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基于厢式无人驾驶车辆的多配送点路径规划问题

Multi-depot routing problem with van-based driverless vehicles.

作者信息

Diao Xiaolong, Fan Houming, Zhu Xiayu, Liu Zhaoxing

机构信息

Department of Transportation Engineering, Dalian Maritime University, Dalian, China.

China Construction Eighth Engineering Division, Dalian, China.

出版信息

Sci Rep. 2024 Aug 27;14(1):19807. doi: 10.1038/s41598-024-70781-0.

DOI:10.1038/s41598-024-70781-0
PMID:39191917
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11349997/
Abstract

In order to strengthen the coordination between different delivery participants and means of transport, this work proposes one extension of multi-depot routing problems where vans and driverless vehicles are used in combination during the delivery. The operation process mainly includes two parts. One is that, vans carry several driverless vehicles and goods, and drop off or pick up driverless vehicles at stops. Another is that, driverless vehicles departing directly from depots and dropped off by vans deliver goods to customers in cooperation. During the delivery, vans and driverless vehicles are in close cooperation through the proposed multi-depot joint distribution and the proposed van-van joint distribution. By the two modes, one van can depart from one depot and return to another depot, and one driverless vehicle can be set off by one van at one stop and be picked up by another van at another stop. This multi-depot routing problem with van-based driverless vehicles is formulated as a mixed integer programming model which can be solved by a designed heuristic algorithm. The sensitivity analyses about the maximum number of driverless vehicles in one van and the maximum traveling time of driverless vehicles are also performed. The results reveal that they have limited effects on the delivery cost and the application of the two modes. In addition, the experimental results demonstrate that the application of the two modes is affected by the distribution of depots and stops.

摘要

为加强不同配送参与者与运输方式之间的协调，本文提出了一种多配送中心路径规划问题的扩展，即在配送过程中联合使用厢式货车和无人驾驶车辆。运营过程主要包括两部分。一是厢式货车搭载若干无人驾驶车辆和货物，并在站点装卸无人驾驶车辆。二是直接从配送中心出发并由厢式货车卸载的无人驾驶车辆协同为客户送货。在配送过程中，厢式货车和无人驾驶车辆通过所提出的多配送中心联合配送和厢式货车-厢式货车联合配送紧密协作。通过这两种模式，一辆厢式货车可以从一个配送中心出发并返回另一个配送中心，一辆无人驾驶车辆可以在一个站点由一辆厢式货车出发搭载，并在另一个站点由另一辆厢式货车接载。这种基于厢式货车的无人驾驶车辆的多配送中心路径规划问题被构建为一个混合整数规划模型，该模型可通过设计的启发式算法求解。还对一辆厢式货车中无人驾驶车辆的最大数量和无人驾驶车辆的最大行驶时间进行了敏感性分析。结果表明，它们对配送成本和两种模式的应用影响有限。此外，实验结果表明，两种模式的应用受配送中心和站点分布的影响。

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2
A Two-Echelon Cooperated Routing Problem for a Ground Vehicle and Its Carried Unmanned Aerial Vehicle.地面车辆及其搭载无人机的两级协同路径规划问题
Sensors (Basel). 2017 May 17;17(5):1144. doi: 10.3390/s17051144.