摘要
针对城市物流混合车队调度的现实需求,本文结合电动汽车车队调度问题的典型特征,系统构建融合电动物流车与传统燃油车的混合车队调度模型,并引入服从联合累积分布函数的需求不确定性因素,建立混合整数规划模型。为高效求解该模型,提出一种改进海星优化算法,在邻域解生成阶段融合模拟退火机制对新解进行接受判定,从而增强全局搜索能力,避免算法陷入局部最优。在算法有效性验证部分,分别与海星优化算法、模拟退火算法和遗传算法进行对比实验。结果表明:所提出的算法在总成本控制与收敛效率方面具有明显优势。在混合车队调度效率验证方面,通过与单一电动物流车队方案对比,验证混合车队在多种工况下的调度优势,最优运营成本降低幅度达7.73%。灵敏度分析进一步指出,电池容量与充电速率的提升均有助于降低总成本,但呈现边际效应递减趋势,此外,充电速率比电池容量更加敏感。
For the practical demand of urban logistics mixed fleet scheduling,considering the typical characteristics of electric vehicle fleet scheduling,a mixed fleet scheduling model is systematically constructed encompassing both electric logistics and traditional fuel vehicles.To solve demand uncertainty,a joint cumulative distribution function is introduced,and a mixed-integer programming model is developed.To efficiently solve the model,an improved starfish optimization algorithm is proposed,in which a simulated annealing mechanism is embedded within the neighborhood solution generation process to evaluate newly generated solutions.The integration enhances the global search capability and prevents convergence to local optima.To validate the effectiveness of the proposed algorithm,comparative experiments based on the JD Logistics are carried out with the starfish optimization algorithm,simulated annealing algorithm and genetic algorithm.The results demonstrate that the proposed algorithm outperforms the benchmarks in terms of both total cost and convergence efficiency.In terms of operational efficiency,a comparative analysis with purely electric logistics fleet reveals that the mixed fleet achieves superior performance under various operating conditions,reducing the total cost by up to 7.73%.Moreover,sensitivity analysis indicates that increasing battery capacity and improving charging speed can reduce total cost,though with diminishing marginal returns.Notably,charging speed exerts a more pronounced influence on cost reduction than battery capacity.
作者
侯伟路
石琴
付昕男
Hou Weilu;Shi Qin;Fu Xinnan(School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei 230009;Hefei University of Technology,Key Laboratory for Automated Vehicle Safety Technology of Anhui Province,Hefei 230009;Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province,Hefei 230009)
出处
《汽车工程》
北大核心
2025年第12期2409-2419,共11页
Automotive Engineering
基金
安徽省重点研发项目(202304A05020087)
安徽省科技厅项目(JZ2024AKKG0003)资助。
关键词
混合车队调度
改进海星优化算法
物流网络
需求不确定性
mixed fleet scheduling
improved starfish optimization algorithm
logistics network
demand uncertainty
