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Stochastic Optimisation Timetabling Tool for university course scheduling

Lookup NU author(s): Professor Christian Hicks


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University timetabling is an NP-hard problem, which means that the amount of computation required to find solutions increases exponentially with problem size. Timetabling is subject to hard constraints that must be satisfied in order to produce feasible timetables and soft constraints, which are not absolutely essential. This paper describes the Stochastic Optimisation Timetabling Tool (SOTT) that has been developed for university course timetabling. Genetic Algorithms (GA), Simulated Annealing (SA) and random search are embedded in the SOTT. The algorithms include a repair process, which ensures that all infeasible timetables are rectified. This prevents clashes and ensures that the rooms are sufficiently large to accommodate the classes. The algorithms also evaluate timetables in terms of soft constraints: minimising student movement; avoiding fragmentation in the timetables for students and lecturers; and satisfying lecturers' preferences for the timing of classes. The algorithms were tested using two sets of timetabling data from a collaborating university. Both GA and SA produced very good timetables, but the results obtained from SA were slightly better than those using GA. However, the GA was 54% faster than SA. © 2007 Elsevier B.V. All rights reserved.

Publication metadata

Author(s): Pongcharoen P, Promtet W, Yenradee P, Hicks C

Publication type: Article

Publication status: Published

Journal: International Journal of Production Economics

Year: 2008

Volume: 112

Issue: 2

Pages: 903-918

Print publication date: 01/04/2008

ISSN (print): 0925-5273

ISSN (electronic):

Publisher: Elsevier


DOI: 10.1016/j.ijpe.2007.07.009


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