The University Course Timetabling Problem with a Three-Phase Approach

  • Philipp Kostuch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3616)


This paper describes the University Course Timetabling Problem (UCTP) used in the International Timetabling Competition 2003 organized by the Metaheuristics Network and presents a state-of-the-art heuristic approach towards the solution of the competition instances. It is a greatly improved version of the winning competition entry. The heuristic is divided into three phases: at first, a feasible timetable is constructed, then Simulated Annealing (SA) is used to order the thus created time-slots optimally, and finally SA is used to swap individual events between time-slots to improve the solution quality.


Simulated Annealing Incidence Matrix Soft Constraint Hard Constraint Time Demand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Burke, E., Bykov, Y., Newall, J., Petrovic, S.: A Time-Predefined Approach to Course Timetabling. Yugoslav J. Oper. Res. 13, 139–151 (2003)Google Scholar
  3. 3.
    Carter, M.W., Johnson, D.G.: Extended Clique Initialisation in Examination Timetabling. J. Oper. Res. Soc. 52, 538–544 (2001)Google Scholar
  4. 4.
    Carter, M.W., Laporte, G.: Recent Developments in Practical Course Timetabling. In: Burke, E.K., Carter, M. (eds.) PATAT 1997. LNCS, vol. 1408, pp. 3–19. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Carter, M.W., Laporte, G., Chinneck, J.W.: A General Examination Scheduling System. Interfaces. 24, 109–120 (1994)Google Scholar
  6. 6.
    Carter, M.W., Laporte, G., Lee, S.-Y.: Examination Timetabling: Algorithmic Strategies and Applications. J. Oper. Res. Soc. 47, 373–383 (1996)Google Scholar
  7. 7.
    Chiarandini, M., Socha, K., Birattari, M., Rossi-Doria, O.: An Effective Hybrid Approach for the University Course Timetabling Problem. Technical Report AIDA-03-05. FG Intellektik, TU Darmstadt (March 2003)Google Scholar
  8. 8.
    Deris, S., Omatu, S., Ohta, H.: Timetable Planning Using the Constraint-Based Reasoning. Comput. Oper. Res. 27, 819–840 (2000)Google Scholar
  9. 9.
    de Werra, D.: The Combinatorics of Timetabling. Eur. J. Oper. Res. 96, 504–513 (1997)Google Scholar
  10. 10.
    Di Gaspero, L., Schaerf, A.: Tabu Search Techniques for Examination Timetabling. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 104–117. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Di Gaspero, L., Schaerf, A.: A Multineighbourhood Local Search Solver for the Timetabling competition TTComp 2002. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 475–478. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)Google Scholar
  13. 13.
    Paechter, B., Rankin, R.C., Cumming, A., Fogarty, T.C.: Timetabling the Classes of an Entire University with an Evolutionary Algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498. Springer, Heidelberg (1998)Google Scholar
  14. 14.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Dover, New York (1998)Google Scholar
  15. 15.
    Ross, P., Hart, E., Corne, D.: Some Observations About GA-based Exam Timetabling. In: Burke, E.K., Carter, M. (eds.) PATAT 1997. LNCS, vol. 1408, pp. 115–129. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Rossi-Doria, O., Blum, C., Knowles, J., Sampels, M., Socha, K., Paechter, B.: A Local Search for the Timetabling Problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 124–127. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Rossi-Doria, O., Sampels, M., Chiarandini, M., Knowles, J., Manfrin, M., Mastrolilli, M., Paquete, L., Paechter, B.: A Comparison of the Performance of Different Metaheuristics on the Timetabling Problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 329–351. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Schaerf, A.: A Survey of Automated Timetabling. Artif. Intell. Rev, 87–127 (1999)Google Scholar
  19. 19.
    Smith, K.A., Abramson, D., Duke, D.: Hopfield Neural Networks for Timetabling: Formulations, Methods, and Comparative Results. Int. J. Comput. Indust. Eng. 44, 283–305 (2003)Google Scholar
  20. 20.
    Socha, K.: \({\cal MAX}\)-\({\cal MIN}\) Ant System for International Timetabling Competition. Technical Report TR/IRIDIA/2003-30. Université Libre de Bruxelles, Belgium (September 2003)Google Scholar
  21. 21.
    Socha, K., Knowles, J., Sampels, M.: A Max–Min Ant System for the University Timetabling Problem. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 1–13. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Thomson, J.M., Dowsland, K.A.: A Robust Simulated Annealing Based Examination Timetabling System. Comput. Oper. Res. 25, 637–648 (1998)Google Scholar
  23. 23.
    West, D.B.: Introduction to Graph Theory, 2nd edn. Prentice-Hall, Englewood Cliffs (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Philipp Kostuch
    • 1
  1. 1.Department of StatisticsOxford UniversityOxfordUK

Personalised recommendations