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

• Published in: IEEE access Vol. 9; p. 1
• Main Authors: He, Meiling, Wei, Zhixiu, Wu, Xiaohui, Peng, Yongtao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive; Adaptive algorithms; Algorithms; Ant colony algorithm; Ant colony optimization; Economics; Mathematical model; Optimization; Route planning; Search problems; Searching; soft time window; Time factors; Transportation; variable neighborhood search; Vehicle routing; vehicle routing problem; Windows (intervals)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3056067

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.