Multi-Objective Hybrid Approach for Solving the Multi-Objective Constrained Electric Vehicle Routing Problem

doi:10.23940/ijpe.25.07.p3.372381

Abstract

Abstract: The Electric Vehicle Routing Problem (EVRP) is a complex combinatorial optimization problem that arises in sustainable transportation logistics. This paper presents the design and implementation of a multi-objective global-global hybrid algorithmic approach to optimize the Constrained Electric Vehicle Routing Problem. The objective of this work is to develop a hybrid multi-objective approach by integrating parameter-free algorithms and Swarm-based Optimization techniques. The performance of the proposed approach evaluated by solving the Multi-Objective constrained Electric Vehicle Routing Problem. The Multi-Objective hybrid approach is designed by integrating MOPSO and the MOJaya algorithm. The proposed approach extends the concept of non-dominated sorting with a ranking scheme. Hypervolume and IGD performance metrics are used to evaluate the performance of the proposed approach. The Multi-Objective constrained Electric Vehicle Routing Problem incorporates unique constraints such as limited battery capacity, charging station availability, and time windows. Since the problem is multi-objective, it seeks to optimize multiple conflicting objectives, including minimizing operational costs, energy consumption, and fleet size. The proposed approach succeeds in obtaining feasible solutions for the selected problem. The results obtained by the multi-objective hybrid PSO-Jaya algorithm are comparatively better than those produced by the multi-objective PSO and multi-objective Jaya algorithm in terms of Hypervolume and IGD values. The proposed approach demonstrates the potential of hybridization of two global search techniques in optimizing the multi-objective problem. The results also encourage further application of this approach to real-time multi-objective optimization problems.

Key words: multi-objective constrained electric vehicle routing problem (EVRP), multi-objective particle swarm optimization (MOPSO), multi-objective Jaya algorithm (MOJaya), multi-objective hybrid Jaya-PSO algorithm, hypervolume, inverted generational distance (IGD)

Tejaswini Patil and S. U. Mane. Multi-Objective Hybrid Approach for Solving the Multi-Objective Constrained Electric Vehicle Routing Problem [J]. Int J Performability Eng, 2025, 21(7): 372-381.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Deb K.,2001. Multi-objective optimization using evolutionary algorithms John Wiley & Sons. Inc., New York, NY.
[2] Deb K.,2011. Multi-objective optimization using evolutionary algorithms: an introduction. In Multi-Objective Evolutionary Optimization for Product Design and Manufacturing, pp. 3-34.
[3] Rajesh K., Jain E., andKotecha P., 2022. A multi-objective approach to the electric vehicle routing problem. Arxiv Preprint Arxiv:2208.12440.
[4] Vani V., Ahmad W., andREDDY B., 2023. Multi-objective optimization of electric vehicle routing problem using bat algorithm.
[5] Cai W., Zhang Y., Huang F., andMa C., 2023. Delivery routing problem of pure electric vehicle with multi-objective pick-up and delivery integration. Plos One, 18(2), e0281131.
[6] Stamadianos T., Kyriakakis N.A., Marinaki M., andMarinakis Y., 2023. Routing problems with electric and autonomous vehicles: review and potential for future research. In Operations Research Forum(Vol. 4, No. 2, 46.
[7] Cataldo-Díaz C., Linfati R., andEscobar J.W., 2024. Mathematical models for the electric vehicle routing problem with time windows considering different aspects of the charging process. Operational Research, 24(1), 1.
[8] Dastpak M., Errico F., Jabali O., andMalucelli F., 2024. Dynamic routing for the electric vehicle shortest path problem with charging station occupancy information. Transportation Research Part C: Emerging Technologies, 158, 104411.
[9] Singgih I.K., andSinggih M.L., 2024. Regression machine learning models for the short-time prediction of genetic algorithm results in a vehicle routing problem. World Electric Vehicle Journal, 15(7), 308.
[10] Amiri A.,2022. Electric Vehicle Routing Problem and Solution Approaches(Doctoral dissertation, Toronto Metropolitan University).
[11] Asín-Achá R., Goldschmidt O., Hochbaum D.S., andHuerta I., 2022. Fast algorithms for the capacitated vehicle routing problem using machine learning selection of algorithm's parameters. In International Conference on Knowledge Discovery and Information Retrieval, pp. 29-39.
[12] Ðurasevic M., andGala F.J.G., 2024. Automated design of routing policies for the dynamic electric vehicle routing problem with genetic programming. In International Joint Conference on Computational Intelligence.
[13] Rao R.V., Savsani V.J., andVakharia D.P., 2011. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), pp. 303-315.
[14] Rao R.,2016. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), pp. 19-34.
[15] Ingle K.K., andJatoth R.K., 2020. An efficient JAYA algorithm with lévy flight for non-linear channel equalization. Expert Systems with Applications, 145, 112970.
[16] Pulyassary H., Kollias K., Schild A., Shmoys D., andWu M., 2024. Network flow problems with electric vehicles. In International Conference on Integer Programming and Combinatorial Optimization, pp. 365-378.
[17] Kim G.,2024. Electric vehicle routing problem with states of charging stations. Sustainability, 16(8), 3439.
[18] Mesa J.P., Montoya A., Ramos-Pollan R., andToro M., 2025. Machine-learning component for multi-start metaheuristics to solve the capacitated vehicle routing problem. Applied Soft Computing, 173, 112916.
[19] De Andoin M.G., Bottarelli A., Schmitt S., Oregi I., Hauke P., andSanz M., 2023. Formulation of the electric vehicle charging and routing problem for a hybrid quantum-classical search space reduction heuristic. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp. 5318-5323.
[20] Mane S., Narsingrao M., andPatil V., 2018. A many-objective jaya algorithm for many-objective optimization problems. Decis Sci Lett, 7(4), pp. 567-582.
[21] Zhou F., Arvidsson A., Wu J., andKulcsár B., 2024. Collaborative electric vehicle routing with meet points. Communications in Transportation Research, 4, 100135.
[22] Lee Z.J., Lee G., Lee T., Jin C., Lee R., Low Z., Chang D., Ortega C., andLow S.H., 2021. Adaptive charging networks: A framework for smart electric vehicle charging. IEEE Transactions on Smart Grid, 12(5), pp. 4339-4350.
[23] Ma T.Y.,2022. Dynamic charging management for electric vehicle demand responsive transport. In Conference on Sustainable Urban Mobility, pp. 171-182.
[24] Mosalli H., Sanami S., Yang Y., Yeh H.G., andAghdam A.G., 2025. Dynamic load balancing for EV charging stations using reinforcement learning and demand prediction. In 2025 IEEE International Systems Conference (SysCon), pp. 1-7.
[25] Amiri A., Zolfagharinia H., andAmin S.H., 2023. A robust multi-objective routing problem for heavy-duty electric trucks with uncertain energy consumption. Computers & Industrial Engineering, 178, 109108.
[26] Martin X.A., Escoto M., Guerrero A., andJuan A.A., 2024. Battery management in electric vehicle routing problems: a review. Energies, 17(5), 1141.
[27] Mane S., andNarsingrao M.R., 2021. A chaotic-based improved many-objective jaya algorithm for many-objective optimization problems. International Journal of Industrial Engineering Computations, 12(1), pp. 49-62.
[28] Eberhart R., andKennedy J., 1995. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, 4, pp. 1942-1948.
[29] Coello C.A.C., Pulido G.T., andLechuga M.S., 2004. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), pp. 256-279.
[30] Jain M., Saihjpal V., Singh N., andSingh S.B., 2022. An overview of variants and advancements of PSO algorithm. Applied Sciences, 12(17), 8392.
[31] Habib M., Aljarah I., Faris H., andMirjalili S., 2019. Multi-objective particle swarm optimization: theory, literature review, and application in feature selection for medical diagnosis. Evolutionary Machine Learning Techniques: Algorithms and Applications, pp. 175-201.
[32] Zhang Y., Wang S., andJi G., 2015. A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering, 2015(1), 931256.
[33] Lalwani S., Singhal S., Kumar R., andGupta N., 2013. A comprehensive survey: applications of multi-objective particle swarm optimization (MOPSO) algorithm. Transactions on Combinatorics, 2(1), pp. 39-101.
[34] Asha L.N., Dey A., Yodo N., andAragon L.G., 2022. Optimization approaches for multiple conflicting objectives in sustainable green supply chain management. Sustainability, 14(19), 12790.