A COURSE TIMETABLING FORMULATION UNDER PANDEMIC CONSIDERATIONS

Öz

The covid-19 pandemic has significantly affected the education sector and raised the new operational challenges that spring from the necessity of planning courses in a way to ensure community safety. Accordingly, many higher education institutions provide guidelines regarding the measures to be implemented in education operations. As such, timetabling of the courses is required to be carried out in line with these guidelines. To address this need, this study introduces a multi-objective mathematical model for a university course timetabling problem under the pandemic-related considerations. In particular, the proposed mathematical model aims to minimize the total number of online courses and sections while considering the balanced distribution of the courses over time slots and days. We test the effectiveness of the proposed model using real-life data. The results show that the proposed approach is able to create an optimal timetable in a reasonable time that addresses the objectives of the faculty administration and satisfies the pandemic-related requirements. The study contributes to the literature by introducing new pandemic constraints and inspires managers facing pandemic guidelines.

Anahtar Kelimeler

• University Course Timetabling
• Pandemic
• Hybrid Education
• Multi-Objective Mathematical Model

PANDEMİ KOŞULLARI ALTINDA DERS ÇİZELGEMESİ FORMÜLASYONU

Öz

Covid-19 pandemisi eğitim sektörünü önemli ölçüde etkilemiş ve derslerin toplum güvenliğini sağlayacak şekilde planlanması gerekliliğinden kaynaklanan yeni operasyonel zorlukları gündeme getirmiştir. Bu sebeple, birçok yükseköğretim kurumu, eğitim faaliyetlerinde uygulanacak önlemlere ilişkin rehberler sunmaktadır ve ders programlarının, bu rehberler göz önüne alınarak hazırlanması gerekmektedir. Bu ihtiyacı karşılamak için bu çalışmada, pandemi ile ilgili endişeleri kapsayan bir üniversite ders çizelgeleme problemi için çok amaçlı bir matematiksel model sunulmaktadır. Önerilen matematiksel model ile çevrimiçi olarak sunulması planlanan derslerin ve kapasite kısıtı sebebiyle oluşturulan şubelerin sayısı, mevcut dersleri günler ve ders saatlerine mümkün mertebe dengeli dağıtma hedefini de göz önüne alarak en düşük düzeye indirgemeyi amaçlamaktadır. Önerilen modelin etkinliği gerçek yaşam verileri kullanılarak test edilmektedir. Sonuçlar, önerilen yaklaşımın fakülte yönetiminin hedeflerine hitap eden ve pandemi ile ilgili gereksinimleri karşılayan optimal bir zaman çizelgesini makul bir sürede oluşturabildiğini göstermektedir. Çalışma pandemiyle ilgili yeni kısıtları tanıtarak literatüre katkı sağlamakta ve pandemi rehberleriyle yüzleşen yöneticilere ilham kaynağı olmaktadır.

Anahtar Kelimeler

• Üniversite Ders Çizelgeleme
• Pandemi
• Hibrit Eğitim
• Çok Amaçlı Matematiksel Model

Kaynakça

1. Akkan, C., & Gülcü, A. (2018). A Bi-criteria Hybrid Genetic Algorithm with Robustness Objective for the Course Timetabling Problem.
2. Al-Betar, M. A., & Khader, A. T. (2012). A harmony search algorithm for university course timetabling. Annals of Operations Research, 194(1), 3–31. https://doi.org/10.1007/s10479-010-0769-z
3. Al-Yakoob, S. M., & Sherali, H. D. (2007). A mixed-integer programming approach to a class timetabling problem: A case study with gender policies and traffic considerations. European Journal of Operational Research, 180(3), 1028–1044. https://doi.org/10.1016/j.ejor.2006.04.035
4. Arratia-Martinez, N. M., Maya-Padron, C., & Avila-Torres, P. A. (2021). University Course Timetabling Problem with Professor Assignment. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/6617177
5. Barnhart, C., Bertsimas, D., Delarue, A., & Yan, J. (2021). Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning.
6. Bellio, R., Ceschia, S., di Gaspero, L., Schaerf, A., & Urli, T. (2014). Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem. http://arxiv.org/abs/1409.7186
7. Colajanni, G., & Daniele, P. (2021). A new model for curriculum-based university course timetabling. Optimization Letters, 15(5), 1601–1616. https://doi.org/10.1007/s11590-020-01588-x
8. Council of Higher Education. (2021). YÖK, üniversitelerdeki KOVİD-19 tedbirlerini ve eğitim süreçlerinin çerçevesini belirledi. https://www.yok.gov.tr/Sayfalar/Haberler/2021/yok-ten-covid-19-tedbirlerine-yonelik-uygulama-rehberleri.aspx

9. Daskalaki, S., & Birbas, T. (2005). Efficient solutions for a university timetabling problem through integer programming. European Journal of Operational Research, 160(1), 106–120. https://doi.org/10.1016/j.ejor.2003.06.023
10. 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
11. Dorneles, Á. P., de Araújo, O. C. B., & Buriol, L. S. (2014). A fix-and-optimize heuristic for the high school timetabling problem. Computers and Operations Research, 52(PART A), 29–38. https://doi.org/10.1016/j.cor.2014.06.023
12. Gui, X., Gou, Z., Zhang, F., & Yu, R. (2021). The impact of COVID-19 on higher education building energy use and implications for future education building energy studies. Energy and Buildings, 251. https://doi.org/10.1016/j.enbuild.2021.111346
13. Kaviani, M., Shirouyehzad, H., & Sajadi, S. M. (2013). A mathematical model for university course timetabling problems by considering multi functions A mathematical model for university course timetabling problems. In Int. J. Modelling in Operations Management (Vol. 3).
14. Kiefer, A., Hartl, R. F., & Schnell, A. (2017). Adaptive large neighborhood search for the curriculum-based course timetabling problem. Annals of Operations Research, 252(2), 255–282. https://doi.org/10.1007/s10479-016-2151-2
15. Lü, Z., & Hao, J.-K. (2010). Adaptive Tabu Search for Course Timetabling. In European Journal of Operational Research (Vol. 200, Issue 1).
16. Mirhassani, S. A., & Habibi, F. (2013). Solution approaches to the course timetabling problem. Artificial Intelligence Review, 39(2), 133–149. https://doi.org/10.1007/s10462-011-9262-6
17. 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. https://doi.org/10.1155/2021/9940866
18. Shen, H., Fu, M., Pan, H., Yu, Z., & Chen, Y. (2020). The Impact of the COVID-19 Pandemic on Firm Performance. Emerging Markets Finance and Trade, 56(10), 2213–2230. https://doi.org/10.1080/1540496X.2020.1785863
19. Şimşek, A. B. (2021). Journal of Turkish Operations Management A course timetabling formulation under circumstances of online education Article Info (Vol. 2, Issue 5). https://orcid.org/0000-0002-7276-2376
20. Thepphakorn, T., & Pongcharoen, P. (2019). Variants and parameters investigations of particle swarm optimisation for solving course timetabling problems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11655 LNCS. https://doi.org/10.1007/978-3-030-26369-0_17
21. World Health Organization. (2020). WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020