Skip to main content
SpringerLink
Log in

An Investigation of a Tabu-Search-Based Hyper-Heuristic for Examination Timetabling

  • Conference paper
Multidisciplinary Scheduling: Theory and Applications

Abstract

This paper investigates a tabu-search-based hyper-heuristic for solving examination timetabling problems. The hyper-heuristic framework uses a tabu list to monitor the performance of a collection of low-level heuristics and then make tabu heuristics that have been applied too many times, thus allowing other heuristics to be applied. Experiments carried out on examination timetabling datasets from the literature show that this approach is able to produce good quality solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Burke. E. K., Kendall, G. and Soubeiga, E. (2003a) A tabu search hyper-heuristic for timetabling and rostering. Journal of Heuristics, 9:451–470.

    Article  Google Scholar 

  • Burke, E. K. and Petrovic, S. (2002), Recent research directions in automated timetabling. European Journal of Operational Research, 140:266–280.

    Article  Google Scholar 

  • Burke, E. K. and Newall, J. P. (2004), Solving examination timetabling problems through adaptation of heuristics orderings. Annals of Operations Research, 129:107–134.

    Article  MathSciNet  Google Scholar 

  • Burke, E. K. and Newall, J. P. (1999) A multi-stage evolutionary algorithm for the timetable problem. IEEE Transactions on Evolutionary Computation, 3:63–74.

    Article  Google Scholar 

  • Burke, E. K., Hart, E., Kendall, G., Newall, J., Ross, P. and Schulenburg, S. (2003b) Hyper-Heuristics: An emerging direction in modem search technology. In Handbook of Meta-Heuristics, F. Glover and G. Kochenberger (Eds.), Chapter 16, Kluwer, Dordrecht, pp. 457–474.

    Google Scholar 

  • Caramia, M., Dell'Olmo, P. and Italiano, G. F. (2001) New algorithms for examination time-tabling. In Proceedings of 4th Workshop on Algorithm Engineering, Lecture Notes in Computer Science, Vol. 1982, Springer, Berlin, pp. 230–242.

    Google Scholar 

  • Carter, M. W. and Laporte, G. (1996) Recent developments in practical examination timetabling. The Practice and Theory of Automated Timetabling I, Lecture Notes in Computer Science, Vol. 1153, E. K. Burke and P. Ross (Eds.), Springer, Berlin, pp. 3–21.

    Google Scholar 

  • Carter, M. W. (1986) A Survey of practical applications of examination timetabling algorithms. Operations Research Society of America, 34:2, March–April.

    MathSciNet  Google Scholar 

  • Carter, M. W., Laporte, G. and Lee, S. Y. (1996) Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society, 47:373–383.

    Google Scholar 

  • Cowling, P., Kendall, G. and Soubeiga, E. (2001) A hyperheuristic approach to scheduling a sales summit. In Practice and Theory of Automated Timetabling III, Lecture Notes in Computer Science, Vol. 2079, E. K. Burke and W. Erben, (Eds.), Springer, Berlin, pp. 176–190.

    Google Scholar 

  • Cowling, P., Kendall, G. and Han, L. (2002a) An Investigation of a hyperheuristic genetic algo-rithm applied to a trainer scheduling problem. In Proceedings of Congress on Evolutionary Computation (CEC2002), pp. 1185–1190.

    Google Scholar 

  • Cowling, P., Kendall, G. and Soubeiga, E. (2002b) Hyperheuristics: A tool for rapid prototyping in scheduling and optimisation. In Applications of Evolutionary Computing: Proceedings of EVO Workshop 2002, Lecture Notes in Computer Science, Vol. 2279, S. Cagoni, J. Gottlieb, E. Hart, M. Middendorf and R. Günther (Eds.), Springer, Berlin, pp 1–10.

    Google Scholar 

  • Cowling, P., Kendall, G. and Soubeiga, E. (2002c) Hyperheuristics: A robust optimisation method applied to nurse scheduling. 7th International Conference on Parallel Problem Solving from Nature, PPSN2002, Lecture Notes in Computer Science, Vol. 2439, Springer, Berlin, pp. 851–860.

    Google Scholar 

  • Di Gaspero, L. (2002) Recolour, shake and kick: A recipe for the examination timetabling problem. In Proceedings of the Fourth International Conference on the Practice and Theory of Automated Timetabling, Gent, Belgium, August 2002, E. Burke and P. De Causmaecker (Eds.), pp. 404–407.

    Google Scholar 

  • Di Gaspero L. and Schaerf A. (2001), Tabu search techniques for examination timetabling. In Practice and Theory of Automated Timetabling III, E. K. Burke and W. Erben (Eds.), Lecture Notes in Computer Science, Vol. 2079, Springer, Berlin, pp. 104–117.

    Google Scholar 

  • Di Gaspero, L. and Schaerf, A. (2003) Multineighbourhood local search with application to course timetabling. In Practice and Theory of Automated Timetabling IV, E. Burke and P. De Causmaecker (Eds.), Lecture Notes in Computer Science, Vol. 2740, Springer, Berlin, pp. 262–275.

    Google Scholar 

  • Downsland, K. (1998) Off-the-peg or made to measure: timetabling and scheduling with SA and TS. In Practice and Theory of Automated Timetabling II, E. Burke and M. Carter (Eds.), Lecture Notes in Computer Science, Vol. 1408, Springer, Berlin, pp. 37–52.

    Google Scholar 

  • Glover, E (1986) Future paths for integer programming and links to artificial intelligence. Computers and Operations Research, 13:533–549.

    Article  MATH  MathSciNet  Google Scholar 

  • Glover, F. and Laguna, M. (1997) Tabu Search, Kluwer, Boston, MA.

    Google Scholar 

  • Gratch, J. M. and Chien, S. A. (1996) Adaptive problem-solving for large scale scheduling problems: A case study. Journal of Artificial Intelligence Research, 4:365–396.

    Google Scholar 

  • Kendall, G., Soubeiga, E. and Cowling, P. (2002) Choice function and random hyperheuristics. Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning, SEAL'02, pp. 667–671.

    Google Scholar 

  • Merlot, L. T. G., Boland, N., Hughes, B. D. and Stuckey, P. J. (2003) A hybrid algorithm for the examination timetabling problem. In Practice and Theory of Automated Timetabling IV, E. Burke and P. De Causmaecker (Eds.), Lecture Notes in Computer Science, Vol. 2740, Springer, Berlin, pp. 207–231.

    Google Scholar 

  • Nareyek, A. (2001) An empirical analysis of weight-adaptation strategies for neighborhoods of heuristics. Proceedings of 4th Metaheuristics International Conference, MIC'2001, pp. 211–216.

    Google Scholar 

  • Schaerf, A. and Schaerf, M. (1995) Local search techniques for high school timetabling. In Proceedings of the 1st International Conference on the Practice and Theory of Automated Timetabling (PATAT'95), E. K. Burke and P. Ross (Eds.), pp. 313–323.

    Google Scholar 

  • Schaerf, A. (1999) A survey of automated timetabling. Artificial Intelligence Review, 13:87–127.

    Article  Google Scholar 

  • Terashima-Marín, H., Ross, P. M. and Valenzuela-Rendón, M. (1999) Clique-based crossover for solving the timetabling problem with gas. Proceedings of the Congress on Evolutionary Computation, Washington, DC, July 6–9, pp. 1200–1206.

    Google Scholar 

  • White, G. M. and Xie, B. S. (2001) Examination timetables and tabu search with longer term memory. In Practice and Theory of Automated Timetabling III, E. K. Burke and W. Erben (Eds.), Lecture Notes in Computer Science, Vol. 2079, Springer, Berlin, pp. 85–103.

    Google Scholar 

  • White, G. M., Xie, B. S. and Zonjic, S. (2004) Using tabu search with longer-term memory and relaxation to create examination timetables. European Journal of Operational Research, 153:80–91.

    Article  MathSciNet  Google Scholar 

  • Wren, A. (1996) Scheduling, Timetabling and Rostering—a special relationship? In Practice and Theory of Automated Timetabling I, E. K. Burke and P. Ross (Eds.), Lecture Notes in Computer Science, Vol. 1153, Springer, Berlin, pp. 46–76.

    Google Scholar 

  • Wright, M. (2001) Subcost-guided search—experiments with timetabling problems. Journal of Heuristics, 7:251–260.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Automated Scheduling, Optimisation and Planning (ASAP) Research Group, School of Computer Science and Information Technology, University of Nottingham, Nottingham, NG8 IBB, UK

    Graham Kendall & Naimah Mohd Hussin

Authors
  1. Graham Kendall
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naimah Mohd Hussin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Univ. of Nottingham, UK

    Graham Kendall , Edmund K. Burke  & Sanja Petrovic ,  & 

  2. Université de Montréal, Canada

    Michel Gendreau

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer Science+Business Media, Inc.

About this paper

Cite this paper

Kendall, G., Hussin, N.M. (2005). An Investigation of a Tabu-Search-Based Hyper-Heuristic for Examination Timetabling. In: Kendall, G., Burke, E.K., Petrovic, S., Gendreau, M. (eds) Multidisciplinary Scheduling: Theory and Applications. Springer, Boston, MA. https://doi.org/10.1007/0-387-27744-7_15

Download citation

Publish with us

Policies and ethics

Search

Navigation