Computational Study of Non-linear Great Deluge for University Course Timetabling

  • Joe Henry Obit
  • Dario Landa-Silva
Part of the Studies in Computational Intelligence book series (SCI, volume 299)


The great deluge algorithm explores neighbouring solutions which are accepted if they are better than the best solution so far or if the detriment in quality is no larger than the current water level. In the original great deluge method, the water level decreases steadily in a linear fashion. In this paper,we conduct a computational study of a modified version of the great deluge algorithm in which the decay rate of the water level is non-linear. For this study, we apply the non-linear great deluge algorithm to difficult instances of the university course timetabling problem. The results presented here show that this algorithm performs very well compared to other methods proposed in the literature for this problem. More importantly, this paper aims to better understand the role of the non-linear decay rate on the behaviour of the non-linear great deluge approach.


Water Level Decay Rate Problem Instance Penalty Cost Large Instance 
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.
    Aarts, E., Korts, J.: Simulated Annealing and Boltzman Machines. Wiley, Chichester (1998)Google Scholar
  2. 2.
    Abdullah, S., Burke, E.K., McCollum, B.: An Investigation of Variable Neighbourhood Search for University Course Timetabling. In: Proceedings of MISTA 2005: The 2nd Multidisciplinary Conference on Scheduling: Theory and Applications, pp. 413–427 (2005)Google Scholar
  3. 3.
    Abdullah, S., Burke, E.K., McCollum, B.: A Hybrid Evolutionary Approach to the University Course Timetabling Problem. In: Proceedings of CEC 2007: The 2007 IEEE Congress on Evolutionary Computation, pp. 1764–1768 (2007)Google Scholar
  4. 4.
    Abdullah, S., Burke, E.K., McCollum, B.: Using a Randomised Iterative Improvement Algorithm with Composite Neighborhood Structures for University Course Timetabling. In: Metaheuristics - Progress in Complex Systems Optimization, pp. 153–172. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Asmuni, H., Burke, E.K., Garibaldi, J.: Fuzzy Multiple Heuristic Ordering for Course Timetabling. In: Proceedings of the 5th United Kingdom Workshop on Computational Intelligence (UKCI 2005), pp. 302–309 (2005)Google Scholar
  6. 6.
    Burke, E.K., Bykov, Y., Newall, J., Petrovic, S.: A Time-predefined Approach to Course Timetabling. Yugoslav Journal of Operations Research (YUJOR) 13(2), 139–151 (2003)zbMATHCrossRefGoogle Scholar
  7. 7.
    Burke, E.K., Kendall, G., Soubeiga, E.: A Tabu-search Hyperheuristic for Timetabling and Rostering. Journal of Heuristics 9, 451–470 (2003)CrossRefGoogle Scholar
  8. 8.
    Burke, E.K., Eckersley, A., McCollum, B., Petrovic, S., Qu, R.: Hybrid Variable Neighbourhood Approaches to University Exam Timetabling. Technical Report NOTTCS-TR-2006-2, University of Nottingham, School of Computer Science (2006)Google Scholar
  9. 9.
    Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A Graph Based Hyper-heuristic for Educational Timetabling Problems. European Journal of Operational Research 176, 177–192 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Chiarandini, M., Birattari, M., Socha, K., Rossi-Doria, O.: An Effective Hybrid Algorithm for University Course Timetabling. Journal of Scheduling 9(5), 403–432 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Cooper, T., Kingston, H.: The Complexity of Timetable Construction Problems. In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 283–295. Springer, Heidelberg (1996)Google Scholar
  12. 12.
    Dueck, G.: New Optimization Heuristic: The Great Deluge Algorithm and the Record-to-record Travel. Journal of Computational Physics 104, 86–92 (1993)zbMATHCrossRefGoogle Scholar
  13. 13.
    Even, S., Itai, A., Shamir, A.: On the Complexity of Timetabling and Multicommodity Flow Problems. SIAM Journal of Computation 5, 691–703 (1976)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Glover, F., Taillard, E., De Werra, D.: A User’s Guide to Tabu Search. Annals of Operations Research 41, 3–28 (1993)zbMATHCrossRefGoogle Scholar
  15. 15.
    Landa-Silva, D., Obit, J.-H.: Great Deluge with Nonlinear Decay Rate for Solving Course Timetabling Problems. In: Proceedings of the 2008 IEEE Conference on Intelligent Systems (IS 2008), pp. 8.11–8.18. IEEE Press, Los Alamitos (2008)Google Scholar
  16. 16.
    Rossi-Doria, O., Sampels, M., Birattari, M., Chiarandini, M., Dorigo, M., Gambardella, L., Knowles, J., Manfrin, M., Mastrolilli, M., Paechter, B., Paquete, L., Stuetzle, T.: A Comparion of the Performance of Different Metaheuristics on the Timetabling Problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 333–352. Springer, Heidelberg (2003)Google Scholar
  17. 17.
    Schaerf, A.: A Survey of Automated Timetabling. Artificial Intelligence Review 13(2), 87–127 (1999)CrossRefGoogle Scholar
  18. 18.
    Socha, K., Knowles, J., Sampels, M.: A Max-min Ant System for the University Course 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
  19. 19.
    Socha, K., Sampels, M., Manfrin, M.: Ant Algorithms for the University Course Timetabling Problem with Regard to the State-of-the-Art. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 334–345. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  20. 20.
    Wren, V.: Scheduling, Timetabling and Rostering A Specail Relationship? In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 46–75. Springer, Heidelberg (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Joe Henry Obit
    • 1
  • Dario Landa-Silva
    • 1
  1. 1.ASAP Research Group, School of Computer ScienceUniversity of NottinghamUnited Kingdom

Personalised recommendations