Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (3): 272-278.DOI: 10.3778/j.issn.1002-8331.1811-0198

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Robust Optimization of Express Delivery Network with Uncertain Demand

XU Chuanglai, HU Jiankun, HUANG Youfang   

  1. Logistics Science and Engineering Research Institute, Shanghai Maritime University, Shanghai 201306, China
  • Online:2020-02-01 Published:2020-01-20



  1. 上海海事大学 物流科学与工程研究院,上海 201306

Abstract: During the “Double Eleventh” period, the phenomenon of “warehouse explosion” in the distribution center shows that the determination of vehicle routing under demand is not suitable for solving the problem of the surge in demand. On the basis of demand determination model, a robust optimization research model based on scenario set is constructed, and customer service time is reallocated according to the number of express mail. By calculating the travel time of each route, the maximum utilization of self-owned vehicles is realized on the basis of meeting the time window. The improved ant colony algorithm uses MATLAB to solve the problem. The analysis shows that there is a small gap between the cost and the optimal value of the cost, but the advantage of the total travel time is obvious. There is a correlation between the size of the company and the volume of business and the total cost, and the increase of the volume of business will not lead to the rapid growth of profits.

Key words: robust optimization, uncertain demand, vehicle routing, scenario set, ant colony algorithm, 2-opt

摘要: “双十一”期间,分拨中心“爆仓”现象表明:确定需求下的车辆路径不适合解决需求激增的配送问题。以需求确定模型为基础,构建基于情景集的鲁棒优化研究模型,并根据变化的快件数量,重新分配客户服务时间;通过计算每条路径的旅行时间,在满足时间窗的基础上,实现对自有车辆的最大利用。改进蚁群算法利用Matlab求解,分析发现:优先考虑运输时效所产生的成本与成本最优值存在较小差距,但总旅行时间优势明显;公司规模与业务量和总成本之间存在相关性,业务量增加并不会引起利润快速增长。

关键词: 鲁棒优化, 不确定需求, 车辆路径, 情景集, 蚁群算法, 2-opt