基于消费行为预测的前摄性电动车辆路径问题  

Proactive Electric Vehicle Routing Problem Based on Consumer Behavior Prediction

作　　者：葛显龙 姜云云 尹秋霜 杨育树 GE Xianlong;JIANG Yunyun;YIN Qiushuang;YANG Yushu(School of Economics and Management,Chongqing Jiaotong University,Chongqing 400074,China;College of Traffic&Transportation,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学经济与管理学院,重庆400074 [2]重庆交通大学交通运输学院,重庆400074

出　　处：《重庆交通大学学报（自然科学版）》2024年第11期84-94,共11页Journal of Chongqing Jiaotong University(Natural Science)

基　　金：国家社会科学基金项目资助(19CGL041);重庆市自然科学基金面上项目(cstc2020jcyj⁃msxmX0108);“成渝地区双城经济圈建设”科技创新重点项目(KJCXZD2020031)。

摘　　要：针对海量、分散、随机到达的电商物流订单需求,提出基于需求预测的前摄性物流服务模式。结合商品属性、用户行为及其交互特征等消费行为轨迹数据预测客户潜在购买意愿,将预测订单需求前置到客户就近仓库,建立“中心仓-仓配中心-客户”两级电动汽车网络优化模型。鉴于问题的复杂性,设计两阶段混合启发式智能优化算法,第1阶段采用二边逐次修正方法获取初始解,第2阶段设计混合模拟遗传算法对初始解迭代寻优。研究结果表明:前摄性配送的一级网络运输总距离、总成本分别节约106%和101%,而二级网络总成本增加仅为9%。Under the requirement of massive,dispersed,and randomly arrived e-commerce logistics orders,a proactive logistics service mode based on demand prediction was proposed.The product attributes,user behaviors,customer interaction characteristics and other behavior trajectory data were combined to predict the potential purchasing intentions of customers,and the predicted order demands was advanced to customers’nearest warehouse.Therefore,a two-echelon electric vehicle network optimization model of“central depot to forward center to customer”was established.Given the complexity of the problem,a two-stage hybrid heuristic intelligent optimization algorithm was designed.In the first stage,a two-edge successive correction method was used to obtain the initial solution,and in the second stage,a hybrid simulated genetic algorithm was designed to iteratively optimize the initial solution.The experimental results show that the total transportation distance and total cost of the first level network for proactive delivery are reduced by 106%and 101%,respectively,while the total cost of the second level network is increased by only 9%.

关 键 词：交通运输工程 需求预测 前摄性配送 混合模拟遗传算法 两级路网 电动汽车 

分 类 号：U469.72[机械工程—车辆工程] TP311.1[交通运输工程—载运工具运用工程]