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An effective hybrid algorithm for university course timetabling

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Abstract

The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an ‘International Timetabling Competition’ to which 24 algorithms were submitted by various research groups active in the field of timetabling. We describe and analyse a hybrid metaheuristic algorithm which was developed under the very same rules and deadlines imposed by the competition and outperformed the official winner. It combines various construction heuristics, tabu search, variable neighbourhood descent and simulated annealing. Due to the complexity of developing hybrid metaheuristics, we strongly relied on an experimental methodology for configuring the algorithms as well as for choosing proper parameter settings. In particular, we used racing procedures that allow an automatic or semi-automatic configuration of algorithms with a good save in time. Our successful example shows that the systematic design of hybrid algorithms through an experimental methodology leads to high performing algorithms for hard combinatorial optimisation problems.

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References

  • Arntzen, H. and A. Løkketangen, “A tabu search heuristic for a university timetabling problem,” in Proceedings of the Fifth Metaheuristics International Conference, Kyoto, Japan (Aug. 2003).

  • Birattari, M., The Problem of Tuning Metaheuristics, as seen from a Machine Learning Perspective. PhD thesis, Université Libre de Bruxelles, Brussels, Belgium (2004).

  • Birattari, M., “The race package for R. Racing methods for the selection of the best,” Technical Report TR/IRIDIA/2003-37, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium (2003).

  • Birattari, M., T. Stützle, L. Paquete, and K. Varrentrapp, “A Racing algorithm for configuring metaheuristics,” in W. B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 11–18, New York, 2002. Morgan Kaufmann Publishers.

  • Burke, E. K., A. J. Eckersley, B. McCollum, S. Petrovic, and R. Qu., “Analysing similarity in examination timetabling,” in E. K. Burke and M. Trick, Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling. 2004, pp. 557–559.

  • Burke, E. K., C. Beyrouthy, J. D. Landa Silva, B. McCollum, and P. McMullan, “SpaceMAP-applying meta-heuristics to real world space allocation problems in academic institutions,” in E. K. Burke and M. Trick (eds.), Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling. 2004, pp. 441–444.

  • Burke, E. K., D. G. Elliman, and R. F. Weare, “Specialised recombinative operators for timetabling problems,” in AISB Workshop on Evolutionary Computing, Springer Verlag Notes in Computer Science Volume 993, pp. 75–85. Springer Verlag, Berlin, Germany (1995).

  • Burke, E. K., G. Kendall, and E. Soubeiga, “A tabu-search hyperheuristic for timetabling and rostering,” Journal of Heuristics, 9(6), 451–470 (2003).

    Article  Google Scholar 

  • Burke, E. K., J. P. Newall, and R. F. Weare, “A memetic algorithm for university exam timetabling,” in E. K. Burke and P. Ross Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science Volume 1153, pp. 241–250.

  • Burke, E. K. and M. A. Trick (eds.), Practice and Theory of Automated Timetabling V, 5th International Conference, PATAT 2004, vol. 3616 of Lecture Notes in Computer Science. Springer Verlag, Berlin, Germany (2005).

  • Burke, E. K., and M. Trick (eds.), Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling. PATAT 2004, Pittsburgh, PA (Aug. 2004).

  • Burke, E. K. and M. W. Carter (eds.), Practice and Theory of Automated Timetabling II, Second International Conference, PATAT 1997, vol. 1408 of Lecture Notes in Computer Science. Springer Verlag, Berlin, Germany (1998).

  • Burke, E. K. and P. de Causmaecker (eds.), Practice and Theory of Automated Timetabling IV, 4th International Conference, PATAT 2002, vol. 2740 of Lecture Notes in Computer Science. Springer Verlag, Berlin, Germany (2003).

  • Burke, E. K. and P. Ross (eds.), Practice and Theory of Automated Timetabling, First International Conference, PATAT 1995, vol. 1153 of Lecture Notes in Computer Science. Springer Verlag, Berlin, Germany, (1996).

  • Burke, E. K. and S. Petrovic. “Recent research directions in automated timetabling,” European Journal of Operational Research, 140(2), 266–280 (2002).

    Article  Google Scholar 

  • Burke, E. K. and W. Erben (eds.), Practice and Theory of Automated Timetabling III, Third International Conference, PATAT 2000, vol. 2079 of Lecture Notes in Computer Science. Springer Verlag, Berlin, Germany (2001).

  • Burke, E. K., Y. Bykov, and S. Petrovic, “A multicriteria approach to examination timetabling,” in E. K. Burke and W. Erben, Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science Volume 1153, pp. 118–131.

  • Burke, E. K., Y. Bykov, J. Newall, and S. Petrovic, “A time-predefined approach to course timetabling,” Yugoslav Journal of Operations Research, 13(2), 139–151 (2003).

    Google Scholar 

  • Carter, M. W. and G. Laporte, “Recent developments in practical course timetabling,” in E. K. Burke and M. W. Carter (eds.), Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science Volume 1408, pp. 3–19.

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

    Google Scholar 

  • Chand, A., “A constraint based generic model for representing complete university timetabling data,” in E. K. Burke and M. A. Trick (eds.), Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling. 2004, pp. 125–150.

  • Chiarandini, M. and T. Stützle, “Experimental evaluation of course timetabling algorithms.” Technical Report AIDA-02-05, Intellectics Group, Computer Science Department, Darmstadt University of Technology, Darmstadt, Germany (April 2002).

  • Chiarandini, M., Stochastic Local Search Methods for Highly Constrained Combinatorial Optimisation Problems. PhD thesis, Computer Science Department, Darmstadt University of Technology, Darmstadt, Germany, (Aug. 2005).

  • Cohen, P. R., Empirical Methods for Artificial Intelligence. MIT Press, Boston (1995).

    Google Scholar 

  • Conover, W. J., Practical Nonparametric Statistics. 3rd edn, John Wiley & Sons, New York, NY, USA (1999).

    Google Scholar 

  • Culberson, J. C., “Iterated greedy graph coloring and the difficulty landscape,” Technical Report 92-07, Department of Computing Science, The University of Alberta, Edmonton, Alberta, Canada (June 1992).

  • Custers, N. P. De Causmaecker, P. Demeester, and G. V. Berghe, “Semantic components for timetabling,” in E. K. Burke and M. A. Trick (eds.), Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science Volume 3616, pp. 17–33.

  • De Werra. D., “An introduction to timetabling,” European Journal of Operational Research, 19(2), 151–162 (1985).

    Article  Google Scholar 

  • den Besten, M. L. Simple Metaheuristics for Scheduling: An Empirical Investigation into the Application of Iterated Local Search to Deterministic Scheduling Problems with Tardiness Penalties. PhD thesis, Darmstadt University of Technology, Darmstadt, Germany, October (2004).

  • Di Gaspero, L. and A. Schaerf, “Writing local search algorithms using EASYLOCAL++,” in S. Voß and D.L. Woodruff, (eds.), Optimization Software Class Libraries, pp. 81–154. Kluwer Academic Publishers, Boston, MA, USA (2002).

    Google Scholar 

  • Fink, A. and S. Voß, “A heuristic optimization framework,” in S. Voß and D.L. Woodruff (eds.), Optimization Software Class Libraries, pp. 81–154. Kluwer Academic Publishers, Boston, MA, USA (2002).

    Google Scholar 

  • Garey, M. R. and D. S. Johnson, Computers and Intractability: A Guide to the Theory of \({\mathcal {NP}}\)-Completeness. Freeman, San Francisco, CA, USA (1979).

  • Hertz, A. and D. de Werra, “Using tabu search techniques for graph coloring,” Computing, 39(4), 345–351 (1987).

    Article  Google Scholar 

  • Hoos, H. H. and T. Stützle, “Characterising the behaviour of stochastic local search,” Artificial Intelligence, 112(1–2), 213–232 (1999).

    Article  Google Scholar 

  • Hoos, H. H., and T. Stützle, “Evaluating Las Vegas algorithms—Pitfalls and remedies,” in G. F. Cooper and S. Moral (eds.), Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 238–245. Morgan Kaufmann Publishers, San Francisco, CA, USA (1998).

  • Johnson, D. S., C. R. Aragon, L. A. McGeoch, and C. Schevon, “Optimization by simulated annealing: An experimental evaluation; part II, graph coloring and number partitioning,” Operations Research, 39(3), 378–406 (1991).

    Article  Google Scholar 

  • Kostuch, P., “The university course timetabling problem with a three-phase approach,” in E. K. Burke and M. A. Trick, Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science Volume 3616, pp. 109–125.

  • Kostuch, P., “University course timetabling,” Transfer Thesis, Oxford University, England (2003).

  • Kostuch, P. and K. Socha, “Hardness prediction for the university course timetabling problem,” in J. Gottlieb and G. R. Raidl, (eds.), Evolutionary Computation in Combinatorial Optimization, Springer Lecture Notes in Computer Science Volume 3004, pp. 132–141. Springer Verlag, Berlin, Germany (2004).

  • Lourenço, H. R., O. Martin, and T. Stützle, “Iterated local search,” in F. Glover and G. Kochenberger (eds.), Handbook of Metaheuristics, 321–353. Kluwer Academic Publishers, Norwell, MA, USA (2002).

    Google Scholar 

  • Morgenstern, C. and H. Shapiro, “Coloration neighborhood structures for general graph coloring,” in Proceedings of the first Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 226–235. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (1990).

  • Newall, J. P., Hybrid Methods for Automated Timetabling. PhD thesis, Department of Computer Science, University of Nottingham, UK, May (1999).

  • Papadimitriou, C. H. and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Inc., Englewood Cliffs, NJ, 1982.

    Google Scholar 

  • Post, G. and B. Veltman. “Harmonious personnel scheduling,” in E. K. Burke and M. A. Trick (eds.), Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling. PATAT 2004, pp. 557–559.

  • Risler, M., M. Chiarandini, L. Paquete, T. Schiavinotto, and T. Stützle, “An algorithm for the car sequencing problem of the ROADEF 2005 challenge,” Technical Report AIDA-04-06, Intellectics Group, Computer Science Department, Darmstadt University of Technology (2004).

  • Rossi-Doria, O. and B. Paeehter, “An hyperheuristic approach to course timetabling problem using evolutionary algorithm,” Technical Report CC-00970503, Napier University, Edinburgh, Scotland (2003).

  • Rossi-Doria, O., B. Paechter, C. Blum, K. Socha, and M. Samples, “A local search for the timetabling problem.” in E. Burke and P. Causmaecker (eds.), Proceedings of the 4th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2002, pp. 124–127 Gent, Belgium (August 2002).

  • Rossi-Doria, O., M. Samples, M. Birattari, M. Chiarandini, M. Dorigo, L. Gambardella, J. Knowles, M. Manfrin, M. Mastrolilli, B. Paechter, L. Paquete, and T. Stützle, “A comparison of the performance of different metaheuristics on the timetabling problem,” in E. K. Burke and P. De Causmaecker, Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science Volume 2740, pp. 329–351.

  • Schaerf, A., “A survey of automated timetabling,” Artificial Intelligence Review, 13(2), 87–127 (1999).

    Article  Google Scholar 

  • Sedgewick, R. Algorithms. 2nd edn., Addison-Wesley, Reading, MA, USA (1988).

    Google Scholar 

  • Setubal, J. C., “Sequential and parallel experimental results with bipartite matching algorithms,” Technical Report EC-96-09, Institute of Computing, University of Campinas, Brasil (1996).

  • Sheskin, D. J., Handbook of Parametric and Nonparametric Statistical Procedures. 2nd edn., Chapman & Hall (2000).

  • Socha, K., “The influence of run-time limits on choosing ant system parameters,” in Cantu-Paz et al. (eds), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003), vol. 2723 of Lecture Notes in Computer Science, pp. 49–60. Springer Verlag, Berlin, Germany (July 2003).

  • Socha, K., M. Sampels, and M. Manfrin, “Ant algorithms for the university course timetabling problem with regard to the state-of-the-art,” in Günther R. Raidl, Jean-Arcady Meyer, Martin Middendorf, Stefano Cagnoni, Juan J. Romero Cardalda, David Corne, Jens Gottlieb, Agnès Guillot, Emma Hart, Colin G. Johnson, and Elena Marchiori (eds.), Applications of Evolutionary Computing: Proceedings of Evo Workshops 2003, vol. 2611 of Lecture Notes in Computer Science, pp. 334–345. Springer Verlag, Berlin, Germany (2003).

  • Terashima-Marín, H., P. Ross, and M. Valenzuela-Rendón, “Evolution of constraint satisfaction strategies in examination timetabling,” in W. Banzhaf, J. M. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. J. Jakiela, and R. E. Smith (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), pp. 635–642. Morgan Kaufmann Publishers, San Francisco, CA, USA (1999).

  • Thompson, J. and K. Dowsland, “A robust simulated annealing based examination timetabling system,” Computers and Operations Research, 25(7–8), 637–648 (1998).

    Article  Google Scholar 

  • Wren, A.,“Scheduling, timetabling and rostering—a special relationship?,” In E. K. Burke and P. Ross (eds.), Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science Volume 1153, pp. 46–75 (1996).

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Correspondence to Marco Chiarandini.

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The work was carried out while Marco Chiarandini was with the Intellectics Group, at the Computer Science Department of the Darmstadt University of Technology, Hochschulstraße 10, 64283 Darmstadt, Germany.

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Chiarandini, M., Birattari, M., Socha, K. et al. An effective hybrid algorithm for university course timetabling. J Sched 9, 403–432 (2006). https://doi.org/10.1007/s10951-006-8495-8

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