Academic Timetabling Design Using Hyper-Heuristics

  • Soria-Alcaraz Jorge A.
  • Carpio-Valadez J. Martin
  • Terashima-Marin Hugo
Part of the Studies in Computational Intelligence book series (SCI, volume 318)

Abstract

The Educational timetabling problem is a common and hard problem inside every educative institution, this problem tries to coordinate Students, Teachers, Classrooms and Timeslots under certain constrains that dependent in many cases the policies of each educational institution. The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. This paper presents a GA-based method that produces general hyper-heuristics for the educational timetabling design problem using API-Carpio methodology. The GA uses static-length representation; witch involves the complete encoding of a solution algorithm capable to solve schedule design instances. this hyper-heuristic is achieved by learning and testing phases using real instances from Intituto Tecnologico de León producing encouraging results for most of the instances. Finally we analyze the quality of our hyper-heuristic in the context of real Academic timetabling process.

Keywords

Genetic Algorithm Timetabling Educational Timetabling Heuristics Meta-heuristics Hyper-heuristics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aguayo. R., Carpio-Valadez, J. M. : Optimization and Automatization in task assignation applied to Academic Area. Master Degree Thesis, Instituto Tecnologico de León, México (2009)Google Scholar
  2. 2.
    Aparecido, L.: The school timetabling problem: a focus on elimination of open periods and isolated classes. In: Proceedings 6th International conference on hybrid intelligent systems (HIS 2006). IEEE, Los Alamitos (2006)Google Scholar
  3. 3.
    Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyperheuristics: An emerging direction in modern research technology. In: Handbook of Metaheuristics, pp. 457–474. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  4. 4.
    Carpio-Valadez, J.M.: Integral Model for the optimal academic task assigna-tion using a heuristic algorithm. In: Investigation in elecrical engineering, Mexico (2006)Google Scholar
  5. 5.
    Carpio-Valadez, J.M.: Integral Model for optimal assignation of academic tasks, encuentro de investigacion en ingenieria electrica. ENVIE, Zacatecas, 78–83 (2006)Google Scholar
  6. 6.
    Cowling, P., Chakhlevitch, K.: Using a large set of low level heuristics in hyperheuristics approach to personal scheduling. Studies in computational Intelligence (SCI), pp. 3–29 (2008)Google Scholar
  7. 7.
    Chakhlevitch, K., Cowling, P.: Hyperheuristics Recent Developments. In: Adaptative and multilevel metaheuristics, University of London, pp. 3–29 (2008)Google Scholar
  8. 8.
    Fogel, D.B., Owens, L.A., Walsh, M.: Artificial Intelligence through Simulated evolution. Wiley, New York (1966)MATHGoogle Scholar
  9. 9.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)MATHGoogle Scholar
  10. 10.
    Goldberg, D., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis and first results. Complex Systems, 93–130 (1989)Google Scholar
  11. 11.
    Golberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison Wesley, New York (1989)Google Scholar
  12. 12.
    Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  13. 13.
    Kendall, G.: A tabu-search hyper-heuristics approach to the examination time-tabling problem at the MARA University of Technology. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 270–293. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Lewis, R.: A survey of metaheuristics-based techniques for University time-tabling problem. Spinger Online Publications (2007)Google Scholar
  15. 15.
    Limin, H., Kendall, G.: An investigation of Tabu Assisted Hyper-heuristic Genetic Algorithm Automated Scheduling Optimization and planning research group, University of Notthingham (2006)Google Scholar
  16. 16.
    López, B., Jonhston, J.: Academic Task Assignment model using Genetic Algorithms, Intituto Tecnologico de Nuevo laredo, Mexico (2006)Google Scholar
  17. 17.
    Zhipeng, L., Hao, j.-l.: Solving the course timetabling problem with a hybrid heuristic algorithm. In: Dochev, D., Pistore, M., Traverso, P. (eds.) AIMSA 2008. LNCS (LNAI), vol. 5253, pp. 262–273. Springer, Heidelberg (2008)Google Scholar
  18. 18.
    Milena, K.: Solving Timetabling Problems Using Genetic Algorithms. In: Proceedings 27th spring seminar on electronics technology, University of Varna (2004)Google Scholar
  19. 19.
    Minton, S., Johnston, M.D., Phillips, A., Laird, P.: Minimizing conflicts: A heu-ristic repair method for csp and scheduling problems. Artificial Intelligence 58, 161–205 (1992)MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Minton, S., Phillips, A., Laird, P.: Solving large-scale csp and scheduling prob-lems using a heuristic repair method. In: Proceedings of 8th AAAI Conference, pp. 17–24 (1990)Google Scholar
  21. 21.
    Ozcan, E., Bilgen, B.: Hill climbers and mutational heuristics in hyperheuristics. Yeditep University, Stambul, pp. 202–211 (2006)Google Scholar
  22. 22.
    Padilla, F., Coello, C.: Generation of schedules with Genetic algorithms. In: Proceedings 2nd Congress on Evolutionary Computation, Mexico, pp. 159–163 (2005)Google Scholar
  23. 23.
    Pillay, N., Banzhaf, W.: A genetic Programming Approach to the generation of hyperheuristics for the incapacitated examination timetabling problem. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 223–234. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    Rattadilok, P., Gaw, A.: Distributed choice function hyper-heuristics for time-tabling and scheduling. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 51–67. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  25. 25.
    Martinez, R., Aguilera, Q.: Educational Timetabling generation with genetic algorithms, Memorias Segundo congreso de computación evolutiva. In: COMCEV, Aguascalientes México, pp. 159–163 (2005)Google Scholar
  26. 26.
    Ross, P., Hart, E.: Some observations about GA-based exam Timetabling. University of Edinburg, United Kindom (2005)Google Scholar
  27. 27.
    Russell, S., Norving, P.: Artificial Intelligence A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2007)Google Scholar
  28. 28.
    Terashima-Marín, H., Ross, P.: Evolution of constrain satisfaction strategies in examination timetabling. In: Proceedings GECCO 1999, pp. 635–642 (1999)Google Scholar
  29. 29.
    Terashima-Marín, H., Calleja-Manzanedo, R., Valenzuela-Rendon, M.: Genetic Algorithms for Dynamic Variable Ordering in Constrain Satisfaction Problems. Advances in Artificial Intelligence Theory 16, 35–44 (2005)Google Scholar
  30. 30.
    Terashima-Marín, H., Farías-Zárate, C.J., Ross, P., Valenzuela-Rendon, M.: A GA Based Method to Produce Generalized Hyper-heuristics for the 2D-Regular Cutting Stock Problem. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, Washington, USA, pp. 591–598 (2006)Google Scholar
  31. 31.
    Terashima-Marín, H., Ortiz-Bayliss, J.C., Ross, P., Valenzuela-Rend\’on, M.: Using Hyper-heuristics for the Dynamic Variable Ordering in Binary Constraint Satisfaction Problems. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS (LNAI), vol. 5317, pp. 407–417. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  32. 32.
    Vazquez, J., Salhi, A.: A Robust Meta-Hyper-Heuristic approach to hybrid flowshop scheduling. SCI, vol. 49, pp. 125–142 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Soria-Alcaraz Jorge A.
    • 1
  • Carpio-Valadez J. Martin
    • 1
  • Terashima-Marin Hugo
    • 2
  1. 1.Instituto Tecnológico de LeónMaestría en Ciencias en Ciencias de la ComputaciónLeón GuanajuatoMéxico
  2. 2.Tecnológico de Monterrey - Center for Intelligent SystemsMonterreyMéxico

Personalised recommendations