Python Google ortools-带加油的mVRP

我正试图用python在几个加油站（仅用于补给）用加油解决mVRP问题。我找到了这个：

我引用了github的代码，但代码中存在一些问题

1） 我遵循了python中的方法（从上面的github代码）

（github代码）

问题

1） 如果我在

fuel\u维度的
SetValue
的for循环中使用
idx=manager.NodeToIndex（I）
，则会产生如下错误：

Process finished with exit code -1073741819 (0xC0000005)

如果我使用
I
而不是
idx
（从
NodeToIndex
），则不会发生错误。有人能解释一下吗

2） 当我打印结果时，结果（尤其是燃料维度）似乎很奇怪。比如说,

结果

8 (fuel: 0) ->  9 (fuel: 0) ->  7 (fuel: 3) ->  11 (fuel: 2) ->  6 (fuel: 4) ->  4 (fuel: 3) ->  5 (fuel: 1) ->  10 (fuel: 0) ->  3 (fuel: 2) ->  2 (fuel: 1) ->  1 (fuel: 0) -> 0

简言之，节点0是虚拟仓库，节点8是代理的指定起始节点。任务节点：[1,2,3,4,5,6,7]，站点节点：[8,9,10,11]。特别是，节点8和9是同一个站点，但我复制了它以允许重新访问加油，10和11也是如此。节点之间的距离与1相同，我假设曼哈顿距离

问题是加油站的燃油不是最大燃油（这里是4）。此外，第二个转换（9（燃料：0）->7（燃料：3））应该消耗燃料1，但它不消耗燃料

更糟糕的是转换（11（燃料：2）->6（燃料：4）->4（燃料：3））是完全错误的

上述问题的索引图如下所示：

Process finished with exit code -1073741819 (0xC0000005)


下面是整个代码：

from __future__ import print_function
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

def print_solution(data, manager, routing, solution):    
    max_route_distance = 0
    fuel_dimension = routing.GetDimensionOrDie('Fuel')

    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
        while not routing.IsEnd(index):
            fuel_var = fuel_dimension.CumulVar(index)

            plan_output += ' {} (fuel: {}) -> '.format(manager.IndexToNode(index), solution.Value(fuel_var))
            previous_index = index
            index = solution.Value(routing.NextVar(index))

            route_distance += routing.GetArcCostForVehicle(previous_index, index, vehicle_id)
        plan_output += '{}\n'.format(manager.IndexToNode(index))
        plan_output += 'Distance of the route: {}m\n'.format(route_distance)

        max_route_distance = max(route_distance, max_route_distance)


def manhattan_distance(position_1, position_2):
    return (abs(position_1[0] - position_2[0]) +
        abs(position_1[1] - position_2[1]))

def main():
    # Create the routing index manager.
    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
                                       data['num_vehicles'],
                                       data['vStart'],
                                       data['vEnd'])

    # 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)

    def fuel_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 -manhattan_distance(data['locations'][from_node], data['locations'][to_node])

    # Add Distance constraint.
    dimension_name = 'Distance'
    routing.AddDimension(
        transit_callback_index,
        0,  # no slack
        100,  # vehicle maximum travel distance
        True,  # start cumul to zero
        dimension_name)
    distance_dimension = routing.GetDimensionOrDie(dimension_name)
    distance_dimension.SetGlobalSpanCostCoefficient(100)

    fuel_callback_index = routing.RegisterTransitCallback(fuel_callback)
    routing.AddDimension(
        fuel_callback_index,
        data['MFuel'],
        data['MFuel'],
        False,
        'Fuel'
    )

    fuel_dimension = routing.GetDimensionOrDie('Fuel')

    for i in range(routing.Size()):
        if (i not in data['vStation']) or routing.IsStart(i):
            idx = manager.NodeToIndex(i)

            fuel_dimension.SlackVar(i).SetValue(0)

        routing.AddVariableMinimizedByFinalizer(fuel_dimension.CumulVar(i))

    # Setting first solution heuristic.
    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = (
    routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
    # Solve the problem.
    solution = routing.SolveWithParameters(search_parameters)
    # Print solution on console.
    if solution:
        print_solution(data, manager, routing, solution)

if __name__ == '__main__':
    main()

谢谢,

此外，下面是数据代码

import numpy as np
from scipy.spatial import distance
np.random.seed(0)

# problem settings
gridX, gridY = 10, 10
N_vehicles = 5
MFuel = 10

coord_stations = [(1,1), (1,4), (1,7), (4,2), (4,5), (4,8), (7,1), (8,4), (4,2), (7,7)]
coord_starts = [(1,1),(1,7),(4,2),(4,8),(8,4)]

coord_srfs = [(x,y) for x in range(gridX) for y in range(gridY) if (x,y) not in coord_stations]

# dummies
dummy_depot = [(0,0)]
N_dummy = 5
N_dummySta = N_dummy * len(coord_stations)


# prerequisite
MFuels = [MFuel] * N_vehicles
N_v = 1 + len(coord_srfs) + N_dummySta

# make map w/ all vertices
map = {}
idx = {}
coord2vertex = {}
for (x,y) in [(x,y) for x in range(gridX) for y in range(gridY)]:
    coord2vertex[(x,y)] = []

map[0] = dummy_depot[0]
idx['depot'] = 0

srfs_idx = []
for i in range(len(coord_srfs)):
    map[i+1] = coord_srfs[i]
    srfs_idx.append(i+1)
    coord2vertex[coord_srfs[i]].append(i+1)
idx['surfaces'] = srfs_idx

stas_idx = []
for i in range(N_dummySta):
    sta_idx = i//N_dummy
    map[i+idx['surfaces'][-1]+1] = coord_stations[sta_idx]
    stas_idx.append(i+idx['surfaces'][-1]+1)
    coord2vertex[coord_stations[sta_idx]].append(i+idx['surfaces'][-1]+1)
idx['stations'] = stas_idx

# make distance matrix w/ all vertices
dist_mat = np.zeros((N_v, N_v), dtype=int)
for i in range(N_v):
    for j in range(N_v):
        if i == 0 or j == 0:
            dist_mat[i,j] = 0
        else:
            if i == j:
                dist_mat[i,j] = 0
            else:
                dist_mat[i,j] = sum(abs(np.array(map[j])-np.array(map[i])))
distance_matrix = dist_mat.tolist()

v_starts = [coord2vertex[coord][0] for coord in coord_starts]

data = dict()
data['distance_matrix'] = distance_matrix
data['num_vehicles'] = N_vehicles
data['vStart'] = v_starts
data['vEnd'] = [0] * N_vehicles
data['MFuel'] = MFuel
data['vStation'] = idx['stations']
data['vSrf'] = idx['surfaces']
data['locations'] = list(map.values())
data['num_locations'] = len(data['locations'])

print('Problem is generated.\n# of vehicles: {} (w/ capacities: {})\n# of tasks: {} (w/ locations: {} & demands: {})\n'.format(N_vehicles, v_capas, N_tasks, coord_tasks, t_demands))

谢谢大家!
 作为盲修复（由于您没有提供
数据进行测试，因此已编辑），我将重写：

#添加燃油限制。
维度名称='燃料'
def fuel_回调（从_索引到_索引）：
“”“返回两个节点之间的距离。”“”
#将路由变量索引转换为距离矩阵节点索引。
from_node=manager.IndexToNode（from_index）
to_node=manager.IndexToNode（to_index）
返回-曼哈顿距离（数据['locations'][从节点]，数据['locations'][到节点]）
fuel\u callback\u index=routing.RegisterTransitCallback（fuel\u callback）
routing.AddDimension(
燃料指数，
数据['MFuel']，
数据['MFuel']，
假,，
维度（名称）
fuel\u dimension=routing.GetDimensionOrde（维度名称）
对于范围内的i（len（数据['distance_matrix']）：
如果（i不在数据['vStation']中）和
（i不在数据['vStart']中）和
（我不在数据['vEnd']中）：
idx=manager.NodeToIndex（i）
燃料尺寸SlackVar（idx）.设定值（0）
路由.AddVariableMinimizedByFinalizer（fuel_dimension.CumulVar（idx））

对于曼哈顿，如果您有浮点值，请注意
int（）
cast！：

def曼哈顿_距离（位置_1，位置_2）：
返回整数（abs（位置1[0]-位置2[0]）+
abs（位置_1[1]-位置_2[1]））
感谢您使用或使用工具，今天下午（巴黎时间）我将试着看一看在这里发布…IIRC NodeToIndex没有定义起点和终点，因为在VRP的情况下，车辆段内部是重复的，因此每辆车都有自己的起点/终点位置，但这意味着这不是一个双交叉点，而是一个1->N关系。你能给我们提供数据对象吗？你还必须将所有距离都设置为整数！否则，SWIG包装器会从Python浮动值转换为C++整数。@ Mizux，谢谢你的友好回复，抱歉迟到。我用我的数据代码编辑了上面的内容。
import numpy as np
from scipy.spatial import distance
np.random.seed(0)

# problem settings
gridX, gridY = 10, 10
N_vehicles = 5
MFuel = 10

coord_stations = [(1,1), (1,4), (1,7), (4,2), (4,5), (4,8), (7,1), (8,4), (4,2), (7,7)]
coord_starts = [(1,1),(1,7),(4,2),(4,8),(8,4)]

coord_srfs = [(x,y) for x in range(gridX) for y in range(gridY) if (x,y) not in coord_stations]

# dummies
dummy_depot = [(0,0)]
N_dummy = 5
N_dummySta = N_dummy * len(coord_stations)


# prerequisite
MFuels = [MFuel] * N_vehicles
N_v = 1 + len(coord_srfs) + N_dummySta

# make map w/ all vertices
map = {}
idx = {}
coord2vertex = {}
for (x,y) in [(x,y) for x in range(gridX) for y in range(gridY)]:
    coord2vertex[(x,y)] = []

map[0] = dummy_depot[0]
idx['depot'] = 0

srfs_idx = []
for i in range(len(coord_srfs)):
    map[i+1] = coord_srfs[i]
    srfs_idx.append(i+1)
    coord2vertex[coord_srfs[i]].append(i+1)
idx['surfaces'] = srfs_idx

stas_idx = []
for i in range(N_dummySta):
    sta_idx = i//N_dummy
    map[i+idx['surfaces'][-1]+1] = coord_stations[sta_idx]
    stas_idx.append(i+idx['surfaces'][-1]+1)
    coord2vertex[coord_stations[sta_idx]].append(i+idx['surfaces'][-1]+1)
idx['stations'] = stas_idx

# make distance matrix w/ all vertices
dist_mat = np.zeros((N_v, N_v), dtype=int)
for i in range(N_v):
    for j in range(N_v):
        if i == 0 or j == 0:
            dist_mat[i,j] = 0
        else:
            if i == j:
                dist_mat[i,j] = 0
            else:
                dist_mat[i,j] = sum(abs(np.array(map[j])-np.array(map[i])))
distance_matrix = dist_mat.tolist()

v_starts = [coord2vertex[coord][0] for coord in coord_starts]

data = dict()
data['distance_matrix'] = distance_matrix
data['num_vehicles'] = N_vehicles
data['vStart'] = v_starts
data['vEnd'] = [0] * N_vehicles
data['MFuel'] = MFuel
data['vStation'] = idx['stations']
data['vSrf'] = idx['surfaces']
data['locations'] = list(map.values())
data['num_locations'] = len(data['locations'])

print('Problem is generated.\n# of vehicles: {} (w/ capacities: {})\n# of tasks: {} (w/ locations: {} & demands: {})\n'.format(N_vehicles, v_capas, N_tasks, coord_tasks, t_demands))