Modelos de Programación Entera para el Problema de Aignación de Horarios en Cursos Universitarios

Autores/as

DOI:

https://doi.org/10.46661/rev.metodoscuant.econ.empresa.7354

Palabras clave:

Programación de Horarios, programación entera, asignación de cursos

Resumen

La programación de horarios se clasifica como un problema combinatorio para el que existen múltiples alternativas de solución incluyendo entre ellas la programación entera. Sin embargo, el modelado de reglas operativas y el tamaño de problemas reales hace que su uso no sea común comparado con otras técnicas. El presente artículo propone un modelo de programación entera (IP), que aborda el problema de programación de horarios conformado por variables asociadas con franjas horarias, asignatura y docente asignado. También se incluyen parámetros como número de franjas horarias mínimas y máximas a impartir por el docente, tiempo de franja horaria, disponibilidad de docente, salones disponibles y costo estimado de insatisfacción generado por el horario asignado. En el modelo se integran siete restricciones duras y diecisiete blandas que proporcionan mayor calidad a la solución final de horarios. Se valida el modelo IP con una función objetivo global, en el que se reportan experimentos y resultados obtenidos en instancias reales de la Universidad de la Salle (ULS). El nuevo enfoque de solución ofrece mejoras en los horarios finales, así como la interacción con los usuarios durante su construcción. Finalmente, en las conclusiones del trabajo se discute el diseño y desarrollo de un sistema que brinda soporte a las decisiones, referenciando sugerencias para futuros desarrollos.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Alnowaini, G. & Aljomai, A. A. (2021). Genetic Algorithm For Solving University Course Timetabling Problem Using Dynamic Chromosomes. 2021 International Conference of Technology, Science and Administration (ICTSA), 1-6. https://doi.org/10.1109/ICTSA52017.2021.9406539

Al-Yakoob, S. M. & Sherali, H. D. (2015). Mathematical models and algorithms for a high school timetabling problem. Computers & Operations Research, 61, 56-68. https://doi.org/10.1016/j.cor.2015.02.011

Asmuni, H. (2008). Fuzzy Methodologies for Automated University Timetabling Solution Construction and Evaluation [PhD Thesis]. University of Nottingham.

Assi, M., Halawi, B. & Haraty, R. A. (2018). Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem. Procedia Computer Science, 126, 899-906. https://doi.org/10.1016/j.procs.2018.08.024

Babaei, H., Karimpour, J. & Hadidi, A. (2015). A survey of approaches for university course timetabling problem. Computers & Industrial Engineering, 86, 43-59. https://doi.org/10.1016/j.cie.2014.11.010

Barnhart, C., Lu, F. & Shenoi, R. (1998). Integrated Airline Schedule Planning. En G. Yu (Ed.), Operations Research in the Airline Industry (Vol. 9, pp. 384-403). Springer US. https://doi.org/10.1007/978-1-4615-5501-8_13

Battiti, R. & Tecchiolli, G. (1994). The Reactive Tabu Search. ORSA Journal on Computing, 6(2), 126-140. https://doi.org/10.1287/ijoc.6.2.126

Brucker, P., Drexl, A., Möhring, R., Neumann, K. & Pesch, E. (1999). Resource-constrained project scheduling: Notation, classification, models, and methods. European Journal of Operational Research, 112(1), 3-41. https://doi.org/10.1016/S0377-2217(98)00204-5

Castillo-Salazar, J. A., Landa-Silva, D. & Qu, R. (2016). Workforce scheduling and routing problems: Literature survey and computational study. Annals of Operations Research, 239(1), 39-67. https://doi.org/10.1007/s10479-014-1687-2

Ceschia, S., Di Gaspero, L. & Schaerf, A. (2012). Design, engineering, and experimental analysis of a simulated annealing approach to the post-enrolment course timetabling problem. Computers & Operations Research, 39(7), 1615-1624. https://doi.org/10.1016/j.cor.2011.09.014

Daskalaki, S., Birbas, T. & Housos, E. (2004). An integer programming formulation for a case study in university timetabling. European Journal of Operational Research, 153(1), 117-135. https://doi.org/10.1016/S0377-2217(03)00103-6

de Palma, A. & Lindsey, R. (2001). Optimal timetables for public transportation. Transportation Research Part B: Methodological, 35(8), 789-813. https://doi.org/10.1016/S0191-2615(00)00023-0

de Werra, D. (1985). An introduction to timetabling. European Journal of Operational Research, 19(2), 151-162. https://doi.org/10.1016/0377-2217(85)90167-5

Dimopoulou, M. & Miliotis, P. (2001). Implementation of a university course and examination timetabling system. European Journal of Operational Research, 130(1), 202-213. https://doi.org/10.1016/S0377-2217(00)00052-7

Domenech, B. & Lusa, A. (2016). A MILP model for the teacher assignment problem considering teachers’ preferences. European Journal of Operational Research, 249(3), 1153-1160. https://doi.org/10.1016/j.ejor.2015.08.057

Feizi-Derakhshi, M.-R., Babaei, H. & Heidarzadeh, J. (2012, septiembre 27). A Survey of Approaches for University Course Timetabling Problem. Proceedings of 8th International Symposium on Intelligent and Manufacturing Systems (IMS 2012).

Feng, X., Lee, Y. & Moon, I. (2017). An integer program and a hybrid genetic algorithm for the university timetabling problem. Optimization Methods and Software, 32(3), 625-649. https://doi.org/10.1080/10556788.2016.1233970

Ghalia, M. B. (2008). Particle swarm optimization with an improved exploration-exploitation balance. 2008 51st Midwest Symposium on Circuits and Systems, 759-762. https://doi.org/10.1109/MWSCAS.2008.4616910

Goh, S. L., Kendall, G. & Sabar, N. R. (2019). Simulated annealing with improved reheating and learning for the post enrolment course timetabling problem. Journal of the Operational Research Society, 70(6), 873-888. https://doi.org/10.1080/01605682.2018.1468862

Gotlieb, C. C. (1962). The Construction of Class-Teacher Time-Tables. Information Processing, Proceedings of the 2nd IFIP Congress 1962, Munich, Germany, August 27 - September 1, 1962, 73-77.

Gülcü, A. & Akkan, C. (2020). Robust university course timetabling problem subject to single and multiple disruptions. European Journal of Operational Research, 283(2), 630-646. https://doi.org/10.1016/j.ejor.2019.11.024

Hung, R. & Emmons, H. (1993). Multiple-shift Workforce Scheduling under the 3-4 compressed workweek with a Hierarchical Workforce. IIE Transactions, 25(5), 82-89. https://doi.org/10.1080/07408179308964318

Ismayilova, N. A., Sağir, M. & Gasimov, R. N. (2007). A multiobjective faculty–course–time slot assignment problem with preferences. Mathematical and Computer Modelling, 46(7-8), 1017-1029. https://doi.org/10.1016/j.mcm.2007.03.012

K. Alsmadi, M., M. Jaradat, G., Alzaqebah, M., ALmarashdeh, I., A. Alghamdi, F., Mustafa A. Mohammad, R., Aldhafferi, N. & Alqahtani, A. (2022). An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling. Computers, Materials & Continua, 72(1), 1995-2014. https://doi.org/10.32604/cmc.2022.025077

LaGanga, L. R. & Lawrence, S. R. (2012). Appointment Overbooking in Health Care Clinics to Improve Patient Service and Clinic Performance. Production and Operations Management, 21(5), 874-888. https://doi.org/10.1111/j.1937-5956.2011.01308.x

Lewis, R. (2012). A time-dependent metaheuristic algorithm for post enrolment-based course timetabling. Annals of Operations Research, 194(1), 273-289. https://doi.org/10.1007/s10479-010-0696-z

McCollum, B., McMullan, P., Parkes, A. J., Burke, E. K. & Qu, R. (2012). A new model for automated examination timetabling. Annals of Operations Research, 194(1), 291-315. https://doi.org/10.1007/s10479-011-0997-x

Nagata, Y. (2018). Random partial neighborhood search for the post-enrollment course timetabling problem. Computers & Operations Research, 90, 84-96. https://doi.org/10.1016/j.cor.2017.09.014

Nandhini, V. (2019). A Study on Course Timetable Scheduling and Exam Timetable Scheduling using Graph Coloring Approach. International Journal for Research in Applied Science and Engineering Technology, 7(3), 1999-2006. https://doi.org/10.22214/ijraset.2019.3368

Neumann, K., Schwindt, C. & Zimmermann, J. (2003). Project Scheduling with Time Windows and Scarce Resources: Temporal and Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions. Springer Berlin Heidelberg.

https://doi.org/10.1007/978-3-540-24800-2_3

Nothegger, C., Mayer, A., Chwatal, A. & Raidl, G. R. (2012). Solving the post enrolment course timetabling problem by ant colony optimization. Annals of Operations Research, 194(1), 325-339. https://doi.org/10.1007/s10479-012-1078-5

Ribić, S. & Konjicija, S. (2010, junio). A two phase integer linear programming approach to solving the school timetable problem | IEEE Conference Publication | IEEE Xplore. Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces. https://ieeexplore.ieee.org/abstract/document/5546473?casa_token=8NXEYLyhBJYAAAAA:DO9jDMq4-wyY2YxPPgfUUOGBO1n4ELugtWtLZzG3WTMrYB09TAvcnX6Nkr2N0KKYHvBoQ7CY0Ect7VU

Saldaña Crovo, A., Oliva San Martín, C. & Pradenas Rojas, L. (2007). Modelos de Programación Entera para un Problema de Programación de Horarios para Universidades. Ingeniare. Revista Chilena de Ingeniería, 15(3). https://doi.org/10.4067/S0718-33052007000300005

Scheepmaker, G. M., Goverde, R. M. P. & Kroon, L. G. (2017). Review of energy-efficient train control and timetabling. European Journal of Operational Research, 257(2), 355-376. https://doi.org/10.1016/j.ejor.2016.09.044

Selim, S. M. (1988). Split Vertices in Vertex Colouring and Their Application in Developing a Solution to the Faculty Timetable Problem. The Computer Journal, 31(1), 76-82. https://doi.org/10.1093/comjnl/31.1.76

Shakibaei, S., Alpkokin, P. & Black, J. A. (2021). A multi-objective optimisation model for train scheduling in an open-access railway market. Transportation Planning and Technology, 44(2), 176-193. https://doi.org/10.1080/03081060.2020.1868085

Sörensen, K. & Glover, F. W. (2013). Metaheuristics. En S. I. Gass & M. C. Fu (Eds.), Encyclopedia of Operations Research and Management Science (pp. 960-970). Springer US. https://doi.org/10.1007/978-1-4419-1153-7_1167

Tan, J. S., Goh, S. L., Kendall, G. & Sabar, N. R. (2021). A survey of the state-of-the-art of optimisation methodologies in school timetabling problems. Expert Systems with Applications, 165, 113943. https://doi.org/10.1016/j.eswa.2020.113943

Torres-Ovalle, C., Montoya-Torres, J. R., Quintero-Araujo, C., Sarmiento Lepesqueur, A. & Castilla Luna, M. (2014). University Course Scheduling and Classroom Assignment. Ingenieria y Universidad, 18(1), 59-76. https://doi.org/10.11144/Javeriana.IYU18-1.phaa

Vermuyten, H., Lemmens, S., Marques, I. & Beliën, J. (2016). Developing compact course timetables with optimized student flows. European Journal of Operational Research, 251(2), 651-661. https://doi.org/10.1016/j.ejor.2015.11.028

Wang, P. & Goverde, R. M. P. (2019). Multi-train trajectory optimization for energy-efficient timetabling. European Journal of Operational Research, 272(2), 621-635. https://doi.org/10.1016/j.ejor.2018.06.034

Welsh, D. J. A. (1967). An upper bound for the chromatic number of a graph and its application to timetabling problems. The Computer Journal, 10(1), 85-86. https://doi.org/10.1093/comjnl/10.1.85

Wirasinghe, S. C. (2002). Initial Planning for Urban Transit Systems. En W. H. K. Lam & M. G. H. Bell (Eds.), Advanced Modeling for Transit Operations and Service Planning (pp. 1-29). Emerald Group Publishing Limited. https://doi.org/10.1108/9780585475226-001

Zacharias, C. & Pinedo, M. (2014). Appointment Scheduling with No-Shows and Overbooking. Production and Operations Management, 23(5), 788-801. https://doi.org/10.1111/poms.12065

Zhu, K., Li, L. D. & Li, M. (2021). School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method [Preprint]. Mathematics and Computer Science. https://doi.org/10.20944/preprints202111.0215.v1

Publicado

2023-11-14

Cómo citar

Arango, C. A., Felizzola, H., Hualpa, A., Gomez, P. ., & Mora, C. . (2023). Modelos de Programación Entera para el Problema de Aignación de Horarios en Cursos Universitarios. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 36. https://doi.org/10.46661/rev.metodoscuant.econ.empresa.7354

Número

Sección

Artículos