Volume 8, Issue 2 (6-2026)
sjamao 2026, 8(2): 10-14 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mahmoodi Fard A, Mozaffarghadirli N. Optimizing transport routing in the supply chain using the Ant Colony Optimization in MATLAB. sjamao 2026; 8 (2) :10-14
URL: http://sjamao.srpub.org/article-7-290-en.html
URL: http://sjamao.srpub.org/article-7-290-en.html
1- National University of Skill-Enghelab Eslami Technical and Vocational College, Tehran, Iran. , alireza10.m10@gmail.com
2- Department of Industrial Engineering, Abrar University, Tehran, Iran.
2- Department of Industrial Engineering, Abrar University, Tehran, Iran.
Abstract: (9 Views)
In recent years, numerous efforts have focused on lowering transportation costs through various vehicle routing problem models. The increasing expenses in transportation have led many to seek ways to cut these costs by implementing vehicle routing systems. This study explores the transportation routing problem within the supply chain. The vehicle routing problem involves vehicles leaving a warehouse, visiting a group of customers, and then returning to the warehouse. Each vehicle has a fixed capacity, and each customer has a specific demand that must be completely fulfilled. The challenge lies in selecting the customers for each vehicle and designing routes according to vehicle routing problem rules, aiming to minimize the total distance traveled, the overall early and late times, and ultimately the total cost. As the vehicle routing problem is intricate and falls under the NP-Hard category, metaheuristic methods are used to address it. This research suggests employing the Ant Colony Optimization to tackle the vehicle routing problem, with the objective of finding an optimal solution using MATLAB software.
Keywords: Vehicle Routing, Ant Colony Optimization, Supply Chain, Optimization, Transportation, MATLAB Software
Type of Study: Applicable |
Subject:
Strategy and Management
Received: 2026/04/7 | Revised: 2026/05/23 | Accepted: 2026/06/5 | Published: 2026/06/15
Received: 2026/04/7 | Revised: 2026/05/23 | Accepted: 2026/06/5 | Published: 2026/06/15
References
1. A. I. Makoui, Mehyar,, "Uncertainty control operational strategies in supply chain management.," Information, educational and research quarterly, numbers 7 and 8., pp. .117-121, 2013.
2. G. B. Dantzig, and J. H. Ramser, "The Truck Dispatching Problem," Management Science, vol. 6, no. 1, pp. 80-91, 1959/10/01, 1959. [DOI:10.1287/mnsc.6.1.80]
3. M. Babaei, and M. Shoudi Noushabadi, "Np complete problems," in The 7th International Conference on Information Technology, Computers and Telecommunications, 2019.
4. M. Yousefikhoshbakht, Didehvar, F., Rahmati, R. & Sedighpour, M., "An effective imperialist competitive algorithm for solving the open vehicle routing problem. ," Transportation Research Journal, (2012). .
5. E. E. Zachariadis, Tarantilis, C. D. & Kiranoudis, C. T., "A Guided Tabu Search for the Vehicle Routing Problem with two-dimensional loading constraints.," European Journal of Operational Research, (2009). [DOI:10.1016/j.ejor.2007.05.058]
6. L. H. Osman, "Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem.," Annal Operations Research, (1993). [DOI:10.1007/BF02023004]
7. L. Santos, Coutinho-Rodrigues, J. & Current, J. R. , "An improved ant colony optimization based algorithm for the capacitated arc routing problem.," Transportation Research Part B: Methodological, 44(2), 246-266., (2010). [DOI:10.1016/j.trb.2009.07.004]
8. C. Prins, "Two memetic algorithms for heterogeneous fleet vehicle routing problems.," Engineering Applications of Artificial Intelligence, 22, 916˚ 928., (2009). [DOI:10.1016/j.engappai.2008.10.006]
9. J. Tang, Zhang, J. & Pan, Z., "A scatter search algorithm for solving vehicle routing problem with loading cost.," Expert Systems with Applications, 37(6), 4073˚ 4083., (2010). [DOI:10.1016/j.eswa.2009.11.027]
10. L. H. Du, R., "Combining Nearest Neighbor Search with Tabu Search for Large-Scale Vehicle Routing Problem.," Physics Procedia, 25, 1536-1546., (2012). [DOI:10.1016/j.phpro.2012.03.273]
11. A. E. Yurtkuran, E., "A new Hybrid Electromagnetism-like Algorithm for capacitated vehicle routing problems. ," Expert Systems with Applications, 37(4), 3427-3433. , (2010). [DOI:10.1016/j.eswa.2009.10.005]
12. P. Chen, Huang, H. K. & Dong, X. Y., " Iterated variable neighborhood descent algorithm for the capacitated vehicle routing problem.," Expert Systems with Applications, 37(2), 1620-1627. , (2010). [DOI:10.1016/j.eswa.2009.06.047]
13. V. C. Hemmelmayr, Cordeau, J. F. & Crainic, T. G. , "An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics.," Computers & Operations Research, 39(12), 3215-3228., (2012.). [DOI:10.1016/j.cor.2012.04.007]
14. A. Imran, Salhi, S. & Wassan, N., "A.A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem," European Journal of Operational Research. 197(2), 509-518. , (2009). [DOI:10.1016/j.ejor.2008.07.022]
15. B. Yao, B. Yu, P. Hu, J. Gao, and M. Zhang, "An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot," Annals of Operations Research, vol. 242, pp. 303-320, 2016. [DOI:10.1007/s10479-015-1792-x]
16. M. Avci, and S. Topaloglu, "A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery," Expert Systems with Applications, vol. 53, pp. 160-171, 2016. [DOI:10.1016/j.eswa.2016.01.038]
17. A. Gupta, and S. Saini, "On solutions to vehicle routing problems using swarm optimization techniques: a review," Advances in Computer and Computational Sciences: Proceedings of ICCCCS 2016, Volume 1, pp. 345-354, 2017. [DOI:10.1007/978-981-10-3770-2_32]
18. M. A. Mohammed, M. K. Abd Ghani, R. I. Hamed, S. A. Mostafa, M. S. Ahmad, and D. A. Ibrahim, "Solving vehicle routing problem by using improved genetic algorithm for optimal solution," Journal of computational science, vol. 21, pp. 255-262, 2017. [DOI:10.1016/j.jocs.2017.04.003]
19. M. Bruglieri, S. Mancini, F. Pezzella, and O. Pisacane, "A path-based solution approach for the green vehicle routing problem," Computers & Operations Research, vol. 103, pp. 109-122, 2019. [DOI:10.1016/j.cor.2018.10.019]
20. B. Peng, L. Wu, Y. Yi, and X. Chen, "Solving the multi-depot green vehicle routing problem by a hybrid evolutionary algorithm," Sustainability, vol. 12, no. 5, pp. 2127, 2020. [DOI:10.3390/su12052127]
21. H. Derbel, Jarboui, B., and Siarry, P., " Green Transportation and New Advances in Vehicle Routing Problems, Springer Nature Switzerland.," 2020. [DOI:10.1007/978-3-030-45312-1]
22. G. D. Konstantakopoulos, S. P. Gayialis, and E. P. Kechagias, "Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification," Operational research, pp. 1-30, 2020. [DOI:10.1007/s12351-020-00600-7]
23. H. Abdullahi, L. Reyes-Rubiano, D. Ouelhadj, J. Faulin, and A. A. Juan, "Modelling and multi-criteria analysis of the sustainability dimensions for the green vehicle routing problem," European Journal of Operational Research, vol. 292, no. 1, pp. 143-154, 2021. [DOI:10.1016/j.ejor.2020.10.028]
24. L. Cai, W. Lv, L. Xiao, and Z. Xu, "Total carbon emissions minimization in connected and automated vehicle routing problem with speed variables," Expert Systems with Applications, vol. 165, pp. 113910, 2021. [DOI:10.1016/j.eswa.2020.113910]
25. I. Kucukoglu, R. Dewil, and D. Cattrysse, "The electric vehicle routing problem and its variations: A literature review," Computers & Industrial Engineering, vol. 161, pp. 107650, 2021. [DOI:10.1016/j.cie.2021.107650]
26. Z. Gu, Y. Zhu, Y. Wang, X. Du, M. Guizani, and Z. Tian, "Applying artificial bee colony algorithm to the multidepot vehicle routing problem," Software: Practice and Experience, vol. 52, no. 3, pp. 756-771, 2022. [DOI:10.1002/spe.2838]
27. Aghbal, Ghaderi, Amini, alliance, M. Mulqarni, A. Allah, Norosh, and Iraj, "Investigating the accuracy of artificial neural networks and ant colony optimization algorithm in predicting profit management," Financial Accounting, vol. 39, no. 10, pp. 82-110, 2018.
28. G. Islami Bidgoli, and E. Tayibi Thani, "Optimizing the investment portfolio based on value at risk," Financial engineering and securities management, vol. 5, no. 18, pp. 168-186, 2014.
29. N. Mikael Vand, A. Javadi, Akram, and Hosseinzade, "Analytical solution of differential equations based on combined meta-heuristic method of genetic algorithms and ant colony optimization," New researches in mathematics, vol. 6, no. 27, pp. 27-40, 2020.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |