Pickup and delivery problem with electric vehicles and time windows considering queues

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Research Part C-Emerging Technologies Pub Date : 2024-08-22 DOI:10.1016/j.trc.2024.104829
Saiqi Zhou , Dezhi Zhang , Wen Yuan , Zhenjie Wang , Likun Zhou , Michael G.H. Bell
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Abstract

The electric vehicle, as a green and sustainable technology, has gained tremendous development and application recently in the logistics distribution system. However, the increasing workload and limited infrastructure capacity pose challenges for electric vehicles in the pickup and delivery operating system, including task allocation, electric vehicle routing, and queue scheduling. To address these issues, this paper introduces a pickup and delivery problem with electric vehicles and time windows considering queues, which considers queue scheduling for multiple electric vehicles when operating at the same site. A novel mixed integer linear programming model is proposed to minimize the cost of travel distance and queue time. An adaptive hybrid neighborhood search algorithm is developed to solve the moderately large-scale problem. Experimental results demonstrate the effectiveness of the model and adaptive hybrid neighborhood search algorithm. The competitive performance of the developed algorithm is further confirmed by finding 9 new best solutions for the pickup and delivery problem with electric vehicles and time windows benchmark instances. Moreover, the results and sensitivity analysis of objective weight costs highlight the impact and importance of considering queues in the studied problem and obtain some management insights.

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电动汽车的取货和送货问题以及考虑到排队的时间窗口
电动汽车作为一种绿色可持续发展技术,近年来在物流配送系统中得到了极大的发展和应用。然而,不断增加的工作量和有限的基础设施容量给电动汽车在取送操作系统中的任务分配、电动汽车路由和队列调度等方面带来了挑战。为解决这些问题,本文提出了一个考虑到队列的电动车辆和时间窗口的取送问题,该问题考虑了多辆电动车辆在同一站点运行时的队列调度。本文提出了一个新颖的混合整数线性规划模型,以最小化行驶距离和排队时间的成本。开发了一种自适应混合邻域搜索算法来解决这个中等规模的问题。实验结果证明了模型和自适应混合邻域搜索算法的有效性。通过为电动汽车和时间窗口基准实例的取货和送货问题找到 9 个新的最佳解决方案,进一步证实了所开发算法的竞争性能。此外,目标权重成本的结果和敏感性分析突出了在所研究的问题中考虑队列的影响和重要性,并获得了一些管理启示。
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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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