Algoritmos genéticos en la asignación de recursos y optimización de rutas en transporte: revisión sistemática

Autores/as

DOI:

https://doi.org/10.36825/RITI.14.34.005

Palabras clave:

Algoritmos Genéticos, Asignación de Recursos, Optimización de Rutas, Transporte, Revisión Sistemática

Resumen

La programación operativa en empresas de transporte implica la asignación eficiente de vehículos, conductores, rutas y horarios, un problema de optimización combinatoria de alta complejidad cuando se incorporan restricciones de capacidad, ventanas de tiempo, costos, emisiones y variabilidad de la demanda. En este contexto, los algoritmos genéticos (AG) y sus variantes híbridas se han empleado como metaheurísticas para obtener soluciones de buena calidad en tiempos computacionales razonables. Esta revisión sistemática, desarrollada según la metodología de Kitchenham y reportada mediante un diagrama de flujo adaptado de PRISMA 2020, tuvo como objetivo identificar los tipos de problemas de asignación de recursos y optimización de rutas abordados con AG, las variantes predominantes, los resultados cuantitativos reportados y las principales limitaciones. Se revisaron artículos publicados entre 2022 y 2026 en ScienceDirect, Springer Nature Link, IEEE Xplore, MDPI y otras plataformas editoriales con DOI verificable; la selección final comprendió 31 estudios. Los resultados muestran un predominio del Vehicle Routing Problem y sus variantes (80.65 %), junto con una presencia destacada de AG híbridos, estrategias multiobjetivo y operadores adaptativos. Se concluye que los AG son pertinentes para la programación operativa del transporte, aunque persisten desafíos de parametrización, escalabilidad, convergencia prematura y validación en escenarios reales.

Citas

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. https://doi.org/10.1007/s12351-020-00600-7

Elshaer, R., Awad, H. (2020). A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Computers & Industrial Engineering, 140. https://doi.org/10.1016/j.cie.2019.106242

Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press.

Vidal, T. (2022). Hybrid genetic search for the CVRP: Open-source implementation and SWAP* neighborhood. Computers and Operations Research, 140, 1-11. https://doi.org/10.1016/j.cor.2021.105643

Zambrano Miranda, J. A., Correa Pillajo, J. E., Grijalva Arévalo, F. L., Vega Sánchez, J. D. (2022). Selección de funciones de voz mediante algoritmos genéticos para la detección de la enfermedad de Parkinson. Revista de Investigación en Tecnologías de la Información (RITI), 10 (21), 140–150. https://doi.org/10.36825/RITI.10.21.013

Kitchenham, B., Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering (EBSE Technical Report EBSE-2007-01). Keele University / University of Durham. https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, 1-9. https://doi.org/10.1136/bmj.n71

Cai, Y., Cheng, M., Zhou, Y., Liu, P., Guo, J. M. (2022). A hybrid evolutionary multitask algorithm for the multiobjective vehicle routing problem with time windows. Information Sciences, 612, 168–187. https://doi.org/10.1016/j.ins.2022.08.103

Zhang, W., Li, H., Yang, W., Zhang, G., Gen, M. (2022). Hybrid multiobjective evolutionary algorithm considering combination timing for multi-type vehicle routing problem with time windows. Computers & Industrial Engineering, 171. https://doi.org/10.1016/j.cie.2022.108435

Hou, D., Fan, H., Ren, X. (2022). Dynamic multi-depot multi-compartment refrigerated vehicle routing problem with multi-path based on real-time traffic information. En Advances in Transdisciplinary Engineering (349-356). IOS Press. https://doi.org/10.3233/ATDE220035

Xin, L., Xu, P., Manyi, G. (2022). Logistics distribution route optimization based on genetic algorithm. Computational Intelligence and Neuroscience, 2022 (1), 1-9. https://doi.org/10.1155/2022/8468438

Agrawal, A. K., Yadav, S., Gupta, A. A., Pandey, S. (2022). A genetic algorithm model for optimizing vehicle routing problems with perishable products under time-window and quality requirements. Decision Analytics Journal, 5, 1-14. https://doi.org/10.1016/j.dajour.2022.100139

Liu, Q., Xu, P., Wu, Y., Shen, T. (2022). A hybrid genetic algorithm for the electric vehicle routing problem with time windows. Control Theory and Technology, 20 (2), 279-286. https://doi.org/10.1007/s11768-022-00091-1

Simensen, M., Hasle, G., Stålhane, M. (2022). Combining hybrid genetic search with ruin-and-recreate for solving the capacitated vehicle routing problem. Journal of Heuristics, 28, 653-697. https://doi.org/10.1007/s10732-022-09500-9

Chen, C. M., Lv, S., Ning, J., Wu, J. M. T. (2023). A genetic algorithm for the waitable time-varying multi-depot green vehicle routing problem. Symmetry, 15 (1), 1-26. https://doi.org/10.3390/sym15010124

Liu, Y., Qin, Z., Liu, J. (2023). An improved genetic algorithm for the granularity-based split vehicle routing problem with simultaneous delivery and pickup. Mathematics, 11 (15), 1-15. https://doi.org/10.3390/math11153328

Wang, C., Ma, B., Sun, J. (2023). A co-evolutionary genetic algorithm with knowledge transfer for multi-objective capacitated vehicle routing problems. Applied Soft Computing, 148. https://doi.org/10.1016/j.asoc.2023.110913

Labidi, H., Ben Azzouna, N., Hassine, K., Gouider, M. S. (2023). An improved genetic algorithm for solving the multi-objective vehicle routing problem with environmental considerations. Procedia Computer Science, 225, 3866-3875. https://doi.org/10.1016/j.procs.2023.10.382

Rocha, Y., Subramanian, A. (2023). Hybrid genetic search for the traveling salesman problem with hybrid electric vehicle and time windows. Computers & Operations Research, 155. https://doi.org/10.1016/j.cor.2023.106223

Rezaei, B., Guimaraes, F. G., Enayatifar, R., Haddow, P. C. (2023). Combining genetic local search into a multi-population Imperialist Competitive Algorithm for the Capacitated Vehicle Routing Problem. Applied Soft Computing, 142. https://doi.org/10.1016/j.asoc.2023.110309

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. https://doi.org/10.1108/JAMR-01-2023-0007

Ouyang, W., Zhu, X. (2023). Meta-heuristic solver with parallel genetic algorithm framework in airline crew scheduling. Sustainability, 15 (2), 1-21. https://doi.org/10.3390/su15021506

Zachariah, B., Misra, S., Odion, P. O., Isah, S. R. (2023). MRDPGA: a multiple restart dynamic population genetic algorithm for scheduling road traffic. Journal of Electrical Systems and Information Technology, 10 (35), 1-18. https://doi.org/10.1186/s43067-023-00099-w

Mrad, M., Bamatraf, K., Alkahtani, M., Hidri, L. (2023). A genetic algorithm for the integrated warehouse location, allocation and vehicle routing problem in a pooled transportation system. International Journal of Industrial Engineering: Theory, Applications and Practice, 30 (3), 852-857. https://doi.org/10.23055/ijietap.2023.30.3.8989

Zhao, W., Bian, X., Mei, X. (2024). An adaptive multi-objective genetic algorithm for solving heterogeneous green city vehicle routing problem. Applied Sciences, 14 (15), 1-16. https://doi.org/10.3390/app14156594

Cheng, F., Jia, S. (2024). Improved GA-LNS algorithm for solving vehicle path problems considering carbon emissions. Applied Sciences, 14 (21), 1-17. https://doi.org/10.3390/app14219956

Lee, S. J., Kim, B. S. (2024). Vehicle routing and scheduling problem with order acceptance for pharmaceutical refrigerated logistics. Applied Soft Computing, 164. https://doi.org/10.1016/j.asoc.2024.111983

Sharma, H., Galván, E., Mooney, P. (2024). A parallel genetic algorithm for multi-criteria path routing on complex real-world road networks. Applied Soft Computing, 170. https://doi.org/10.1016/j.asoc.2024.112559

Malashin, I. P., Tynchenko, V., Masich, I. S., Sukhanov, D. A., Ageev, D. A., Nelyub, V. A., Gantimurov, A. P., Borodulin, A. S. (2024). Two-stage genetic algorithm for optimization logistics network for groupage delivery. Applied Sciences, 14 (24), 1-20. https://doi.org/10.3390/app142412005

Liu, J., Tong, L., Xia, X. (2024). A genetic algorithm for vehicle routing problems with time windows based on cluster of geographic positions and time windows. Applied Soft Computing, 169. https://doi.org/10.1016/j.asoc.2024.112593

Zhao, J., Poon, M., Tan, V. Y. F., Zhang, Z. (2024). A hybrid genetic search and dynamic programming-based split algorithm for the multi-trip time-dependent vehicle routing problem. European Journal of Operational Research, 317 (3), 921-935. https://doi.org/10.1016/j.ejor.2024.04.011

Su, Y., Zhang, S., Zhang, C. (2024). A lightweight genetic algorithm with variable neighborhood search for multi-depot vehicle routing problem with time windows. Applied Soft Computing, 161. https://doi.org/10.1016/j.asoc.2024.111789

Li, J., Liu, R., Wang, R. (2024). Handling dynamic capacitated vehicle routing problems based on adaptive genetic algorithm with elastic strategy. Swarm and Evolutionary Computation, 86. https://doi.org/10.1016/j.swevo.2024.101529

Jamshidi, M., Alirezanejad, M., Motameni, H., Enayatifar, R. (2025). A parallel hybrid genetic search for solving the capacitated vehicle routing problem. International Journal of Computational Intelligence Systems, 18 (1), 1-19. https://doi.org/10.1007/s44196-025-01059-0

Latorre, V. (2025). A hybrid genetic search based approach for the generalized vehicle routing problem. Soft Computing, 29, 1553-1566. https://doi.org/10.1007/s00500-025-10507-0

Hvattum, L. M. (2025). Where to split in hybrid genetic search for the capacitated vehicle routing problem. Algorithms, 18 (3), 1-19. https://doi.org/10.3390/a18030165

Miao, Y., Bao, X. (2025). An improved genetic algorithm for solving the semi-soft clustered vehicle routing problem. Applied Sciences, 15 (9), 1-17. https://doi.org/10.3390/app15094871

Du, H., Tanimizu, Y., Watanabe, R. (2026). A hybrid constructive genetic algorithm (HCGA) for the vehicle routing problem with time windows in logistics optimization. IEEE Access, 14, 40532-40549. https://doi.org/10.1109/ACCESS.2026.3672634

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Publicado

2026-09-04

Cómo citar

Rios Sepulveda, L. E., & Sanchez Calderon, Y. (2026). Algoritmos genéticos en la asignación de recursos y optimización de rutas en transporte: revisión sistemática. Revista De Investigación En Tecnologías De La Información, 14(34), 60–74. https://doi.org/10.36825/RITI.14.34.005

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