A biased randomised GRASP for the electric vehicle routing problem with heterogeneous supplemental infrastructures

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-04-05 DOI:10.1016/j.asoc.2025.113109

Rui Xu , Bowen Song , Wei Xiao , Xing Fan

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引用次数: 0

Abstract

Green logistics policies have positioned electric vehicles (EVs) as the preferred choice for logistics. Prompted by technological advancements, more companies are now adopting electric logistics vehicles equipped with both charging and battery swapping capabilities. This study addresses the electric vehicle routing problem (EVRP) by integrating various charging technologies, partial charging strategies, and different battery swapping specifications. A mixed-integer programming (MIP) model is developed to minimise total logistics costs, including vehicle operating costs, energy replenishment costs, and variable mileage costs. To solve this problem, we design a biased randomised-greedy randomised adaptive search procedure (BR-GRASP) algorithm incorporating geometric distribution. This algorithm is complemented by local search operators and energy management strategies designed for heterogeneous supplemental infrastructures (HSI). For efficient iterative optimisation, we employ a variable neighbourhood descent (VND) mechanism. Computational experiments validate the effectiveness of HSI and the proposed algorithm from multiple perspectives. Additionally, a real-world case study demonstrates the significant benefits of applying our methods to a logistics company. The research findings offer decision-making recommendations and managerial insights for logistics companies adopting EVs, as well as for relevant government agencies.

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具有异构辅助基础设施的电动汽车路径问题的有偏随机把握

绿色物流政策已将电动汽车（EV）定位为物流的首选。在技术进步的推动下，越来越多的公司开始采用配备充电和电池更换功能的电动物流车。本研究通过整合各种充电技术、部分充电策略和不同的电池更换规格，来解决电动汽车路由问题（EVRP）。为了使物流总成本（包括车辆运营成本、能源补充成本和可变里程成本）最小化，我们开发了一个混合整数编程（MIP）模型。为解决这一问题，我们设计了一种包含几何分布的偏置随机-贪婪随机自适应搜索程序（BR-GRASP）算法。针对异构补充基础设施（HSI）设计的局部搜索算子和能源管理策略对该算法进行了补充。为了实现高效的迭代优化，我们采用了可变邻域下降（VND）机制。计算实验从多个角度验证了 HSI 和所提算法的有效性。此外，一项实际案例研究证明了将我们的方法应用于一家物流公司的显著优势。研究结果为采用电动汽车的物流公司以及相关政府机构提供了决策建议和管理见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.