Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (9): 264-270.DOI: 10.3778/j.issn.1002-8331.1801-0297

Previous Articles    

Multi-Products Joint Distribution Vehicle Routing Optimization with Uncertain Circumstance

GE Xianlong1,2, XUE Guiqin1   

  1. 1.School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
    2.Key Laboratory of Intelligent Logistics Network, Chongqing Jiaotong University, Chongqing 400074, China
  • Online:2019-05-01 Published:2019-04-28

不确定环境下多品类共同配送路径优化

葛显龙1,2,薛桂琴1   

  1. 1.重庆交通大学 经济与管理学院,重庆 400074
    2.重庆交通大学 智能物流网络重点实验室,重庆 400074

Abstract: In view of the interference of dynamic events in distribution process, this paper proposes the multi-products joint distribution vehicle routing problem. Based on the dynamic events in uncertain circumstance, this paper proposes a space-time passion distribution method to generate dynamic customers and buils the multi-products joint distribution vehicle routing problem with uncertain circumstance accordingly for the sake of the minimum the overall operating cost and fixed cost. For the particularity of this issues, a combination of genetic algorithm and Tabu search algorithm is designed and its performance is tested by a set of specific examples. The results show that the multi-products joint distribution method is superior to the single one, and the improved combinational algorithm has a better find-best ability.

Key words: logistics engineering, joint distribution, a combination of genetic algorithm and Tabu search algorithm, multi-products, vehicle routing problem, uncertainty

摘要: 针对动态事件对配送过程的干扰问题,提出多品类共同配送车辆路径优化问题。基于对不确定环境下动态客户时空特性的分析,提出利用时空泊松分布生成动态客户的方法;并从整体运营成本及车辆固定成本入手,建立不确定环境下多品类共同配送模型;鉴于考虑模型的特殊性,设计遗传-禁忌搜索组合优化算法,结合具体算例对模型和算法性能进行验证。结果表明,提出的多品类共同配送方法优于单品类配送方法,且改进后的遗传-禁忌搜索算法具有更强的寻优能力。

关键词: 物流工程, 共同配送, 遗传禁忌算法, 多品类, 车辆路径问题, 不确定性