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An Evolutionary Algorithm for Solving the School Time-Tabling Problem

  • Calogero Di Stefano
  • Andrea G. B. Tettamanzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

This paper describes an evolutionary algorithm for school time-tabling, demonstrated through applications to the Italian school system. Heuristics have been found and perfected which offer good generalization capabilities. A particular attention has been devoted to problem formulation, also in terms of fuzzy logic, as well as to testing different genetic operators and parameter settings. This work has obtained results of remarkable practical relevance on real-world problem instances illustrated in the paper, and eventually gave rise to a successful commercial product.

Keywords

Evolutionary Algorithm Problem Instance Soft Constraint Hard Constraint Perturbation Operator 
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.

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References

  1. 1.
    S. Even, A. Itai, A. Shamir. On the Complexity of Timetable and Multicommodity Flow Problems. Siam Journal of Computing, Vol. 5,No.4, December 1976, 691–703.MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    W. Erben. A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling. Proceedings of the Third International Conference on the Practice and Theory of Automated Timetabling, Constance, Germany, August 16-18, 2000.Google Scholar
  3. 3.
    P. Adamidis and P. Arakapis. Weekly lecture timetabling with genetic algorithms. Proceedings of the 2nd International Conference on the Practice and Theory of Automated Timetabling, University of Toronto, Canada, 1997.Google Scholar
  4. 4.
    J.P. Caldeira and A.C Rosa. School timetabling using genetic search. Proceedings of the 2nd International Conference on the Practice and Theory of Automated Timetabling, University of Toronto, Canada, 1997.Google Scholar
  5. 5.
    L.A. Zadeh. Fuzzy Sets and Applications: Selected Papers. John Wiley & Sons, New York, 1987.zbMATHGoogle Scholar
  6. 6.
    A.M. Barham and J.B. Westwood. A Simple Heuristic to Facilitate Course Timetabling. J. Opnl. Res. Soc. 29, 1055–1060.Google Scholar
  7. 7.
    D. Corne, P. Ross, H. Fang. Evolutionary Timetabling: Practice, Prospects and Work in Progress. Presented at the UK Planning and Scheduling SIG Workshop, (Strathclyde, UK, September 1994), organised by P Prosser.Google Scholar
  8. 8.
    B. Paechter, R.C. Rankin, A. Cumming. Improving a Lecture Timetabling System for University Wide Use. Practice and Theory of Automated Timetabling II, Springer-Verlag, LNCS 1408, Berlin, 1998.CrossRefGoogle Scholar
  9. 9.
    A. Brindle. Genetic algorithms for function optimization. Technical Report TR81-2, Department of Computer Science, University of Alberta, Edmonton, 1981.Google Scholar
  10. 10.
    Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, 1992.CrossRefzbMATHGoogle Scholar
  11. 11.
    D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Calogero Di Stefano
    • 1
  • Andrea G. B. Tettamanzi
    • 2
  1. 1.Genetica S.r.l.MilanItaly
  2. 2.Dipartimento di Tecnologie dell’InformazioneUniversità degli Studi di MilanoCremaItaly

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