Abstract
University timetabling problems (UTPs) represent a class of challenging and practical constrained optimization problems with its unique requirements when compared to school timetabling. In the past, researchers had proposed different intelligent search methods, that can be generally classified as the constructive or local search methods, to automate school and/or university timetabling. In this paper, we considered a flexible local search scheme combining both min- conflicts and look-forward heuristics to effectively solve general university timetabling problems. Our search proposal augmented with a k-reset repair operator achieved impressive results when compared to that of a general finite- domain constraint solving system, namely the ZDC, on a set of challenging UTPs obtained from an international timetabling competition. A preliminary analysis of their search results was conducted.More importantly, our search proposal of combined heuristics sheds light on various directions to effectively handle other complex or large-scale scheduling problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aarts, E., Korst, J.: Boltzmann machines for traveling salesman problems. European Journal of Operational Research 39, 79–95 (1989)
Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. The MIT Press, McGraw-Hill Book Company (1990)
Davenport, A., Tsang, E., Wang, C., Zhu, K.: GENET: A connectionist architecture for solving constraint satisfaction problems by iterative improvement. In: Proceedings of AAAI 1994 (1994)
Gotlieb: The Construction of Class-Teacher Timetables. In: Proceedings of IFIP Congress, vol. 62, pp. 73–77 (1963)
Johnson, D., Aragon, C., McGeoch, L., Schevon, C.: Optimization by simulated annealing: an experimental evaluation; Part II, graph coloring and number partitioning. Operations Research 39(3), 378–406 (1991)
Kwan, A.C.M., Chan, H.L.: Efficient Lesson Selection Heuristic for High-School Timetabling. In: Proceedings of the IASTED International Conference Artificial Intelligence and Soft Computing, August 9-12 (1999)
Kwan, A.C.M., Chung, K.C.K., Yip, K., Tam, V.: An Automated School Timetabling System Using Hybrid Intelligent Techniques. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 124–134. Springer, Heidelberg (2003)
The Meta-Heuristics Network. The International Timetabling Competition 2002 (October 2002 to March 2003), at http://www.idsia.ch/Files/ttcomp2002/ - sponsored by the International Series of Conferences on the Practice and Theory of Automated Timetabling (PATAT).
Minton, S., Philips, A., Johnston, M.D., Laird, P.: Minimizing Conflicts: A Heuristic Repair Method for Constraint-Satisfaction and Scheduling Problems. Artificial Intelligence 58, 161–205 (1992)
Stuckey, P.J., Tam, V.: Improving Evolutionary Algorithms for Efficient Constraint Satisfaction. The International Journal on Artificial Intelligence Tools, the World Scientific Publishers 8(4), 363–383 (1999)
Tam, V., Ting, D.: Combining the Min-Conflicts and Look-Forward Heuristics to Effectively Solve A Set of Hard University Timetabling Problems. In: Proceedings of the IEEE ICTAI 2003, Sacramento, USA, November 3-5, pp. 492–496 (2003)
Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London (1993)
Yoshikawa, M., Kaneko, K., Nomura, Y., Wantanabe, M.: A Constraint- Based Approach to High-School Timetabling Problems: A Case Study. In: AAAI 1994, pp. 1111–1116 (1994)
Yoshikawa, M., Kaneko, K., Nakakuki, Y.: Improving a Heuristic Rapair Method for Large-Scale School Timetabling Problems. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 275–288. Springer, Heidelberg (1999)
The ZDC Constraint Solving System (Version 1.81), Available at http://cswww.essex.ac.uk/Research/CSP/cacp/cacpdemo.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tam, V., Ho, J., Kwan, A. (2004). Applying An Improved Heuristic Based Optimiser to Solve a Set of Challenging University Timetabling Problems: An Experience Report. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-28633-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
eBook Packages: Springer Book Archive