TY - JOUR
T1 - Applying artificial bee colony algorithm to the multidepot vehicle routing problem
AU - Gu, Zhaoquan
AU - Zhu, Yan
AU - Wang, Yuexuan
AU - Du, Xiaojiang
AU - Guizani, Mohsen
AU - Tian, Zhihong
N1 - Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
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U2 - 10.1002/spe.2838
DO - 10.1002/spe.2838
M3 - Article
AN - SCOPUS:85084196738
SN - 0038-0644
VL - 52
SP - 756
EP - 771
JO - Software - Practice and Experience
JF - Software - Practice and Experience
IS - 3
ER -