A heuristic solution to the university timetabling problem

Author:

Adewumi Aderemi O.,Sawyerr Babatunde A.,Montaz Ali M.

Abstract

PurposeThe purpose of this paper is to consider the problem of university lecture timetabling. Timetabling deals with the problem of placing certain resources into a limited number of time slots, subject to given constraints, in order to satisfy a set of stated objectives to the highest possible extent. It is a well‐known and established NP‐hard problem. University timetabling is a major administrative activity especially in the third world universities. Solving the problem requires dynamic heuristics with predictable performance especially as the number of courses increases without corresponding increase in needed resources.Design/methodology/approachA genetic algorithm metaheuristic is designed to handle a real‐life case study. Given the present structure of the case study, a modular approach to the design of the timetable schedules is adopted. The approach considers timetable in a bottom‐up fashion at the various levels of department, faculty or entire university. Simulation study is conducted using the open source Java IDE, Eclipse® 3.0 in a window XP/vista environment running on a processor of 1.12 GHz.FindingsUsing the data sub‐set from the case study, simulation experiments are conducted based on the proposed method and obtained promising results.Research limitations/implicationsGiven the modular approach, the timetable system can easily be adapted to other various levels in the institution.Originality/valueWith reference to the case study, this is believed to be the first application of metaheuristics to a timetabling problem. The sensitivity analysis of the algorithm parameters is very valuable in guiding actual application development for the problem.

Publisher

Emerald

Subject

Computational Theory and Mathematics,Computer Science Applications,General Engineering,Software

Reference16 articles.

1. Adewumi, A.O., Ihemedu, N. and Ayeni, J.O.A. (2005), “An evolutionary‐based approach to university course time‐tabling problem”, First Annual Research Conference and Fair, Poster No. 9.11, University of Lagos, 26 October.

2. Adewumi, A.O., Fasina, E.P., Ayeni, J.O.A. and Ali, M.M. (2008), “A genetic algorithm metaheuristic for a multi‐stage hostel space allocation problem”, Proceedings of the 18th Triennial International Federation of Operation Research Society Conference (IFORS 2008), Sandton, pp. 12‐13.

3. Dammak, A., Elloumi, A. and Kamoun, H. (2006), “Lecture and tutorial timetabling at a Tunisia University”, in Burke, E.K. and Rudova, H. (Eds), Proceedings of the of the Sixth International Conference on Practice and Theory of Automated Timetabling (PATAT 2006), Masaryk University, Brno, pp. 384‐90.

4. Gendreau, M. (2003), “An introduction to Tabu Search”, in Kochenberger, G. and Glover, F. (Eds), Handbook of Metaheuristics, Kluwer Academic Publishers, Dordrecht, pp. 37‐54.

5. Glover, F. and Kochenberger, G.A. (Eds) (2003), Handbook of Metaheuristics, Kluwer Academic Publishers, Dordrecht.

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