Abstract
This paper investigates a number of approaches to encoding and crossover to support timetable design using genetic algorithms, thus extending the range of techniques available for solving such problems. Timetabling is used in this paper to refer to organising a weekly lecture timetable, as used in universities. In addition the algorithm is designed to produce a ‘good’ timetable as defined by a fitness function rather than merely a legal solution. The first approach to encoding timetabling dealt with in this paper uses a ‘greedy algorithm’ variant and a variety of standard crossover methods. The second encoding method searches a wider space of solutions but requires a new adaptation of existing order and position-based crossover algorithms. Results are compared with a traditional search technique and timetables provided by lecturers. These results demonstrate the effectiveness of genetic algorithms when used to optimise a timetable and introduce a combinatorial crossover operator which can deal with a more general class of problem than the normal order and position based operators. The greedy algorithm version of the genetic algorithm outperformed the other methods, despite the fact it cannot search the whole of the legal solution space.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
E. Burke, D. Elliman, and R. Weare. Specialised recombinative operators for timetabling problems. In Proc of the AISB, pages 75–85, Berlin, 1995. Springer-Verlag.
M.W. Carter, G. Laporte, and S.Y. Lee. Examination timetabling: Algorithmic strategies and applications. J.Opl Res. Soc, 47:373–383, 1996.
A. Colorni, M. Dorigo, and V. Maniezzo. Genetic algorithms and highly constrained problems: The time-table case. In Proc. of the First Workshop on PPSN, pages 55–59, 1990.
D. Corne and P. Ross. Some combinatorial landscapes on which genetic algorithm outperforms other stochastic iterative methods. In Proc of the AISB, Berlin, 1995. Springer-Verlag.
K. Dowsland. A timetabling problem in which clashes are inevitable. J. Opl Res. Soc., 41(10):907–918, 1990.
R. Feldman and M.C. Golumbic. Optimization algorithms for student scheduling via constraint satisfiability. Comp. J., 33(4):356–364, 1990.
A. Hertz. Tabu search for large scale timetabling problems. European Journal of Operational Research, 54:39–47, 1991.
G. Syswerda. Schedule Optimisation Using Genetic Algorithms, pages 332–349. Van Nostrand Reinhold, 1991.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Turton, B.C.H. (1998). Genetic Algorithms and the Timetabling Problem. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_60
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6492-1_60
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive