Heuristic and genetic algorithm approaches to the real-world university examination timetabling problem
Article Sidebar
Main Article Content
Abstract
Timetabling is one of the computationally difficult problems in scheduling and aims to find best time slots for a number of tasks which require limited resources. In this paper, we examine different solution approaches for the real-world examination timetabling problem (ETP) for university courses. The problem has unique hard and soft constraints, when compared to previous efforts, i.e. consecutive exams, sharing of rooms, room preferences, room capacity and number of empty slots. The aim of the problem is to achieve a timetable, which minimizes the total number of the examination slots without any conflicts. First, the real-world problem is formally defined and a mixed integer linear model is presented. Then, a constructive heuristic and a genetic algorithm based meta-heuristic are proposed in order to solve the ETP. Proposed approaches are tested on a problem set formed by using a real-life data. Results reveal that, proposed approaches are able to produce superior solutions in a limited time.
Keywords: Timetabling, constructive heuristic, genetic algorithm;
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The Global Journal of Business Economics and Management: Current Issues is an open-access journal. The copyright holder is the author or authors. Licensee: Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi, North Nicosia, Cyprus. All articles can be downloaded free of charge. Articles published in the Journal are Open-Access articles distributed under the CC-BY license [Attribution 4.0 International (CC BY 4.0)].
References
Aldeeb, B. A., Norwawi, N. M., Al-Betar, M. A., & Jali, M. Z. B. (2014). Solving university examination timetabling problem using intelligent water drops algorithm. Swarm, Evolutionary, and Memetic Computing: Springer.
Arbaoui, T., Boufflet, J.-P., & Moukrim, A. (2015). Preprocessing and an improved MIP model for examination timetabling. Annals of Operations Research, 229(1), 19-40.
Burke, E. K., & Newall, J. P. (2002). Enhancing timetable solutions with local search methods. Practice and Theory of Automated Timetabling IV: Springer.
Burke, E. K., & Petrovic, S. (2002). Recent research directions in automated timetabling. European Journal of Operational Research, 140(2), 266-280.
Carter, M. W., Laporte, G., & Lee, S. Y. (1996). Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society, 47, 373-383.
Eley, M. (2006). Ant algorithms for the exam timetabling problem. Practice and Theory of Automated Timetabling VI: Springer.
Ersoy, E., Özcan, E., & Uyar, Ş. (2007). Memetic algorithms and hyperhill-climbers. Proceeding of the 3rd Multidisciplinary International Conference on Scheduling: Theory and Applications, 28-31 August 2007, Paris, France.
Gogos, C., Alefragis, P., & Housos, E. (2012). An improved multi-staged algorithmic process for the solution of the examination timetabling problem. Annals of Operations Research, 194(1), 203-221.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Boston, USA: Addion-Wesley.
Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66-72.
Kahar, M. N. M., & Kendall, G. (2010). The examination timetabling problem at Universiti Malaysia Pahang: Comparison of a constructive heuristic with an existing software solution. European Journal of Operational Research, 207(2), 557-565.
Kalayci, C. B., & Güngör, A. (2012). A genetic algorithm based examination timetabling model focusing on student success for the case of the college of engineering at Pamukkale University, Turkey. Gazi University Journal of Science, 25(1), 137-153.
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.
Pillay, N., & Banzhaf, W. (2010). An informed genetic algorithm for the examination timetabling problem. Applied Soft Computing, 10(2), 457-467.
Qu, R., Burke, E. K., McCollum, B., Merlot, L. T. G., & Lee, S. Y. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of scheduling, 12(1), 55-89.
Sabar, N. R., Ayob, M., Kendall, G., & Qu, R. (2009). Roulette wheel graph colouring for solving examination timetabling problems. Combinatorial Optimization and Applications: Springer.
Sabar, N. R., Ayob, M., Kendall, G., & Qu, R. (2012). A honey-bee mating optimization algorithm for educational timetabling problems. European Journal of Operational Research, 216(3), 533-543.