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作为 OR-Tools 库的新手，我无法根据我的要求修改现有代码。我有一个要求增加取货和交付的容量限制，即一个人将交付物品，如取货和交付算法中提到的那样，但会有一个限制，他可以容纳多少物品。我尝试使用带有取货和交付代码的容量限制代码，但没有成功。这是一个示例代码：

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()
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2 回答 2

2

没有解决方案，因为总需求大于总车辆容量。需求为 70 容量为 60。

于 2019-12-30T19:02:22.673 回答
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。您可能错过了这个答案中的某些内容。

让我们先了解一下容量限制是如何用简单的英语进行的

什么是demands数组？

它是车辆前往该地点时必须携带的数量单位。

什么是vehicle_capacities数组？

它是特定车辆可以携带的最大单位数量。

现在继续取货和送货

然而，在这种情况下，我们的“车辆”在到达交货地点时不会携带额外的重量/数量（以单位为单位）。相反，它将释放它从取货地点携带的数量（以单位为单位）。

所以，我们的需求数组会相应地改变。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我们从取货地点提取的相同数量的数量。

旁注（与问题无关，但可能对您有所帮助）

当使用带有容量限制的提货和交付或将其与任何其他限制（例如时间窗口甚至多个开始结束）结合使用时，如果我们先指定仓库然后指定提货1 然后下落1、然后提货2 然后下落2 等，这会使工作变得容易得多。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
于 2021-06-30T12:50:24.193 回答