 'addresses' array (i.e. they will be the first rows in the distance / time matrices)
data['starts'] = [0, 1, 2, 3]
data['ends'] = [0, 1, 2, 3]
# then simply, from the next 2 indices start defining the pickups and drops
# i.e. 4 is pickup1 5 is drop1, 6 is pickup2 7 is drop2, etc. (this also makes it easier for dynamic input)
# i.e. if num_vehicles is 3 -> next 2 -> 3,4 (since index starts from 0)
data['pickups_deliveries'] = [
[4, 5],
[6, 7],
[8, 9],
[10, 11],
[12, 13],
[14, 15]
]
# then 0 to num_vehicle values will be 0 (since depots won't have weight in normal condition unless you have something...)
# and the rest of the numbers will be pairs of pickups and drop weights i.e. what is picked up is dropped so (num,-num) for 1 pickup-drop pair etc.
data['demands'] = [0, 0, 0, 0, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1]
data['vehicle_capacities'] = [1, 1, 1, 1] # this depends on your problem.
# here I have considered that a vehicle can only carry one thing at a time