Applying artificial bee colony algorithm to the multidepot vehicle routing problem

Zhaoquan Gu, Yan Zhu, Yuexuan Wang, Xiaojiang Du, Mohsen Guizani, Zhihong Tian

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

With advanced information technologies and industrial intelligence, Industry 4.0 has been witnessing a large scale digital transformation. Intelligent transportation plays an important role in the new era and the classic vehicle routing problem (VRP), which is a typical problem in providing intelligent transportation, has been drawing more attention in recent years. In this article, we study multidepot VRP (MDVRP) that considers the management of the vehicles and the optimization of the routes among multiple depots, making the VRP variant more meaningful. In addressing the time efficiency and depot cooperation challenges, we apply the artificial bee colony (ABC) algorithm to the MDVRP. To begin with, we degrade MDVRP to single-depot VRP by introducing depot clustering. Then we modify the ABC algorithm for single-depot VRP to generate solutions for each depot. Finally, we propose a coevolution strategy in depot combination to generate a complete solution of the MDVRP. We conduct extensive experiments with different parameters and compare our algorithm with a greedy algorithm and a genetic algorithm (GA). The results show that the ABC algorithm has a good performance and achieve up to 70% advantage over the greedy algorithm and 3% advantage over the GA.

Original languageEnglish
Pages (from-to)756-771
Number of pages16
JournalSoftware - Practice and Experience
Volume52
Issue number3
DOIs
StatePublished - Mar 2022

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