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Solving the Randomly Generated University Examination Timetabling Problem Through Domain Transformation Approach (DTA)

  • Siti Khatijah Nor Abdul RahimEmail author
  • Andrzej Bargiela
  • Rong Qu
Conference paper
  • 296 Downloads

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.

Keywords

Examination scheduling Domain transformation approach Granular computing Randomly generated problem 

References

  1. 1.
    Cooper, T.B., Kingston, J.H.: The Complexity of Timetable Construction Problems. Springer, Heidelberg (1996)Google Scholar
  2. 2.
    Even, S., Itai, A., Shamir, A.: On the complexity of time table and multi-commodity flow problems. In: 16th Annual IEEE Symposium on Foundations of Computer Science (1975)Google Scholar
  3. 3.
    Carter, M.W., Laporte, G., Lee, S.Y.: Examination time-tabling: algorithmic strategies and applications. J. Oper. Res. Soc. 373–383 (1996)Google Scholar
  4. 4.
    Rahim, S.K.N.A., Bargiela, A., Qu, R.: Domain transformation approach to deterministic optimization of examination timetables. Artif. Intell. Res. 2(1), 122 (2012)Google Scholar
  5. 5.
    Rahim, S.K.N.A., Bargiela, A., Qu, R.: A study on the effectiveness of genetic algorithm and identifying the best parameters range for slots swapping in the examination scheduling. In: iSMSC (2013)Google Scholar
  6. 6.
    Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Springer Science & Business Media, Berlin (2003)Google Scholar
  7. 7.
    Bargiela, A., Pedrycz, W., Hirota, K.: Logic-based granular prototyping. In: Proceedings of 26th Annual International Computer Software and Applications Conference P IEEE (2002)Google Scholar
  8. 8.
    Bargiela, A., Pedrycz, W., Hirota, K.: Granular prototyping in fuzzy clustering. IEEE Trans. Fuzzy Syst. 697–709 (2004)Google Scholar
  9. 9.
    Bargiela, A., Pedrycz, W.: Toward a theory of granular computing for human-centered information processing. IEEE Trans. Fuzzy Syst. 320–330 (2008)Google Scholar
  10. 10.
    Pedrycz, W., Smith, M.H., Bargiela, A.: A granular signature of data. In: 19th IEEE International Conference of the North American. Fuzzy Information Processing Society (2000)Google Scholar
  11. 11.
    Rahim, S.K.N.A., Bargiela, A., Qu, R.: Granular modelling of exam to slot allocation. In: ECMS (2009)Google Scholar
  12. 12.
    Rahim, S.K.N.A., Bargiela, A., Qu, R.: Hill climbing versus genetic algorithm optimization in solving the examination timetabling problem. In: 2nd International Conference on Operations Research and Enterprise Systems, ICORES 2013 (2013)Google Scholar
  13. 13.
    Rahim, S.K.N.A., Bargiela, A., Qu, R.: Analysis of backtracking in university examination scheduling. In: ECMS (2013)Google Scholar
  14. 14.
    Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30(1), 167–190 (2008)CrossRefGoogle Scholar
  15. 15.
    Qu, R., et al.: A survey of search methodologies and automated system development for examination timetabling. J. Sched. 55–89 (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Siti Khatijah Nor Abdul Rahim
    • 1
    • 2
    Email author
  • Andrzej Bargiela
    • 3
  • Rong Qu
    • 3
  1. 1.Universiti Teknologi MaraTapahMalaysia
  2. 2.University of Nottingham (Malaysia Campus)SelangorMalaysia
  3. 3.University of Nottingham (UK Campus)NottinghamUK

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