Advertisement

A genetic algorithm solving a weekly course-timetabling problem

  • Wilhelm Erben
  • Jürgen Keppler
Genetic Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1153)

Abstract

In this paper we describe a heavily constrained university timetabling problem, and our genetic algorithm based approach to solve it. A problem-specific chromosome representation and knowledge-augmented genetic operators have been developed; these operators ‘intelligently’ avoid building illegal timetables. The prototype timetabling system which is presented has been implemented in C and PROLOG, and includes an interactive graphical user interface. Tests with real data from our university were performed and yield promising results.

Keywords

Genetic Algorithm Genetic Operator Soft Constraint Hard Constraint Timetabling Problem 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Abramson, D. (1991): Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms. Management Science 37/1:98–113Google Scholar
  2. [2]
    Burke, E.K.; Elliman, D.G.; Weare, R. (1994): A Genetic Algorithm for University Timetabling. Proceedings of the AISB Workshop on Evolutionary Computing, LeedsGoogle Scholar
  3. [3]
    Burke, E.K.; Elliman, D.G.; Weare, R. (1995): A Genetic Algorithm Based University Timetabling System. In: Proceedings of the 2nd East-West International Conference on Computer Technologies in Education, Sep. 19–23, Crimea, Ukraine, Vol. I:35–40Google Scholar
  4. [4]
    Colorni, A.; Dorigo, M.; Maniezzo, V. (1990): A Genetic Algorithm to Solve the Time-table Problem. Technical Report No. 90-060, Politecnico di Milano, ItalyGoogle Scholar
  5. [5]
    Colorni, A.; Dorigo, M.; Maniezzo, V. (1991): Genetic Algorithms and Highly Constrained Problems: The Time-Table Case. In: Schwefel, H.-P.; Männer, R. (eds.): Parallel Problem Solving from Nature. Proceedings of the First Workshop PPSN I, Dortmund, Oct. 1–3. Springer, Berlin:55–59Google Scholar
  6. [6]
    Corne, D.; Fang, H.-L.; Mellish, C. (1993): Solving the Module Exam Scheduling Problem with Genetic Algorithms. In: Chung; Lovegrove; Ali (eds.): Proceedings of the Sixth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: 370–373Google Scholar
  7. [7]
    Corne, D.; Ross, P.; Fang, H.-L. (1994): Fast Practical Evolutionary Timetabling. In: Fogarty, T.C. (ed.): Evolutionary Computing. AISB Workshop, Leeds, U.K., April 11–13. Selected Papers. LNCS 865. Springer, Berlin:250–263Google Scholar
  8. [8]
    Davis, L.; Steenstrup, M. (1987): Genetic Algorithms and Simulated Annealing: An Overview. In: Davis, L. (ed.), Genetic Algorithms and Simulated Annealing. Morgan Kaufmann Publishers Inc., Los Altos, CA:1–11Google Scholar
  9. [9]
    East, N. (1995): Genetische Algorithmen fir Stundenpläne. Final Year Project Dissertation, University of Nottingham, U.K., and Fachhochschule Konstanz, GermanyGoogle Scholar
  10. [10]
    Erben, W. (1995): Timetabling Using Genetic Algorithms, in: Pearson, D.W.; Steele, N.C.; Albrecht, R.F. (eds.): Artificial Neural Nets and Genetic Algorithms. Proceedings of the International Conference in Alès/France. Springer, Wien:30–32Google Scholar
  11. [11]
    Evan, S.; Itai, A.; Shamir, A. (1976): On the Complexity of Timetable and Multicom-modity Flow Problems. SIAM Journal of Computing vol. 5, no.4:691–703Google Scholar
  12. [12]
    Kang, L.; White, G.M. (1992): A Logic Approach to the Resolution of Constraints in Timetabling. European Journal of Operational Research 61:306–317Google Scholar
  13. [13]
    Ling, S.E. (1992): Integrating Genetic Algorithms with a Prolog Assignment Program as a Hybrid Solution for a Polytechnic Timetable Problem. In: Männer, R.; Manderick, B. (eds.): Parallel Problem Solving from Nature, Proceedings of the Second Conference on PPSN, Brussels, Sep. 28–30. North-Holland, Amsterdam:321–329Google Scholar
  14. [14]
    Michalewicz, Z. (1992): Genetic Algorithms + Data Structures=Evolution Programs. Springer, BerlinGoogle Scholar
  15. [15]
    Werra, D. de (1985): An Introduction to Timetabling. European Journal of Operational Research 19:151–162Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Wilhelm Erben
    • 1
  • Jürgen Keppler
    • 1
  1. 1.Department of Computer ScienceFachhochschule KonstanzKonstanz

Personalised recommendations