A transdisciplinary approach to course timetabling an optimal comprehensive campus application
Federico Trigos, Roberto Coronel
Abstract
Academic timetabling is a core process of higher education institutions (HEIs) with profound implications for stakeholder satisfaction and organizational efficiency. This process serves as the operational backbone of every HEI. It involves the allocation of professors to course-sections, schedules, and facilities such as classrooms and laboratories for a specific academic term. Given its mathematical complexity (NP-Hard) and despite the extensive literature on timetabling practices, real-world applications often present challenges that deviate from standard problem formulations, creating a gap between theory and practice. This research addresses these challenges by promoting knowledge integration and improving decision-making processes. The contribution of this article is twofold: Firstly, it introduces a transdisciplinary framework that considers stakeholder preferences and available organizational resources to optimize the expected academic performance of HEIs. At the core of this framework lies a quantitative decision support tool (based on a mixed integer optimization model) designed to bridge knowledge and resource gaps, thus aiding the decision-making team in achieving their objectives. Secondly, the article presents a practical demonstration of this framework using data from an entire HEI Campus, encompassing all schools and academic programs, to illustrate its efficiency and benefits.
Keywords
References
Abdullah, S., Muthusamy, M., & Kaur, H. (2015). An improved genetic algorithm for university course timetabling problems. Procedia Computer Science, 76, 470-475.
Bertsekas, D. (1995). The role of mathematical models in decision making. IEEE Transactions on Automatic Control, 40(11), 1813-1825.
Bettinelli, A., Cacchiani, V., Roberti, R., & Toth, P. (2015). An overview of curriculum-based course timetabling. Top (Madrid), 23, 313-349.
Burke, E. K., & Petrovic, S. (2002). Recent research directions in automated timetabling. European Journal of Operational Research, 140(2), 266-280.
Burke, E., Kingston, J., & De Werra, D. (2004). Applications to timetabling. In J. L. Gross & Y. Yellen. Handbook of graph theory (Vol. 445, p. 4). Boca Raton: CRC Press.
Ceschia, S., Di Gaspero, L., & Schaerf, A. (2023). Educational timetabling: problems, benchmarks, and state-of-the-art results. European Journal of Operational Research, 308(1), 1-18.
Chen, M. C., Goh, S. L., Sabar, N. R., & Kendall, G. (2021). A survey of university course timetabling problem: perspectives, trends and opportunities. IEEE Access, vol. 9, pp. 106515-106529.
Choi, B., & Pak, A. (2008). Transdisciplinarity in the classroom and beyond: a review of the challenges and promise of integrating multiple academic perspectives. Ecosystem Health, 4(1), 31-37.
Clark, B. R. (1986). The higher education system: academic organization in cross-national perspective. Berkeley: University of California Press.
Eleyan, D., Arnaout, J. P., & Diabat, A. (2018). A comprehensive survey of course timetabling in universities. Computers & Industrial Engineering, 117, 253-265.
Franco Sánchez, V. (2020). Pre-processing techniques for Integer Linear Programming (Bachelor's thesis). Universitat Politècnica de Catalunya, Barcelona. Retrieved in 2023, December 5, from https://upcommons.upc.edu/bitstream/handle/2117/339357/151791.pdf
Froese, A. D., Gantz, B. S., & Henry, A. L. (1998). Teaching students to write literature reviews: a meta-analytic model. Teaching of Psychology, 25(2), 102-105.
Gooding, H., Lattanzio, S., Parry, G., Newnes, L., & Alpay, E. (2023). Characterising the transdisciplinary research approach. Product: Management and Development, 20(2), e20220012. https://doi.org/10.4322/pmd.2022.024.
Holmes, R. C., Zhang, Z., Mallison, B., Saldaña, S., Francis, J., & Moser, B. R. (2022). Prioritization of Digital Innovation Team projects using a utility model and tradespace analysis. Product: Management and Development, 20(1), e20220010. https://doi.org/10.4322/pmd.2022.022.
ILOG Cplex. (2023). Pre-processing data. Retrieved in 2023, December 5, from https://www.ibm.com/docs/en/icos/20.1.0?topic=sources-preprocessing-data
Kheiri, A., Keedwell, E., & Gheorghe, M. (2018). A novel algorithm for the university exam timetabling problem using an immune algorithm with a new feasible individual generation mechanism. Applied Soft Computing, 64, 440-453.
Kingston, J. H. (2022). Timetabling research: a progress report. Retrieved in 2023, December 5, from https://patatconference.org/patat2022/proceedings/PATAT_2022_paper_23.pdf
MirHassani, S. A., & Habibi, F. (2013). Solution approaches to the course timetabling problem. Artificial Intelligence Review, 39, 133-149.
Mokhtari, M., Vaziri Sarashk, M., Asadpour, M., Saeidi, N., & Boyer, O. (2021). Developing a model for the university course timetabling problem: a case study. Complexity, 2021, 9940866.
Muklason, A., Parkes, A. J., Özcan, E., McCollum, B., & McMullan, P. (2017). Fairness in examination timetabling: student preferences and extended formulations. Applied Soft Computing, 55, 302-318.
Nurmi, K., & Kyngäs, J. (2008). A conversion scheme for turning a curriculum-based timetabling problem into a school timetabling problem. In Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2008). PATAT. Retrieved in 2023, December 5, from https://patatconference.org/patat2008/proceedings/Nurmi-TC1a.pdf
Özcan, E., Bilgin, B., & Yilmaz, T. (2013). A simulated annealing algorithm with a new neighborhood structure for university exam scheduling. Applied Soft Computing, 13(8), 3593-3602.
Özcan, E., De Causmaecker, P., & Berghe, G. V. (2022). Advances in the practice and theory of automated timetabling. Journal of Scheduling, 25(3), 259-259.
Pillay, N. (2014). A survey of school timetabling research. Annals of Operations Research, 218, 261-293.
Qu, R., & Burke, E. K. (2009). Bridging the gap: from theoretical models to practical solutions in university course timetabling. The Journal of the Operational Research Society, 60(10), 1345-1355.
Qu, R., Burke, E. K., McCollum, B., Merlot, L. T., & Lee, S. Y. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12, 55-89.
Sabin, G., & Winter, G. (1986). The impact of automated timetabling on universities: a case study. The Journal of the Operational Research Society, 37(7), 689-693.
Schaerf, A. (1999). A survey of automated timetabling. Artificial Intelligence Review, 13, 87-127.
Selimi, S., Arbneshi, L., Sylejmani, K., & Musliu, N. (2022). Iterated Local Search for the examination timetabling problem with constructive-based initial solution. In Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling-PATAT (Vol. 3). PATAT.
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.
Timetabling PMD 2023. (2023). Retrieved in 2023, December 5, from https://www.dropbox.com/scl/fo/boyjguiy9x0qh60ten2a6/h?rlkey=3qmgbar4b4encm4konpbct42k&dl=0
Trigos, F. (2002). Sobre la reducción de modelos de programación lineal. Métodos Numéricos en Ingeniería y Ciencias Aplicadas, 1, 409-418.
Trigos, F., & Coronel, R. (2023). A transdisciplinary approach to the academic timetabling problem. Advances in Transdisciplinary Engineering, Vol. 44, pp. 591-600. Amsterdam: IOS Press. https://doi.org/10.3233/ATDE230654.
Van Bulck, D., & Goossens, D. (2023). The international timetabling competition on sports timetabling (ITC2021). European Journal of Operational Research, 308(3), 1249-1267.
Wren, A. (1995). Scheduling, timetabling and rostering a special relationship? In International Conference on the Practice and Theory of Automated Timetabling (pp. 46-75). USA: Springer.
Submitted date:
09/09/2023
Accepted date:
12/06/2023
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