Methodology of Design: A Novel Generic Approach Applied to the Course Timetabling Problem

  • Soria-Alcaraz Jorge A.Email author
  • Carpio Martin
  • Puga Héctor
  • Terashima-Marin Hugo
  • Cruz Reyes Laura
  • Sotelo-Figueroa Marco A.
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 294)


The Course Timetabling problem is one of the most difficult and common problems inside a university. The main objective of this problem is to obtain a timetabling with the minimum student conflicts between assigned activities. A Methodology of design is a framework of solution applied to a heuristic algorithm for timetabling problem. This strategy has recently emerged and aims to improve the obtained results as well as provide a context-independent layer to different versions of the timetabling problem. This methodology offers the researcher the advantage of solving different set instances with a single algorithm; which it is a new paradigm in the timetabling problem state of art. In this chapter the proposed methodology is described and tested with several metaheuristic algorithms over some well-known set instances, Patat 2002 and 2007. The main objectives in this chapter are: to show the construction of a two-phase algorithm based in a novel generic approach called design methodology and to find which metaheuristic algorithm shows a better performance in terms of quality. The design methodology generates set of generic structures: MMA, LPH, LPA and LPS. These structures build an independent context layer, so the two-phase algorithm only needs to solve the problem coded into them. No further specification or explicit codification of any problem-dependent constraint is needed inside the algorithm. This guarantee that in order to solve other instance of the Course timetabling problem, only it is needed the translation of the incoming instance into the proposed structures. With these structures the proposed methodology searches, in the first phase, for at least one feasible solution (a solution that has no conflict in the hard constraints). In a second phase the methodology utilizes this feasible solution in order to intensify the search around it, looking for the perfect solution (a solution with no conflict in any constraint hard or soft). Precisely for this two phases it is necessary the use of metaheuristic algorithms. This kind of algorithms does not guarantee to obtain the global optima but offers an opportunity to obtain a good solution in a reasonable time. The algorithms chosen to be tested along with the design methodology are from the area of evolutionary computation, Cellular algorithms and Swarm Intelligence. It is important to say that there exist several previous implementations of these metaheuristic algorithms over CTTP problems but this is the first time that these algorithms will be evaluated under a generic approach like the Methodology of design. Finally our experiments use some non-parametric statistical tests like Sing test, Kruskal-Wallis test and Wilcoxon signed rank test in order to identify the metaheuristic algorithm with the best performance over the course timetabling problem using the Methodology of Design.


Genetic Algorithm Particle Swarm Optimization Feasible Solution Differential Evolution Design Methodology 
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.
    Adriaen, M., Causmaecker, P., Demeester, P.: Tackling the university course timetabling problem with an aggregation approach. In: Burke, K., Rudova, H. (eds.) Proceedings PATAT 2006, pp. 330–335 (2006)Google Scholar
  2. 2.
    Alba, E., Dorronsoro, B.: Introduction to Cellular Genetic Algorithms. Cellular Genetic Algorithms, 3–20 (2008)Google Scholar
  3. 3.
    Alba, E., Dorronsoro, B.: The State of the Art in Cellular EvolitionaryAlgorithms. Cellular Genetic Algorithms 1, 21–34 (2008)CrossRefGoogle Scholar
  4. 4.
    Alba, E., Troya, J.M.: A survey of parallel distributed genetic algorithms, complexity, vol. 4(4), pp. 31–52 (1999)Google Scholar
  5. 5.
    Alba, E., Troya, J.M.: Cellular Evolutionary Algorithms: Evaluating the Influence of Ratio. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 29–38. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Alba, E., Troya, J.M.: Improving flexibility and efficiancy by adding parallelism to genetic algorithms. Statistics and Computing 12(2), 91–114 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Burke, E., Bykov, Y.,Newall, J., Petrovic, S.: A time-predefined local search approach to exam timetabling problems. Computer Science TEchnical Report No. NOTTCS-TR-2001-6, 1 (2001)Google Scholar
  8. 8.
    Martín, C., Jorge A., S.-A., Héctor J., P., Rosario, B., Manuel, O., Ernesto, M.L.: Variable Length Number Chains Generation without Repetitions. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds.) Soft Computing for Recognition Based on Biometrics. SCI, vol. 312, pp. 349–364. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Cambazard, H., Hebrard, E., O’Sullivan, B., Papadopoulos, A.: Submission to ICT: Track 2. International Timetabling Compertition 2007 (2008)Google Scholar
  10. 10.
    Colorni, A., Dorigo, M., Maniezzo, V.: Genetic Algorithms and Highly Constrained Problems: The Time-Table Case. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, Springer, Heidelberg (1991)CrossRefGoogle Scholar
  11. 11.
    Corne, D., Ross, P., Fang, H.: Fast Practical Evolutionary Timetabling. In: Fogarty, T.C. (ed.) AISB-WS 1994. LNCS, vol. 865, pp. 251–263. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  12. 12.
    Conant-Pablos, S.E., Magaña-Lozano, D.J., Terashima-Marín, H.: Pipelining Memetic Algorithms, Constraint Satisfaction, and Local Search for Course Timetabling. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds.) MICAI 2009. LNCS, vol. 5845, pp. 408–419. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Cooper Tim, B., Kingston, J.H.: The Compexity of Timetable Construction Problems. PhD thesis, The University of Sydney, 1995.Google Scholar
  14. 14.
    Dueck, G.: New Optimization Heuristics: The Great Deluge Algorithm and the Record-to-Record Travel. Journal of Computational Physics 104, 86–92 (1993)zbMATHCrossRefGoogle Scholar
  15. 15.
    Erben, W.: A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, p. 132. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Sheau, H., Deri, F.: University course timetable planning using hybrid particle swarm optimization. In: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 239–246 (2009)Google Scholar
  17. 17.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proccedings of IEEE International Conference on Neural Networks, vol. 1, pp. 1942–1948 (1995)Google Scholar
  18. 18.
    Kostuch. P.: Timetabling Competition-SA-based Heuristic. Metaheuristics Network (2003)Google Scholar
  19. 19.
    Angel, K.M.: A solution to the prisioner’s dilemma using an eclectic genetic algorithm. IPN 1 (2000)Google Scholar
  20. 20.
    Angel, K.M., Quezada, C.V.: A universal Eclectic Genetic Algorithm for constrained optimization. ITAM 1 (1998)Google Scholar
  21. 21.
    Lewis, R.: Metaheuristics for University Course Timetabling. PhD thesis, University of Notthingham (August 2006)Google Scholar
  22. 22.
    Price, K., Storn, R., Lampinen: Differential Evolution: A pratical approach to global optimization. Springer (2005)Google Scholar
  23. 23.
    Andrea, S., Di Gaspero, L.: Measurability and Reproducibility in University Timetabling Research: Discussion and Proposals. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 40–49. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    Sheau, F., Safaai, D., Hashim, S.: A study on PSO-Based University Course Timetabling Problem. In: International Conference on Advanced Computer Control, ICACC 2009, pp. 648–651 (2009)Google Scholar
  25. 25.
    Jorge. A., S.-A.: Diseño de horarios con respecto al alumno mediante técnicas de cómputo evolutivo. Master’s thesis, Instituto Tecnologico de León, 2010. Google Scholar
  26. 26.
    Jorge. A., S.-A., Carpio, M., Puga, H.: Diseño de Horarios mediante algoritmos géneticos. Décima Primera Reunión de Otoño de Potencia. In: Electrónica y Computación del IEEE, XI ROPEC, Morelia, vol. 1, pp. 24–35 (2009)Google Scholar
  27. 27.
    Jorge. A., S.-A., Terashima-Marin, H., Carpio, M.: Academic Timetabling Design using Hyper-heuristics. In: Advances in Soft Computing, ITT, vol. 1, pp. 158–164 (2010)Google Scholar
  28. 28.
    Jorge. A., S.-A., Martin, C., Terashima-Marin, H.: Several Strategies to Improve the Performance of Hyperheuristics for Academic Timetabling Design Problem. In: EEE Electronics, Robotics and Automative Mechanics Conference 2010. IEEE Computer Society, México (2010) ISBN: 978-0-7695-4204-1Google Scholar
  29. 29.
    Storn, R.: On the usage of differential evolution for function optimization. In: Biennial Conference of the North America Fuzzy Information Processing Society (NAFIPS), pp. 519–523 (1996)Google Scholar
  30. 30.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for gobal optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Willemen Robertus, J.: School Timetable Constructrion: Algorithms and complexity. PhD thesis, Institute for Programming research and Algorithms (2002)Google Scholar
  32. 32.
    Wolpert, H., Macready, G.: No free lunch Theorems for Search. Technical report The Santa Fe Institute, 1 (1996)Google Scholar
  33. 33.
    Yang, Y., Petrovic, S.: A Novel Similarity for Heuristic Selection in Examination Timetabling. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, p. 1. Springer, Heidelberg (2007)Google Scholar
  34. 34.
    Xin-She, Y.: Nature-Inspired Metaheuristics Algorithms, 2nd edn. Luniver Press (2010)Google Scholar
  35. 35.
    Yu, E., Sung, K.S.: A Genetic Algorithm for a University Wekly Courses Timetabling Problem. International Transactions in Operational Research 9, 703–717 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Soria-Alcaraz Jorge A.
    • 1
    Email author
  • Carpio Martin
    • 1
  • Puga Héctor
    • 1
  • Terashima-Marin Hugo
    • 1
  • Cruz Reyes Laura
    • 1
  • Sotelo-Figueroa Marco A.
    • 1
  1. 1.División de Estudios de Posgrado e InvestigaciónInstituto Tecnológico de LeónLeón GuanajuatoMéxico

Personalised recommendations