python cvrp问题代码

Python cvrp问题代码是用Python编写的，用于解决车辆路径问题（CVRP）的问题。车辆路径问题是指需要确定一组车辆在给定需求点集合中的路径，以满足一定的限制条件，例如每个路径的总长度不超过某个值，每个路径的需求总和不超过某个值等。

以下是一个简单的Python cvrp问题代码示例：

import numpy as np
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

# 创建数据模型
def create_data_model():
    data = {}
    # 车辆数
    data['num_vehicles'] = 2
    # 需求点集合
    data['demands'] = [0, 1, 1, 2, 1, 2, 1]
    # 距离矩阵
    data['distances'] = [
        [0, 1, 1, 2, 1, 2, 1],
        [1, 0, 2, 1, 2, 1, 1],
        [1, 2, 0, 1, 2, 1, 2],
        [2, 1, 1, 0, 1, 2, 2],
        [1, 2, 2, 1, 0, 1, 2],
        [2, 1, 1, 2, 1, 0, 1],
        [1, 1, 2, 2, 2, 1, 0]]
    return data

# 解决CVRP问题
def solve_cvrp():
    data = create_data_model()

    manager = pywrapcp.RoutingIndexManager(len(data['distances']),
                                           data['num_vehicles'], [0], [0])
    routing = pywrapcp.RoutingModel(manager)

    def distance_callback(from_index, to_index):
        from_node = manager.IndexToNode(from_index)
        to_node = manager.IndexToNode(to_index)
        return data['distances'][from_node][to_node]

    transit_callback_index = routing.RegisterTransitCallback(distance_callback)

    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
    add_capacity_constraints(data, manager, routing, transit_callback_index)

    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.local_search_metaheuristic = (
        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
    search_parameters.time_limit.seconds = 30

    solution = routing.SolveWithParameters(search_parameters)

    if solution:
        print_solution(data, manager, routing, solution)

# 添加容量约束
def add_capacity_constraints(data, manager, routing, transit_callback_index):
    capacity = 'Capacity'
    routing.AddDimension(
        transit_callback_index,
        0,  # no slack
        3,  # vehicle maximum capacity
        # 车辆初始容量
        capacity)
    capacity_dimension = routing.GetDimensionOrDie(capacity)
    for vehicle_id in range(data['num_vehicles']):
        index = manager.NodeToIndex(vehicle_id)
        capacity_dimension.SetCumulVarSoftUpperBound(index, 3, 0)

# 打印路径和总距离
def print_solution(data, manager, routing, solution):
    total_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):
            node_index = manager.IndexToNode(index)
            plan_output += ' {} ->'.format(node_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: {}\n'.format(route_distance)
        print(plan_output)
        total_distance += route_distance
    print('Total distance of all routes: {}'.format(total_distance))

solve_cvrp()

以上代码使用Google OR-Tools库解决CVRP问题。它创建了一个数据模型，包括车辆数、需求点集合和距离矩阵。然后，它使用约束求解器和路由模型来解决问题，并添加了容量约束。最后，它输出每个车辆的路径和总距离。

请注意，这只是一个简单的示例，实际上解决CVRP问题可能需要更复杂和精细的模型和算法。