摘要
文章主要关注软时间窗约束下的同时取送货车辆路径规划问题(Vehicle Routing Problem with Simultaneous Pickup-Delivery and Soft Time Window,VRPSPDSTW),这是物流配送活动的核心问题,尤其在互联网催动下的物流行业发展中尤为重要。文章首先构建了一种以最低总成本和最大顾客满意度为目标的取送货车辆路径规划模型,其次,提出了一种改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA),该算法融合了立方混沌映射、精英反向学习策略和正余弦优化算法,以提升种群质量、加速收敛、平衡开发与探索能力,并能有效跳出局部最优,最后,将此算法应用于M物流企业的实际订单数据,并与麻雀搜索算法(Sparrow Search Algorithm,SSA)、遗传算法(Genetic Algorithm,GA)和鲸鱼优化算法(Whale Optimization Algorithm,WOA)的解决方案进行比较。实验结果证明,所提出的ISSA能够有效地解决VRPSPDSTW问题。
This paper primarily focuses on the Vehicle Routing Problem with Simultaneous Pickup-Delivery and Soft Time Window(VRPSPDSTW),which is a core issue in logistics activities,especially vital in the development of the logistics industry driven by the internet.Firstly,the paper constructs a model for simultaneous pickup-delivery vehicle routing with the goal of minimizing total costs and maximizing customer satisfaction.Secondly,the paper proposes an Improved Sparrow Search Algorithm(ISSA),integrating cubic chaotic mapping,elite backward learning strategy,and sine cosine optimization algorithm to improve population quality,accelerate convergence,balance exploitation and exploration capabilities,and effectively escape from local optima.Lastly,we apply this algorithm to the actual order data of M logistics company and compare its performance with the solutions of the Sparrow Search Algorithm(SSA),Genetic Algorithm(GA),and Whale Optimization Algorithm(WOA).Experimental results demonstrate that the proposed ISSA can effectively solve the VRPSPDSTW problem.
作者
肖磊
XIAO Lei(School of Mechanical Engineering,Shenyang University,Shenyang 110003,China)
出处
《物流科技》
2023年第16期89-94,共6页
Logistics Sci-Tech
关键词
车辆路径规划
同时取送货
客户满意度
改进麻雀搜索算法
vehicle routing planning
simultaneous pickup and delivery
customer satisfaction
Improved Sparrow Search Algorithm
