An Adaptive Variable Neighborhood Search Ant Colony Algorithm for Vehicle Routing Problem With Soft Time Windows

被引：23
|
作者：
He, Meiling [1 ]
Wei, Zhixiu [1 ]
Wu, Xiaohui [1 ]
Peng, Yongtao [2 ]
机构：
[1] Jiangsu Univ, Sch Automot & Traff Engn, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Jiangsu Univ, Sch Management, Zhenjiang 212013, Jiangsu, Peoples R China
来源：
IEEE ACCESS | 2021年 / 9卷
基金：
中国国家自然科学基金;
关键词：
Vehicle routing; Mathematical model; Optimization; Transportation; Time factors; Search problems; Economics; Ant colony algorithm; variable neighborhood search; adaptive; soft time window; vehicle routing problem;
D O I：
10.1109/ACCESS.2021.3056067
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, an adaptive variable neighborhood search ant colony algorithm (AVNSACA) is proposed to solve the vehicle routing problem with soft time windows (VRPSTW). The ant colony algorithm's pheromone update strategy is improved to make up for the lack of pheromone in the algorithm's early stage. In order to avoid the algorithm falling into local optimum, two variable neighborhood search operators are designed, and the conditions for the algorithm to enter the variable neighborhood search are set. The effectiveness of AVNSACA in solving vehicle routing problem with soft time windows is verified by Solomon benchmark problem. Through the comparative analysis of the experimental results of the two algorithms, the advantages of the improved ant colony algorithm are illustrated. The experimental results show that the proposed algorithm can effectively obtain better solutions.
引用
收藏
页码：21258 / 21266
页数：9
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