An improved multiobjective evolutionary algorithm for time-dependent vehicle routing problem with time windows

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal Pub Date : 2024-11-15 DOI:10.1016/j.eij.2024.100574

Jia-ke Li , Jun-qing Li , Ying Xu

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引用次数: 0

Abstract

Time-dependent vehicle routing problem with time windows (TDVRPTW) is a pivotal problem in logistics domain. In this study, a special case of TDVRPTW with temporal-spatial distance (TDVRPTW-TSD) is investigated, which objectives are to minimize the total travel time and maximize customer satisfaction while satisfying the vehicle capacity. To address it, an improved multiobjective evolutionary algorithm (IMOEA) is developed. In the proposed algorithm, a hybrid initialization strategy with two efficient heuristics considering temporal-spatial distance is designed to generate high-quality and diverse initial solutions. Then, two crossover operators are devised to broaden the exploration space. Moreover, an efficient local search heuristic combing the adaptive large neighborhood search (ALNS) and the variable neighborhood descent (VND) is developed to improve the exploration capability. Finally, detailed comparisons with several state-of-the-art algorithms are tested on a set of instances, which verify the efficiency and effectiveness of the proposed IMOEA.

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一种改进的多目标进化算法，用于解决具有时间窗口的时间相关车辆路由问题

有时间窗的时间相关车辆路由问题（TDVRPTW）是物流领域的一个关键问题。本研究探讨了具有时空距离的 TDVRPTW（TDVRPTW-TSD）的一个特例，其目标是在满足车辆容量的前提下，使总行驶时间最小化，客户满意度最大化。为此，开发了一种改进的多目标进化算法（IMOEA）。在所提出的算法中，设计了一种混合初始化策略，其中包含两个考虑到时间-空间距离的高效启发式算法，以生成高质量和多样化的初始解。然后，设计了两个交叉算子来拓宽探索空间。此外，还开发了一种结合自适应大邻域搜索（ALNS）和可变邻域下降（VND）的高效局部搜索启发式，以提高探索能力。最后，在一组实例上与几种最先进的算法进行了详细的比较测试，从而验证了所提出的 IMOEA 的效率和有效性。

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来源期刊

Egyptian Informatics Journal Decision Sciences-Management Science and Operations Research

CiteScore

11.10

自引率

1.90%

发文量

审稿时长

110 days

期刊介绍： The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.