电动汽车最小-最大路由问题的高效算法

Seyed Sajjad Fazeli , Saravanan Venkatachalam , Jonathon M. Smereka
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引用次数: 0

摘要

运输行业温室气体排放量的增加促使企业和政府大力提倡和支持电动汽车(EV)的生产。随着城市化和电子商务的发展,运输公司正在用电动汽车取代传统车队,以加强可持续发展和环保运营。然而,部署电动汽车车队需要高效的路由和充电策略,以缓解其有限的续航能力并降低电池衰减率。在这项研究中,我们考虑了在电池容量有限和充电站稀缺的情况下,电动汽车车队的运输和物流能力。我们引入了一个最小-最大电动汽车路由问题(MEVRP),即在考虑充电站充电的同时,最大限度地减少任何电动汽车的最大行驶距离。我们提出了一个高效的分支和切割框架以及一个三阶段混合启发式算法,可以高效地解决各种实例。我们进行了大量的计算结果和敏感性分析,从定量和定性两方面证实了所提方法的效率。最后,利用机器人操作系统(ROS)中间件进行了数据驱动模拟,从定量和定性两方面证实了所提方法的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient algorithms for electric vehicles’ min-max routing problem

An increase in greenhouse gases emission from the transportation sector has led companies and the government to elevate and support the production of electric vehicles (EV). With recent developments in urbanization and e-commerce, transportation companies are replacing their conventional fleet with EVs to strengthen the efforts for sustainable and environment-friendly operations. However, deploying a fleet of EVs asks for efficient routing and recharging strategies to alleviate their limited range and mitigate the battery degradation rate. In this work, a fleet of electric vehicles is considered for transportation and logistic capabilities with limited battery capacity and scarce charging station availability. We introduce a min-max electric vehicle routing problem (MEVRP) where the maximum distance traveled by any EV is minimized while considering charging stations for recharging. We propose an efficient branch and cut framework and a three-phase hybrid heuristic algorithm that can efficiently solve a variety of instances. Extensive computational results and sensitivity analyses are performed to corroborate the efficiency of the proposed approach, both quantitatively and qualitatively. Finally a data-driven simulation implemented with the robot operating system (ROS) middleware are performed to corroborate the efficiency of the proposed approach, both quantitatively and qualitatively.

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CiteScore
18.20
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