Google OR-Tools找不到VRPtw问题的解决方案

问题描述

我正在处理VRPtw问题，并努力使求解器无法找到任何数据，除了人工的小数据之外。

设置如下。
有几个仓库和地点参观。每个位置都有时间窗口。每辆车都有休息时间和工作时间。此外，这些地点还受到一些限制，只有满足需求的车辆才能参观那里。

基于此实验设置，我编写了以下代码。
如我所写，看起来它正在处理小型人工数据，但是对于真实数据，它从未找到解决方案。我尝试了5种不同的数据集。
尽管我设置了7200秒的时间限制，但以前我跑了10个小时以上，而且还是一样。
数据规模为40〜50辆车和200〜300个位置。
这段代码有问题吗？如果没有，我应该以哪种顺序更改方法（例如初始化，搜索方法等）？

（已编辑为将整数用于时间矩阵）

from dataclasses import dataclass
from typing import List,Tuple

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

# Todo: Refactor
BIG_ENOUGH = 100000000
TIME_DIMENSION = 'Time'
TIME_LIMIT = 7200


@dataclass
class DataSet:
    time_matrix: List[List[int]]
    locations_num: int
    vehicles_num: int
    vehicles_break_time_window: List[Tuple[int,int,int]]
    vehicles_work_time_windows: List[Tuple[int,int]]
    location_time_windows: List[Tuple[int,int]]
    vehicles_depots_indices: List[int]
    possible_vehicles: List[List[int]]


def execute(data: DataSet):
    manager = pywrapcp.RoutingIndexManager(data.locations_num,data.vehicles_num,data.vehicles_depots_indices,data.vehicles_depots_indices)

    routing_parameters = pywrapcp.DefaultRoutingModelParameters()
    routing_parameters.solver_parameters.trace_propagation = True
    routing_parameters.solver_parameters.trace_search = True

    routing = pywrapcp.RoutingModel(manager,routing_parameters)

    def time_callback(source_index,dest_index):
        from_node = manager.IndexToNode(source_index)
        to_node = manager.IndexToNode(dest_index)
        return data.time_matrix[from_node][to_node]

    transit_callback_index = routing.RegisterTransitCallback(time_callback)
    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
    routing.AddDimension(
        transit_callback_index,BIG_ENOUGH,False,TIME_DIMENSION)
    time_dimension = routing.GetDimensionorDie(TIME_DIMENSION)

    # set time window for locations start time
    # set condition restrictions
    possible_vehicles = data.possible_vehicles
    for location_idx,time_window in enumerate(data.location_time_windows):
        index = manager.NodetoIndex(location_idx + data.vehicles_num)
        time_dimension.CumulVar(index).SetRange(time_window[0],time_window[1])
        routing.SetAllowedVehiclesForIndex(possible_vehicles[location_idx],index)

    solver = routing.solver()
    for i in range(data.vehicles_num):
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.Start(i)))
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.End(i)))

    # set work time window for vehicles
    for vehicle_index,work_time_window in enumerate(data.vehicles_work_time_windows):
        start_index = routing.Start(vehicle_index)

        time_dimension.CumulVar(start_index).SetRange(work_time_window[0],work_time_window[0])

        end_index = routing.End(vehicle_index)
        time_dimension.CumulVar(end_index).SetRange(work_time_window[1],work_time_window[1])

    # set break time for vehicles
    node_visit_transit = {}
    for n in range(routing.Size()):
        if n >= data.locations_num:
            node_visit_transit[n] = 0
        else:
            node_visit_transit[n] = 1

    break_intervals = {}

    for v in range(data.vehicles_num):
        vehicle_break = data.vehicles_break_time_window[v]
        break_intervals[v] = [
            solver.FixedDurationIntervalVar(vehicle_break[0],vehicle_break[1],vehicle_break[2],True,'Break for vehicle {}'.format(v))
        ]
        time_dimension.SetBreakIntervalsOfVehicle(
            break_intervals[v],v,node_visit_transit
        )

    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.GREEDY_DESCENT)
    search_parameters.time_limit.seconds = TIME_LIMIT
    search_parameters.log_search = True

    solution = routing.solveWithParameters(search_parameters)
    return solution


if __name__ == '__main__':

    data = DataSet(
        time_matrix=[[0,4,5,6],[0,6,5],[1,3,4],[2,1,2,[3,0]],locations_num=6,vehicles_num=2,vehicles_depots_indices=[0,1],vehicles_work_time_windows=[(720,1080),(720,1080)],vehicles_break_time_window=[(720,720,15),15)],location_time_windows=[(735,750),(915,930),(975,990)],possible_vehicles=[[0],[1],[0],[1]]
    )
    solution = execute(data)
    if solution is not None:
        print("solution is found")

解决方法

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