如何在有能力的车辆路线问题中使用 for 循环Google OR 工具

如何解决如何在有能力的车辆路线问题中使用 for 循环Google OR 工具

我想找到一辆车可以拥有的最短路线距离和车辆承载能力。这是一个有能力的车辆路线问题（Google OR 工具）。我想在代码之间使用 for- 循环，以便它为保存在一个位置的所有地区提供 distance_optimization。

例如：out_1 = get_sol( "banswara",1) 仅显示“banswara”区的 fos（车辆）路线。我想编写一个 for 循环，以便输出对所有地区显示相同的结果。

我的代码：

dist = distanceMetric.get_metric('haversine')

def create_data_model(df_user,no_fos):
    """Stores the data for the problem."""
    data = {}
    data['distance_matrix'] = dist.pairwise(df_user [['lat','lon']].to_numpy())*6373
    data['num_vehicles'] = no_fos
    data['depot'] = 0
    return data


def print_solution(data,manager,routing,solution):
    """Prints solution on console."""
    max_route_distance = 0
    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        plan_output = 'Route for FOS {}:\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\n'.format(route_distance)
        print(plan_output)
        max_route_distance = max(route_distance,max_route_distance)
    print('Maximum of the route distances: {}Km'.format(max_route_distance))

def distance(x,y,data):
    dis = data['distance_matrix'][x][y]
    return dis

def get_routes(solution,df_user,data):
  """Get vehicle routes from a solution and store them in an array."""
  # Get vehicle routes and store them in a two dimensional array whose
  # i,j entry is the jth location visited by vehicle i along its route.
  routes = []
  #routes_dist = []  
  for route_nbr in range(routing.vehicles()):
    index = routing.Start(route_nbr)
    route = [manager.IndexToNode(index)]
    
    while not routing.IsEnd(index):
      index = solution.Value(routing.Nextvar(index))
      route.append(manager.IndexToNode(index))
    routes.append(route)
    #routes = get_routes(solution,manager)
    routes_t = pd.DataFrame(routes).T
    col_to_iter = routes_t.columns
    routes_t['route_info'] = routes_t.index
    routes_t = pd.melt(routes_t,id_vars=['route_info'],value_vars=col_to_iter)
    routes_t = routes_t.drop_duplicates(subset='value',keep="first")

    df_user['value'] = df_user.index
    df_user_out = pd.merge(df_user,routes_t,on="value")
    
    df_user_out = df_user_out.sort_values(by=['variable','route_info'])
    df_user_out['route_lag'] = df_user_out.groupby('variable')['value'].shift(-1).fillna(0)
    df_user_out['route_lag'] = df_user_out['route_lag'].astype(np.int64)
    df_user_out['route_info'] = df_user_out['route_info'].astype(np.int64)
    df_user_out['dist'] = df_user_out.apply(lambda row: distance(row['route_lag'],row['value'],data),axis=1)
    
  return df_user_out

def get_sol(sub_dist_fil,fos_cnt):
    df_user_org_sub = df_user_org[(df_user_org.sub_district == sub_dist_fil) ]
    df_user_org_sub.reset_index( inplace=True,drop=True)

    fos_cnt=fos_cnt

    data = create_data_model(df_user_org_sub,fos_cnt)

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

    # 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)

    # Add distance constraint.
    dimension_name = 'distance'
    routing.AddDimension(
        transit_callback_index,# no slack
        3000,# vehicle maximum travel distance
        True,# start cumul to zero
        dimension_name)
    distance_dimension = routing.GetDimensionorDie(dimension_name)
    distance_dimension.SetGlobalSpanCostCoefficient(100)

    # 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)

    out_df = get_routes(solution,df_user_org_sub,data)

    # Print solution on console.
    if solution:
        print_solution(data,solution)
    return   out_df 
  
   out_1 = get_sol( "banswara",1)

输出如下：

FOS 0 的路由： 0 -> 14 -> 1 -> 9 -> 8 -> 7 -> 10 -> 11 -> 16 -> 13 -> 15 -> 12 -> 4 -> 3 -> 2 -> 5 -> 6 - > 0 路线距离：217m

最大路线距离：217Km

因此，上面的输出仅适用于 banswara 区，但我希望通过在其间编写 for 循环来为所有区提供相同的结果。