Abstract
Amongst the wide-ranging areas of the timetabling problems, educational timetabling was reported as one of the most studied and researched areas in the timetabling literature. In this paper, our focus is the university examination timetabling. Despite many approaches proposed in the timetabling literature, it has been observed that there is no single heuristic that is able to solve a broad spectrum of scheduling problems because of the incorporation of problem-specific features in the heuristics. This observation calls for more extensive research and study into how to generate good quality schedules consistently. In order to solve the university examination timetabling problem systematically and efficiently, in our previous work, we have proposed an approach that we called a Domain Transformation Approach (DTA) which is underpinned by the insights from Granular Computing concept. We have tested DTA on some benchmark examination timetabling datasets, and the results obtained were very encouraging. Motivated by the previous encouraging results obtained, in this paper we will be analyzing the proposed method in different aspects. The objectives of this study include (1) To test the generality/applicability/universality of the proposed method (2) To compare and analyze the quality of the schedules generated by utilizing Hill Climbing (HC) optimization versus Genetic Algorithm (GA) optimization on a randomly generated benchmark. Based on the results obtained in this study, it was shown that our proposed DTA method has produced very encouraging results on randomly generated problems. Having said this, it was also shown that our proposed DTA method is very universal and applicable to different sets of examination timetabling problems.
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Abdul Rahim, S.K.N., Bargiela, A., Qu, R. (2017). Solving the Randomly Generated University Examination Timetabling Problem Through Domain Transformation Approach (DTA). In: Ahmad, AR., Kor, L., Ahmad, I., Idrus, Z. (eds) Proceedings of the International Conference on Computing, Mathematics and Statistics (iCMS 2015). Springer, Singapore. https://doi.org/10.1007/978-981-10-2772-7_8
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DOI: https://doi.org/10.1007/978-981-10-2772-7_8
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