摘要
为解决传统反应性调度方法存在的动态客户响应能力不足和响应效率不高的问题,针对海量、分散、多样、并发的电商物流需求,在分析历史交易数据的基础上,从动态客户预测、分区聚类和车辆路径优化3个角度研究了配送车辆动态调度问题。在利用客户需求的历史表现预测动态需求的基础上,建立了单周期和多周期配送路径优化的数学模型,设计了前摄性车辆调度方案。以重庆某超市为例对模型和算法性能进行验证,结果表明,所提方法能够有效提升动态客户的响应能力和效应效率。
To solve the problem of insufficient dynamic customer response capability and low response efficiency in traditional reactive scheduling methods,aiming at the e-commerce logistics features of massive,decentralized,diverse and concurrent,a proactive scheduling method was proposed to optimize the dynamic customer scheduling process.Based on the analysis of historical transaction data,the dynamic scheduling problem of delivery vehicles was researched by exploiting the analysis of historical transaction data from the perspectives of dynamic customer forecasting,partition clustering and vehicle routing optimization.The dynamic demand was predicted by using the historical performance of customer demand,a mathematic model of single-cycle and multi-cycle delivery route optimization was established,the proactive vehicle scheduling scheme was designed,and the response efficiency of customer needs was improved.A supermarket in Chongqing was taken as an example,and the results showed that the proposed method could effectively improve the dynamic customer response capability and efficiency.
作者
葛显龙
薛桂琴
GE Xianlong;XUE Guiqin(School of Economics and Management,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2018年第8期2111-2121,共11页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金项目资助(71502021
71602015)
教育部人文社会科学基金项目资助(2014YJC630038
2015XJC630007)
博士后科学基金特别资助项目(2016T90862)
重庆市基础与前沿研究资助项目(cstc2016jcyjA0160)
重庆市教委人文社会科学研究资助项目(17SKG073)
重庆市科学技术研究资助项目(KJ1500702)~~
关键词
前摄性调度
车辆路径问题
分区
预测
物流
proactive scheduling
vehicle routing problem
districting
forecasting
logistics
