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首页> 外文期刊>Transportation research, Part B. Methodological >A cumulative service state representation for the pickup and delivery problem with transfers
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A cumulative service state representation for the pickup and delivery problem with transfers

机译：传输的取件和递送问题的累积服务状态表示形式

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The pickup and delivery problem with transfers is a challenging version of the vehicle routing problem. In order to tackle this problem, we add a time dimension to physical transportation networks to not only track the location of vehicles at any time but also impose parcels' pickup/delivery time windows, synchronization time points, and precedence constraints to the problem. We also add another dimension, described as the "cumulative service state" to the constructed space-time network to track the service status of parcels at any time. The constructed network not only handles real-life transportation networks but also is well-suited for connecting microscopic cumulative service states to macroscopic cumulative flow count diagrams. We develop a continuous time approximation approach using cumulative arrival, departure, and on-board count diagrams to effectively assess the performance of the system and dynamically constrict the search space. To handle a large-scale set of parcels, we develop the traditional cluster-first, route-second approach. We reach optimality for the clusters derived from the original set of parcels. We also propose an integer programming model to improve the vehicles' efficiency. We perform extensive numerical experiments over the standard data set used by Ropke and Pisinger (2006) and real-world large-scale data set proposed by Cainiao Network (with about 10,000 delivery orders) to examine the computational efficiency of our developed algorithm. (C) 2019 The Authors. Published by Elsevier Ltd.
机译：接送的取货和交付问题是车辆路线问题的一个具有挑战性的版本。为了解决这个问题，我们在物理运输网络中增加了一个时间维度，不仅可以随时跟踪车辆的位置，还可以对包裹的取件/交付时间窗口、同步时间点和优先级约束施加问题。我们还在构建的时空网络中增加了另一个维度，称为“累积服务状态”，以随时跟踪包裹的服务状态。构建的网络不仅可以处理现实生活中的交通网络，而且非常适合将微观累积服务状态连接到宏观累积流量计数图。我们开发了一种连续时间近似方法，使用累积到达、离开和机载计数图来有效评估系统的性能并动态收缩搜索空间。为了处理大量地块，我们开发了传统的集群优先，路线第二的方法。我们达到了从原始宗地集派生的聚类的最优性。我们还提出了一种整数规划模型来提高车辆的效率。我们对Ropke和Pisinger（2006）使用的标准数据集和菜鸟网络提出的真实世界大规模数据集（大约有10,000个交付订单）进行了广泛的数值实验，以检验我们开发的算法的计算效率。（C） 2019 年作者。由以下开发商制作：Elsevier Ltd.

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