如何收集多个解决方案，Google OR-Tools，TSP

Question

我正在尝试为 TSP 收集不止一种解决方案。 我在 Jupyter Notebook 中运行 Python 代码，它将所有解决方案发送到终端（没有路线，只有总距离），但只有最佳解决方案被分配给“分配”。

任何帮助收集不止一种解决方案的帮助将不胜感激。 干杯。

我已经设置：search_parameters.number_of_solutions_to_collect = 10

根据此文档：“如果指定了‘解决方案’，它将包含搜索过程中找到的 k 个最佳解决方案（从最坏到最好，包括此方法返回的那个），其中 k 对应于‘search_parameters’中的‘number_of_solutions_to_collect’ '。”

#load the data into a matrix
from __future__ import print_function
import math
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, 2451, 713, 1018, 1631, 1374, 2408, 213, 2571, 875, 1420, 2145, 1972],
        [2451, 0, 1745, 1524, 831, 1240, 959, 2596, 403, 1589, 1374, 357, 579],
        [713, 1745, 0, 355, 920, 803, 1737, 851, 1858, 262, 940, 1453, 1260],
        [1018, 1524, 355, 0, 700, 862, 1395, 1123, 1584, 466, 1056, 1280, 987],
        [1631, 831, 920, 700, 0, 663, 1021, 1769, 949, 796, 879, 586, 371],
        [1374, 1240, 803, 862, 663, 0, 1681, 1551, 1765, 547, 225, 887, 999],
        [2408, 959, 1737, 1395, 1021, 1681, 0, 2493, 678, 1724, 1891, 1114, 701],
        [213, 2596, 851, 1123, 1769, 1551, 2493, 0, 2699, 1038, 1605, 2300, 2099],
        [2571, 403, 1858, 1584, 949, 1765, 678, 2699, 0, 1744, 1645, 653, 600],
        [875, 1589, 262, 466, 796, 547, 1724, 1038, 1744, 0, 679, 1272, 1162],
        [1420, 1374, 940, 1056, 879, 225, 1891, 1605, 1645, 679, 0, 1017, 1200],
        [2145, 357, 1453, 1280, 586, 887, 1114, 2300, 653, 1272, 1017, 0, 504],
        [1972, 579, 1260, 987, 371, 999, 701, 2099, 600, 1162, 1200, 504, 0],
    ]  # yapf: disable
    data['num_vehicles'] = 1
    data['depot'] = 0
    return data

def main():
    data = create_data_model()

    # Create the routing index manager.
    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']), 1, 0)

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

    distance_matrix = data['distance_matrix']

    def distance_callback(from_index, to_index):
        #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 distance_matrix[from_node][to_node]

    transit_callback_index = routing.RegisterTransitCallback(distance_callback)

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

    # Setting parameters
    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.seconds = 1
    search_parameters.number_of_solutions_to_collect = 10 #PROBLEM HERE
    search_parameters.log_search = True

    assignment = routing.SolveWithParameters(search_parameters)
#     print(assignment)

    # Print solution on console.
    if assignment:
        # Solution cost.
        print(assignment.ObjectiveValue())
        # Inspect solution.
        # Only one route here; otherwise iterate from 0 to routing.vehicles() - 1
        route_number = 0
        node = routing.Start(route_number)
        route = ''
        while not routing.IsEnd(node):
            route += str(node) + ' -> '
            node = assignment.Value(routing.NextVar(node))
        route += '0'
        print(route)
    else:
        print('No solution found.')

main()

Answer 1

在这里调整答案Google OR-Tools TSP 返回几个解决方案

以下应该工作：

from __future__ import print_function

from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver.pywrapcp import SolutionCollector


def create_data_model():
    """Stores the data for the problem."""
    data = {}
    data['distance_matrix'] = [
        [0, 2451, 713, 1018, 1631, 1374, 2408, 213, 2571, 875, 1420, 2145, 1972],
        [2451, 0, 1745, 1524, 831, 1240, 959, 2596, 403, 1589, 1374, 357, 579],
        [713, 1745, 0, 355, 920, 803, 1737, 851, 1858, 262, 940, 1453, 1260],
        [1018, 1524, 355, 0, 700, 862, 1395, 1123, 1584, 466, 1056, 1280, 987],
        [1631, 831, 920, 700, 0, 663, 1021, 1769, 949, 796, 879, 586, 371],
        [1374, 1240, 803, 862, 663, 0, 1681, 1551, 1765, 547, 225, 887, 999],
        [2408, 959, 1737, 1395, 1021, 1681, 0, 2493, 678, 1724, 1891, 1114, 701],
        [213, 2596, 851, 1123, 1769, 1551, 2493, 0, 2699, 1038, 1605, 2300, 2099],
        [2571, 403, 1858, 1584, 949, 1765, 678, 2699, 0, 1744, 1645, 653, 600],
        [875, 1589, 262, 466, 796, 547, 1724, 1038, 1744, 0, 679, 1272, 1162],
        [1420, 1374, 940, 1056, 879, 225, 1891, 1605, 1645, 679, 0, 1017, 1200],
        [2145, 357, 1453, 1280, 586, 887, 1114, 2300, 653, 1272, 1017, 0, 504],
        [1972, 579, 1260, 987, 371, 999, 701, 2099, 600, 1162, 1200, 504, 0],
    ]  # yapf: disable
    data['num_vehicles'] = 1
    data['depot'] = 0
    return data


def main():
    data = create_data_model()
    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']), 1, 0)
    routing = pywrapcp.RoutingModel(manager)

    distance_matrix = data['distance_matrix']

    def distance_callback(from_index, to_index):
        return distance_matrix[manager.IndexToNode(from_index)][manager.IndexToNode(to_index)]

    routing.SetArcCostEvaluatorOfAllVehicles(routing.RegisterTransitCallback(distance_callback))
    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.seconds = 1

    # without an initial assignment the CostVar is not available
    assignment = routing.SolveWithParameters(search_parameters)
    collector = initialize_collector(data, manager, routing)

    routing.SolveFromAssignmentWithParameters(assignment, search_parameters)
    for i in range(collector.SolutionCount()):
        print(f'================ solution: {i} ================')
        print_solution(data, manager, routing, collector.Solution(i))


def initialize_collector(data, manager, routing):
    collector: SolutionCollector = routing.solver().AllSolutionCollector()
    collector.AddObjective(routing.CostVar())

    routing.AddSearchMonitor(collector)

    for node in range(len(data['distance_matrix'])):
        collector.Add(routing.NextVar(manager.NodeToIndex(node)))

    for v in range(data['num_vehicles']):
        collector.Add(routing.NextVar(routing.Start(v)))

    return collector


def print_solution(data, manager, routing, solution):
    max_route_distance = 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
        while not routing.IsEnd(index):
            plan_output += ' {} -> '.format(manager.IndexToNode(index))
            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'.format(route_distance)
        print(plan_output)
        max_route_distance = max(route_distance, max_route_distance)
    print('Maximum of the route distances: {}m'.format(max_route_distance))


main()

请注意，在通过收集器枚举所有解决方案之前，首先需要进行初始分配，否则routing.CostVar失败。

此外，所有节点都必须手动添加到收集器，否则在调用Solution.Value(...) 。 这在 or-tools 站点上似乎没有得到很好的记录，但似乎收集器必须有权访问所有变量，这些变量稍后需要通过 collect.Solution 读取。

此外，我认为在您使用routing.SolveWithParameters代码中，有一个可选的第二个参数，称为解决方案，用于填充找到的解决方案。 那应该是一个向量，但它不适用于 python 代码。