Algoritmos del orden: el futuro de los horarios universitarios

Guerrero Silva, E. A., Mora Gutiérrez, R. A., & Rincón García, E. (2026). Algoritmos del orden: el futuro de los horarios universitarios. AZCATL Revista De Divulgación En Ciencias, Ingeniería E Innovación , 4(6), 48–54. https://doi.org/10.24275/AZC2026E1007

Resumen

La asignación de horarios en universidades es un problema complejo que requiere coordinar múltiples factores, como la disponibilidad de profesores, aulas y cursos, además de considerar las necesidades de los estudiantes. Tradicionalmente, este proceso se realiza manualmente, lo que implica un alto consumo de tiempo y la posibilidad de errores. Para abordar esta problemática, se han desarrollado diversas soluciones basadas en heurísticas y meta heurísticas, como los algoritmos genéticos, recocido simulado, búsqueda tabú y optimización por enjambre de partículas.

Estos enfoques permiten optimizar la asignación de recursos, reducir conflictos de horarios y mejorar la planificación académica. La aplicación de inteligencia artificial y técnicas de optimización multi-objetivo representa una alternativa prometedora para la gestión eficiente de horarios en instituciones educativas.

https://doi.org/10.24275/AZC2026E1007

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Citas

• Abdipoor, S., Yaakob, R., Goh, S. L. y Adbullah, S. (2023). Meta-heuristic approaches for the university course timetabling problem. Intelligent Systems with Application, 19. https://www.sciencedirect.com/science/article/pii/S2667305323000789

• Chen, M. C., Sze, S. N., Goh, S. L., Sabar, N. R. y G. Kendall (2021). A Survey of University Course Timetabling Problem: Perspectives, Trends and Opportunities. IEEE Access, 9, 106515-106529. https://ieeexplore.ieee.org/document/9499056

• Chen, X., Yue, X. G., Li, R. Y. M., Zhumadillayeva, A. y Liu, R. (2021). Design and Application of an Improved Genetic Algorithm to a Class Scheduling System. International Journal of Emerging.Technologies in Learning, 16(1). https://online-journals.org/index.php/i-jet/article/view/18225

• Cortés, E., Montero, O., Pacheco, D., Sánchez, S. y Aguilar, F. (14-18 de junio de 2021). A genetic algorithm solution for scheduling problem [Conferencia]. 2021 XVII International Engineering Congress (CONIIN), Querétaro, México. https://ieeexplore.ieee.org/document/9634725#citations

• Hossain, Sk. I., Shuvo, M. I. R., Siddique, N. y Adeli. H. (2019). Optimization of university course scheduling problem using particle swarm optimization with selective search. Expert Systems with Applications, 127. 9-24. https://www.sciencedirect.com/science/article/abs/pii/S0957417419301393

• Khelifi, S., Zeghida, D. y Mazouzi, S. (2023). A novel method for solving the university course timetabling problem based on the grey wolf optimizer algorithm [Conferencia]. 2023 Third International Conference on Theoretical and Applicative Aspects of Computer Science, Skikda, Algeria. https://www.researchgate.net/publication/378723306_A_Novel_Method_for_Solving_the_University_Course_Timetabling_Problem_Based_on_the_Grey_Wolf_Optimizer_Algorithm

• Ngo, S. T., Jaafar, J. B., Azizand, I. A., Nguyenand, G. H. Bui, A. N. (2021). Genetic algorithm for solving multi-objective optimization in examination timetabling problem. International Journal of Emerging. Technologies in Learning, 16 (11). https://online-journals.org/index.php/i-jet/article/view/21017

• Rahman, R. A., Wahid, J., Wahaband, A. A., Hassanand, S., Ahmadand, R. y Ahmad, A. (2022). Systematic literature review of metaheuristic methodologies for high school timetabling problem [Conferencia]. 2022 Applied Informatics International Conference (AiIC), Serdang, Malasia. https://ieeexplore.ieee.org/document/9914034

• Sun, Z. y Wu, Q. (2023). Two-phase tabu search algorithm for solving chinese high school timetabling problems under the new college entrance examination reform. Data Science and Management, 6(1), 55-63. https://www.sciencedirect.com/science/article/pii/S2666764923000073

• Sylejmani, K., Gashi, E. y Ymeri, A. (2023). Simulated annealing with penalization for university course timetabling. Journal of Scheduling (5). https://www.springerprofessional.de/en/simulated-annealing-with-penalization-for-university-course-time/23289402

• Wen-jing, W. (2018). Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities. International Journal of Emerging.Technologies in Learning, 13(6). https://online-journals.org/index.php/i-jet/article/view/8442

• Widayu, U. R. K., Mukhlason, A. y Nurkasanah, I. (2021). Automation and optimization of course timetabling using the iterated local search hyper heuristic algorithm with the problem domain from the 2019 international timetabling competition [Conferencia]. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), Surabaya, Indonesia. https://www.researchgate.net/publication/351646562_Automation_and_Optimization_of_Course_Timetabling_Using_the_Iterated_Local_Search_Hyper-Heuristic_Algorithm_with_the_Problem_Domain_from_the_2019_International_Timetabling_Competition

• Wu, L. (2015). The application of coarse-grained parallel genetic algorithm with hadoop in university intelligent course-timetabling system. International Journal of Emerging. Technologies in Learning, 10(8). https://online-journals.org/index.php/i-jet/article/view/5206

• Yazdani, M., Naderi, B. y Zeinali, E. (2017). Algorithms for university course scheduling problems. Tehnički Vjesnik, 24(2), pp. 241-247. https://hrcak.srce.hr/file/274394

• Zhang, D., Liu, Y., Hallah, R. M. y Leung, S. C. H. (2011). A simulated annealing algorithm with a new neighborhood structure for the timetabling problem. European Journal of Operational Research, 203(3), 550-558. https://www.sciencedirect.com/science/article/abs/pii/S0377221709006055