需求可离散拆分电动汽车充电策略和路径优化问题  

Electric vehicle charging strategies and routing optimization under discrete split demands

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作  者:邢玉伟 王展华 杨华龙 XING Yu-wei;WANG Zhan-hua;YANG Hua-long(College of Transportation Engineering,Dalian Maritime University,Dalian 116026,China)

机构地区:[1]大连海事大学交通运输工程学院,辽宁大连116026

出  处:《控制与决策》2025年第3期987-995,共9页Control and Decision

基  金:辽宁省社会科学规划基金项目(L21CJY004).

摘  要:针对电动汽车的物流配送问题,考虑到客户需求可以拆分成若干离散订单的特性,以最小化电动汽车的固定成本、路径行驶成本、充电成本以及时间窗惩罚成本为目标,构建需求可离散拆分的多车型电动汽车充电策略和路径优化模型.针对模型特点,设计改进的遗传-模拟退火算法.为验证算法的有效性进行算例分析,结果表明,考虑需求可离散拆分的情况下,该算法能够快速优化出电动汽车的充电策略和配送路径,其中部分充电策略不仅能够缩短充电时间,而且能够大幅度降低总成本.敏感性分析结果显示,充电等待时间增加会导致两种策略的时间窗惩罚成本上升,但部分充电策略的成本增速显著低于完全充电策略,尤其适用于充电等待时间较长的情况.所做的研究能够为物流企业电动汽车配送优化提供重要参考.This study endeavors to optimize the logistical distribution of electric vehicles(EVs),considering the characteristic that customer demand can be split into several discrete orders.With the objective of minimizing the fixed,routing,charging,and time window penalty costs for the EVs,a multi-type EV charging strategies and routing optimization model is formulated that considers discrete split demands.Given the characteristics of this model,an improved genetic-simulated annealing algorithm is designed.The effectiveness of the algorithm is validated through empirical analysis.The findings indicate that the algorithm can efficiently optimize EV charging strategies and distribution routes under discrete split demands.Notably,the partial charging strategy not only reduces total costs but also shortens charging time compared to the full charging strategy.Furthermore,sensitivity analysis reveals that as charging waiting time increases,the time window penalty costs rise for both strategies.However,the cost growth rate for the partial charging strategy is notably lower than that of the full charging strategy,suggesting its superior suitability in scenarios involving prolonged charging waiting times.This research offers valuable guidance for logistics companies seeking to optimize EV distribution operations.

关 键 词:电动汽车 充电策略 车辆路径优化 需求可离散拆分 改进的遗传-模拟退火算法 

分 类 号:U116.2[交通运输工程]

 

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