A COMPLETE SOLUTION to EXAM SCHEDULING PROBLEM: A CASE STUDY

Year 2022, Issue: 049, 12 - 34, 30.06.2022

Abdullah Elen

Abstract

Exam scheduling is a complex task that higher education institutions (universities, colleges, etc.) must prepare each semester depending on their academic calendar. The preparation of exam schedules requires a multi-dimensional analysis and experience. It is also a quite time-consuming sequence of operations. Exam times should not overlap when preparing the schedules and needed constraints are expected to be complied with as much as possible. Therefore, it takes a long time to form a complete solution. In this study, a Genetic Algorithm based exam scheduling method was developed to create a complete solution for the Vocational School of T.O.B.B. Technical Sciences, Karabuk University. During the test phase, four different experiments were performed in different constraints and criteria. As a result of these experiments, all solutions gave appropriate results until 2000 iterations. There was no overlap in any of the exam schedules and significant success was achieved in the desired constraints.

Keywords

Exam Scheduling, Genetic Algorithm, Metaheuristics, Karabuk University

Thanks

The dataset in this study has been prepared on basis of the 2018-2019 fall term student information system of the Vocational School of T.O.B.B. Technical Sciences, Karabuk University.

References

• [1] Elen, A., & Çayıroğlu, İ., (2010), Solving of Scheduling Problem with Heuristic Optimization Approach. Teknoloji (Engineering Science and Technology, an International Journal), 13(3), 159-172.
• [2] Soghier, A., & Qu, R., (2013), Adaptive selection of heuristics for assigning time slots and rooms in exam timetables. Applied Intelligence, 39(2), 438–450. https://doi.org/10.1007/s10489-013-0422-z
• [3] Çayıroğlu, İ., & Elen, A., (2012), A Heuristic Optimization Approach for A Real-World University Timetabling Problem. Advances in Computer Science and Engineering, 9(2), 103-131.
• [4] Brunato, M., & Battiti, R., (2019), Combining intelligent heuristics with simulators in hotel revenue management. Annals of Mathematics and Artificial Intelligence. https://doi.org/10.1007/s10472-019-09651-9
• [5] Taheri, G., Khonsari, A., Entezari-Maleki, R., & Sousa, L., (2020), A hybrid algorithm for task scheduling on heterogeneous multiprocessor embedded systems. Applied Soft Computing, 106202. https://doi.org/10.1016/j.asoc.2020.106202
• [6] Gantt, H.L., (1910), “Work, Wages and Profit”. Engineering Magazine. New York.; republished as Work, Wages and Profits. Easton, Pennsylvania: Hive Publishing Company. 1974. ISBN 0-87960-048-9.
• [7] Coit, D. W., & Zio, E., (2018), The Evolution of System Reliability Optimization. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2018.09.008
• [8] Saldarriaga, J., Páez, D., Salcedo, C., Cuero, P., López, L. L., León, N., & Celeita, D., (2020), A Direct Approach for the Near-Optimal Design of Water Distribution Networks Based on Power Use. Water, 12(4), 1037. https://doi.org/10.3390/w12041037
• [9] Khosravanian, R., Mansouri, V., Wood, D. A., & Alipour, M. R., (2018), A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs. Journal of Petroleum Exploration and Production Technology. https://doi.org/10.1007/s13202-018-0447-2
• [10] Crown, W., Buyukkaramikli, N., Sir, M. Y., Thokala, P., Morton, A., Marshall, D. A., Tosh, J. C., Ijzerman, M. J., Padula, W. V., & Pasupathy, K. S., (2018), Application of Constrained Optimization Methods in Health Services Research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force. Value in Health, 21(9), 1019–1028. https://doi.org/10.1016/j.jval.2018.05.003
• [11] Chávez-Bosquez O, Hernández-Torruco J, Hernández-Ocaña B, Canul-Reich J., (2020), Modeling and Solving a Latin American University Course Timetabling Problem Instance. Mathematics 8(10), 1833. https://doi.org/10.3390/math8101833
• [12] Santiago-Mozos, R., Salcedo-Sanz, S., DePrado-Cumplido, M., & Bousoño-Calzón, C., (2005), A two-phase heuristic evolutionary algorithm for personalizing course timetables: a case study in a Spanish university. Computers & Operations Research, 32(7), 1761–1776. https://doi.org/10.1016/j.cor.2003.11.030
• [13] Dammak, A., Elloumi, A., & Kamoun, H., (2006), Classroom assignment for exam timetabling. Advances in Engineering Software, 37(10), 659–666. https://doi.org/10.1016/j.advengsoft.2006.02.001
• [14] Pillay, N., & Banzhaf, W., (2010), An informed genetic algorithm for the examination timetabling problem. Applied Soft Computing, 10(2), 457–467. https://doi.org/10.1016/j.asoc.2009.08.011
• [15] Turabieh, H., & Abdullah, S., (2011), An integrated hybrid approach to the examination timetabling problem. Omega, 39(6), 598–607. https://doi.org/10.1016/j.omega.2010.12.005
• [16] Shatnawi, A., Fraiwan, M., & Al-Qahtani, H. S., (2017), Exam scheduling: A case study. 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI). https://doi.org/10.1109/icaci.2017.7974498
• [17] Keskin, M. E., Döyen, A., Akyer, H., & Güler, M. G., (2018), Examination timetabling problem with scarce resources: a case study. European J. of Industrial Engineering, 12(6), 855. https://doi.org/10.1504/ejie.2018.096394
• [18] Güler, M. G., Geçici, E., Köroğlu, T., & Becit, E., (2021), A web-based decision support system for examination timetabling. Expert Systems with Applications, 183, 115363. https://doi.org/10.1016/j.eswa.2021.115363
• [19] Aldeeb, B. A., Azmi Al-Betar, M., Md Norwawi, N., Alissa, K. A., Alsmadi, M. K., Hazaymeh, A. A., & Alzaqebah, M., (2021), Hybrid Intelligent Water Drops Algorithm for Examination Timetabling Problem. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.06.016
• [20] Hao, X., Qu, R., & Liu, J., (2020), A Unified Framework of Graph-based Evolutionary Multitasking Hyper-heuristic. IEEE Transactions on Evolutionary Computation, 25, 1. https://doi.org/10.1109/TEVC.2020.2991717