降低这段代码重复率：def calDistanceMatrix(model): for i in range(len(model.demand_id_list)): from_node_id = model.demand_id_list[i] for j in range(i + 1, len(model.demand_id_list)): to_node_id = model.demand_id_list[j] dist = math.sqrt((model.demand_dict[from_node_id].x_coord - model.demand_dict[to_node_id].x_coord) ** 2 + (model.demand_dict[from_node_id].y_coord - model.demand_dict[to_node_id].y_coord) ** 2) model.distance_matrix[from_node_id, to_node_id] = dist model.distance_matrix[to_node_id, from_node_id] = dist for _, vehicle in model.vehicle_dict.items(): dist = math.sqrt((model.demand_dict[from_node_id].x_coord - vehicle.x_coord) ** 2 + (model.demand_dict[from_node_id].y_coord - vehicle.y_coord) ** 2) model.distance_matrix[from_node_id, vehicle.type] = dist model.distance_matrix[vehicle.type, from_node_id] = dist

可以将其中的重复计算距离的部分抽象成一个函数，传入不同的参数进行计算。具体实现如下：

def cal_distance(model, node1, node2):
    return math.sqrt((model.demand_dict[node1].x_coord - model.demand_dict[node2].x_coord) ** 2 + (model.demand_dict[node1].y_coord - model.demand_dict[node2].y_coord) ** 2)

def cal_distance_matrix(model):
    for i in range(len(model.demand_id_list)):
        from_node_id = model.demand_id_list[i]
        for j in range(i + 1, len(model.demand_id_list)):
            to_node_id = model.demand_id_list[j]
            dist = cal_distance(model, from_node_id, to_node_id)
            model.distance_matrix[from_node_id, to_node_id] = dist
            model.distance_matrix[to_node_id, from_node_id] = dist

        for _, vehicle in model.vehicle_dict.items():
            dist = cal_distance(model, from_node_id, vehicle)
            model.distance_matrix[from_node_id, vehicle.type] = dist
            model.distance_matrix[vehicle.type, from_node_id] = dist

这样，可以看到重复计算距离的代码已经被封装成一个函数 `cal_distance`，使得主函数 `cal_distance_matrix` 更加简洁易懂，也易于维护。