Skip to main content

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. 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. 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. 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. 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. Chakhlevitch, K., Cowling, P.: Hyperheuristics Recent Developments. In: Adaptative and multilevel metaheuristics, University of London, pp. 3–29 (2008)

    Google Scholar 

  8. Fogel, D.B., Owens, L.A., Walsh, M.: Artificial Intelligence through Simulated evolution. Wiley, New York (1966)

    MATH  Google Scholar 

  9. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  10. Goldberg, D., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis and first results. Complex Systems, 93–130 (1989)

    Google Scholar 

  11. Golberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison Wesley, New York (1989)

    Google Scholar 

  12. Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  14. Lewis, R.: A survey of metaheuristics-based techniques for University time-tabling problem. Spinger Online Publications (2007)

    Google Scholar 

  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. López, B., Jonhston, J.: Academic Task Assignment model using Genetic Algorithms, Intituto Tecnologico de Nuevo laredo, Mexico (2006)

    Google Scholar 

  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. Milena, K.: Solving Timetabling Problems Using Genetic Algorithms. In: Proceedings 27th spring seminar on electronics technology, University of Varna (2004)

    Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  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. Ozcan, E., Bilgen, B.: Hill climbers and mutational heuristics in hyperheuristics. Yeditep University, Stambul, pp. 202–211 (2006)

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Ross, P., Hart, E.: Some observations about GA-based exam Timetabling. University of Edinburg, United Kindom (2005)

    Google Scholar 

  27. Russell, S., Norving, P.: Artificial Intelligence A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2007)

    Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  32. Vazquez, J., Salhi, A.: A Robust Meta-Hyper-Heuristic approach to hybrid flowshop scheduling. SCI, vol. 49, pp. 125–142 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Jorge A., SA., Martin, CV.J., Hugo, TM. (2010). Academic Timetabling Design Using Hyper-Heuristics. In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15534-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15534-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15533-8

  • Online ISBN: 978-3-642-15534-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics