摘要
针对商超配送多批次、小批量的实际情况,综合考虑了需求点的服务时间窗、最小配送量、访问次数等要求,设计了一种新的拆分策略,即“最大车辆载重量—最小配送量”需求拆分策略,构建相关商超配送车辆路径优化模型,并使用人工免疫算法对该模型进行求解。由于需求拆分车辆路径问题是一个复杂的组合优化过程,考虑到传统人工免疫算法局部搜索能力不足的局限,设计了多种变邻域操作改进人工免疫算法,并采用轮盘赌选择法将变邻域操作用于抗体突变,形成变邻域人工免疫算法。通过数值仿真实验,结果显示,变邻域人工免疫算法比人工免疫算法求得的综合成本平均优化3%~5%,碳排放相关成本平均降低5%~10%。
Multi-batch and small-batch delivery are the common for supermarket,this paper comprehensively considers the service time window,minimum distribution volume and number of visits of demand points etc,this paper designs the demand splitting strategy of“maximum vehicle capacity-minimum delivery volume”.Building the problem model with the objective of minimizing the comprehensive cost,and using the Artificial Immune Algorithm(AIA)to solve it.In order to resolve complexly combinatorial Split Delivery Vehicle Routing Problem(SDVRP),considering the limitation of traditional AIA local search capability,this paper designs variable neighborhood operations to improve the AIA and make roulette selection method to apply variable neighborhood operation for antibody mutation,and forms the Variable Neighborhood Search Artificial Immune Algorithm(VNS-AIA).Then,through numerical simulation experiments,the result reflects that VNS-AIA optimizes the comprehensive cost by an average of 3%~5%,and reduces the cost related to carbon emissions by an average of 5%~10%.
作者
刘旺盛
魏琦
马国旺
周佳雨
曾艳
LIU Wang-sheng;WEI Qi;MA Guo-wang;ZHOU Jia-yu;ZENG Yan(College of navigation,Jimei University,Xiamen 361021,China;Xinyang University,Xinyang 464000,China)
出处
《物流研究》
2024年第4期75-84,共10页
Logistics Research
基金
福建省自然科学基金项目(2022J01322)。
关键词
车辆路径
碳排放
变邻域人工免疫算法
需求拆分策略
Vehicle Routing
Carbon Emission
Variable Neighborhood Search Artificial Immune Algorithm
Demand Splitting Strategy
