Optimizing Multi-Depot Vehicle Routing: An ABC-GA Hybrid Algorithm
DOI:
https://doi.org/10.38032/scse.2026.4.44Keywords:
Multi Depot Vehicle Routing Problem (MDVRP), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Route Optimization, ABC-GA, MetaheuristicsAbstract
The Multi Depot Vehicle Routing Problem (MDVRP) is a generalized form of the Vehicle Routing Problem (VRP) and Travelling Salesman Problem (TSP). It is considered one of the NP-hard optimization problems. The MDVRP is a logistics problem that involves finding the most efficient route to transport goods between multiple different pickup and delivery locations. In this study a hybrid metaheuristic algorithm that integrates artificial bee colony and genetic algorithms is developed to solve MDVRP efficiently. The main objective of this study is to find the optimal route from multiple depots to serve a set of customers dispersed in different geographical locations. Initially nearest neighbor algorithm is used to assign customer to their nearest depot and randomly generated initial solution. These solutions are subsequently optimized using the proposed hybrid ABC-GA algorithm. The ABC algorithm serves as the main optimization framework, within which GA operators are embedded in the employed and onlooker bee phases. A probabilistic selection mechanism dynamically applies ABC neighbor search, GA operators, and 2-opt local search to improve solution quality. Finally, the results of the ABC-GA approach are compared against the outcomes of the conventional GA and ABC algorithm to assess performance improvements. Experimental results demonstrate that the hybrid approach consistently produces lower routing costs, achieving superior solution quality at the expense of increased computational time.
Downloads
Downloads
Downloads
References
[1] I. Kaabachi, H. Yahyaoui, S. Krichen, and A. Dekdouk, “Measuring and evaluating hybrid metaheuristics for solving the multi-compartment vehicle routing problem,” Measurement (Lond)., vol. 141, pp. 407–419, Jul. 2019, doi: 10.1016/j.measurement.2019.04.019. DOI: https://doi.org/10.1016/j.measurement.2019.04.019
[2] R. Elshaer and H. Awad, “A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants,” Comput. Ind. Eng., vol. 140, Feb. 2020, doi: 10.1016/j.cie.2019.106242. DOI: https://doi.org/10.1016/j.cie.2019.106242
[3] H. M. Pandey, A. Chaudhary, and D. Mehrotra, “A comparative review of approaches to prevent premature convergence in GA,” 2014, Elsevier Ltd. doi: 10.1016/j.asoc.2014.08.025. DOI: https://doi.org/10.1016/j.asoc.2014.08.025
[4] S. E. Comert and H. R. Yazgan, “A new approach based on hybrid ant colony optimization-artificial bee colony algorithm for multi-objective electric vehicle routing problems,” Eng. Appl. Artif. Intell., vol. 123, p. 106375, Aug. 2023, doi: 10.1016/J.ENGAPPAI.2023.106375. DOI: https://doi.org/10.1016/j.engappai.2023.106375
[5] Z. Gu, Y. Zhu, Y. Wang, X. Du, M. Guizani, and Z. Tian, “Applying artificial bee colony algorithm to the multidepot vehicle routing problem,” in Software - Practice and Experience, John Wiley and Sons Ltd, Mar. 2022, pp. 756–771. doi: 10.1002/spe.2838. DOI: https://doi.org/10.1002/spe.2838
[6] B. Peng, L. Wu, Y. Yi, and X. Chen, “Solving the multi-depot green vehicle routing problem by a hybrid evolutionary algorithm,” Sustainability (Switzerland), vol. 12, no. 5, Mar. 2020, doi: 10.3390/su12052127. DOI: https://doi.org/10.3390/su12052127
[7] M. Davoodi, M. Malekpour Golsefidi, and M. S. Mesgari, “A hybrid optimization method for vehicle routing problem using artificial bee colony and genetic algorithm,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing, Oct. 2019, pp. 293–297. doi: 10.5194/isprs-archives-XLII-4-W18-293-2019. DOI: https://doi.org/10.5194/isprs-archives-XLII-4-W18-293-2019
[8] M. Rabbani, P. Pourreza, H. Farrokhi-Asl, and N. Nouri, “A hybrid genetic algorithm for multi-depot vehicle routing problem with considering time window repair and pick-up,” Journal of Modelling in Management, vol. 13, no. 3, pp. 698–717, Oct. 2018, doi: 10.1108/JM2-04-2017-0046. DOI: https://doi.org/10.1108/JM2-04-2017-0046
[9] X. Zhong, S. Jiang, and H. Song, “ABCGA Algorithm for the Two Echelon Vehicle Routing Problem,” in Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017, Institute of Electrical and Electronics Engineers Inc., Aug. 2017, pp. 301–308. doi: 10.1109/CSE-EUC.2017.61. DOI: https://doi.org/10.1109/CSE-EUC.2017.61
[10] S. Geetha, P. T. Vanathi, and G. Poonthalir, “Metaheuristic approach for the multi-depot vehicle routing problem,” Applied Artificial Intelligence, vol. 26, no. 9, pp. 878–901, Jan. 2012, doi: 10.1080/08839514.2012.727344. DOI: https://doi.org/10.1080/08839514.2012.727344
[11] Surekha P and S. Sumathi, “Solution To Multi-Depot Vehicle Routing Problem Using Genetic Algorithms,” 2011. [Online]. Available: www.waprogramming.com
[12] P. Chen and X. Xu, “A hybrid algorithm for multi-depot vehicle routing problem,” Proceedings of 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, IEEE/SOLI 2008, vol. 2, pp. 2031–2034, 2008, doi: 10.1109/SOLI.2008.4682866. DOI: https://doi.org/10.1109/SOLI.2008.4682866
[13] “Vehicle Routing Problem Set.” Accessed: Aug. 13, 2025. [Online]. Available: https://www.kaggle.com/datasets/adamjoseph7945/vehicle-routing-problem-set
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2026 Md. Ashiqur Rahman Hridoy , Rafiatun Ferdous Khan Lubaba , Sanjeeb Roy , Deepta Dhar (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All the articles published by this journal are licensed under a Creative Commons Attribution 4.0 International License
How to Cite
