Application of Teaching–Learning-Based Optimization in Solving the Vehicle Routing Problem
Keywords:
Vehicle routing problem, Teaching–learning-based optimization, MetaheuristicsAbstract
The performance of metaheuristic methods can be improved through several approaches, one of the most widely adopted being the determination of appropriate parameter settings. Teaching–Learning-Based Optimization (TLBO) is a metaheuristic algorithm that contains only two main parameters. Therefore, the proper configuration of these parameters plays a crucial role in determining both solution quality and search efficiency.
This study aimed to 1) develop an application based on the Teaching–Learning-Based Optimization algorithm for solving the Vehicle Routing Problem (VRP), and 2) identify suitable parameter-setting patterns of the Teaching–Learning-Based Optimization algorithm for solving the Vehicle Routing Problem. The research process began with the development of a TLBO-based application for solving the Vehicle Routing Problem. Subsequently, an experimental design was conducted to analyze the algorithm's performance using five benchmark problem instances of different sizes in order to determine the most appropriate parameter settings.
The results revealed that the developed application was able to effectively solve the Vehicle Routing Problem using the Teaching–Learning-Based Optimization algorithm when equipped with parameter settings identified through statistical analysis of experimental results. Furthermore, the application was capable of handling problem instances of varying sizes, demonstrating the applicability of the proposed optimization approach for developing practical decision-support tools for transportation routing problems.
References
Chakraa, H., Guérin, F., Leclercq, E., & Lefebvre, D. (2023). Optimization techniques for multi-robot task allocation problems: Review on the state of the art. Robotics and Autonomous Systems, 168, 104492. Doi: 10.1016/j.robot.2023.104492
Chansombat, S., Musikapun, P., Pongcharoen, P., & Hicks, C. (2019). A hybrid discrete bat algorithm with krill herd-based advanced planning and scheduling tool for the capital goods industry. International Journal of Production Research, 57(21), 6705–6726. Doi: 10.1080/00207543.2018.1471240
Djihane, M., Abdelkrim, H., & Dhiya Eddine, C. (2026). UAV association and beam planning in NTN-assisted 5G networks: A TLBO framework. Physical Communication, 74, 102969. Doi: 10.1016/j.phycom.2025.102969
Konstantakopoulos, G. D., Gayialis, S. P., & Kechagias, E. P. (2022). Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification. Operational Research, 22(3), 2033–2062. Doi: 10.1007/s12351-020-00600-7
Krishnamurthy, N. K., Sabhahit, J. N., Jadoun, V. K., Gaonkar, D. N., Shrivastava, A., Rao, V. S., & Kudva, G. (2023). Optimal placement and sizing of electric vehicle charging infrastructure in a grid-tied DC microgrid using modified TLBO method. Energies, 16(4), 1781. Doi: 10.3390/en16041781
Kuntasup, M., Pongcharoen, P., & Thepphakorn, T. (2025). Moth flame optimisation based timetabling tool for educational course timetabling. International Journal of Innovation and Learning, 38(3), 282–300. Doi: 10.1504/IJIL.2025.148422
Lakkamraju, V., Tewari, N., Panigrahi, G. R., Nayak, S. K., Kulkarni, A., & Mortazavi, M. Z. (2026). Utilizing artificial intelligence for solving many-objective construction time–cost–quality–safety risk trade-off optimization problems. Asian Journal of Civil Engineering, 27(7), 3565–3575. Doi: 10.1007/s42107-026-01683-x
Mehroliya, S., & Arya, A. (2024). Distributed generator and capacitor-embedded reconfiguration in three-phase unbalanced distribution systems using teaching–learning-based optimization. Computer Applications in Engineering Education, 32(1), e22689. Doi: 10.1002/cae.22689
Mohammed, M. A., Abd Ghani, M. K., Hamed, R. I., Mostafa, S. A., Ahmad, M. S., & Ibrahim, D. A. (2017). Solving vehicle routing problem by using improved genetic algorithm for optimal solution. Journal of Computational Science, 21, 255–262. Doi: 10.1016/j.jocs.2017.04.003
Niracha, B. (2024). Modified marine predators algorithms for solving capacitated vehicle routing problem: A case study of an LED company (Master’s thesis). Naresuan University, Phitsanulok.
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. Doi: 10.1016/j.cad.2010.12.015
Roy, S., Van Deursen, A., & Hermans, F. (2019). Perceived relevance of automatic code inspection in end-user development: A study on VBA. In Proceedings of the Evaluation and Assessment in Software Engineering Conference (pp. 167–176). New York, NY: ACM. Doi: 10.1145/3319008.3319028
Sooncharoen, S., Pongcharoen, P., & Hicks, C. (2020). Grey wolf production scheduling for the capital goods industry. Applied Soft Computing, 94, 106480. Doi: 10.1016/j.asoc.2020.106480
Stamadianos, T., Taxidou, A., Marinaki, M., & Marinakis, Y. (2024). Swarm intelligence and nature-inspired algorithms for solving vehicle routing problems: A survey. Operational Research, 24(3), 47. Doi: 10.1007/s12351-024-00862-5
Sun, X., Dai, X., Pan, S., Bao, N., Liu, N., & Shi, X. (2022). A discrete teaching–learning-based optimization algorithm for the green vehicle routing problem. In 2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City, and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications (pp. 1552–1557). IEEE.
Thepphakorn, T., & Pongcharoen, P. (2023). Modified and hybridised bi-objective firefly algorithms for university course scheduling. Soft Computing, 27(14), 9735–9772. Doi: 10.1007/s00500-022-07810-5
Trachanatzi, D., Rigakis, M., Marinaki, M., & Marinakis, Y. (2024). A teaching–learning-based optimization algorithm for the environmental prize-collecting vehicle routing problem. Energy Systems, 15(4), 1429–1456. Doi: 10.1007/s12667-021-00477-1
Ullah, W., Mu’tasim, M. A. N., & Rashid, M. F. F. (2024). Optimization of cost-based hybrid flowshop scheduling using teaching–learning-based optimization algorithm. International Journal of Automotive and Mechanical Engineering, 21(3), 11616–11628. Doi: 10.15282/ijame.21.3.2024.13.0896
Valdez, F., Melin, P., & Castillo, O. (2014). A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Systems with Applications, 41(14), 6459–6466. Doi: 10.1016/j.eswa.2014.04.015
Venkata Rao, R. (2016). Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decision Science Letters, 5(1), 1–30. Doi: 10.5267/j.dsl.2015.9.003
Vitayasak, S., & Pongcharoen, P. (2020). Cooperative designing of machine layout using teaching–learning-based optimisation and its modifications. In Y. Luo (Ed.), Cooperative design, visualization, and engineering (Lecture Notes in Computer Science, Vol. 12341, pp. 137–147). Cham, Switzerland: Springer. Doi: 10.1007/978-3-030-60816-3_16
Wareepitak, K., Sooncharoen, S., Pongcharoen, P., & Hicks, C. (2026). A modified and hybrid-based optimization tool for solving multi-row, non-identical machine layout problems in multiple-product job shop production environments. The International Journal of Advanced Manufacturing Technology. Advance online publication. Doi: 10.1007/s00170-026-18242-y
Zahedi, F., Kia, H., & Khalilzadeh, M. (2023). A hybrid metaheuristic approach for solving a bi-objective capacitated electric vehicle routing problem with time windows and partial recharging. Journal of Advances in Management Research, 20(4), 695–729. Doi: 10.1108/JAMR-01-2023-0007
