考虑充电站因素的电动汽车路由模型,促进可持续物流

Yan Li, Ming K. Lim, Weiqing Xiong, Xingjun Huang, Yuhe Shi, Songyi Wang
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摘要

目的最近,电动汽车在冷链物流领域得到了广泛应用,以减少过多能源消耗的影响并支持环境友好。考虑到电动汽车的电池容量有限,在配送过程中优化电池充电至关重要。本研究建立了一个带有充电站的冷链物流电动汽车路由模型,该模型将整合多个配送中心,实现可持续物流。建议的优化模型旨在最大限度地降低冷链物流的总体成本,其中包括固定成本、损坏成本、制冷成本、罚款成本、排队成本、能源成本和碳排放成本。此外,建议的模型还考虑了充电站的时变速度、时变电价、能耗和排队等因素。在拟议模型中,开发了一种混合乌鸦搜索算法(CSA),该算法结合了基于反对的学习(OBL)和禁忌搜索(TS),用于优化目的。算法实验结果表明,与遗传算法(GA)和粒子群优化(PSO)相比,混合乌鸦搜索算法在解的质量和速度方面都很有效。研究局限性/意义基于电动汽车的冷链物流配送路径优化模型为管理者制定配送计划提供了参考,有助于可持续物流的发展。原创性/价值在以往的研究中,许多学者分别就冷链物流车辆路径问题和电动汽车路径问题进行了相关研究,但很少有学者将上述两个课题进行合并。为此,本研究创新性地设计了冷链物流电动汽车选线模型,考虑了时变速度、时变电价、能源消耗和充电站排队等问题,使之与现实世界相一致。
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An electric vehicle routing model with charging stations consideration for sustainable logistics
PurposeRecently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.Design/methodology/approachThis study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.FindingsThe result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.Research limitations/implicationsThe optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.Originality/valueIn prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.
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