A two-phase multiple objective approach to university timetabling utilising optimisation and evolutionary solution methodologies

S K Mirrazavi, S J Mardle () and M Tamiz
Additional contact information
S K Mirrazavi: Temposoft (UK) Ltd
S J Mardle: CEMARE, University of Portsmouth
M Tamiz: University of Portsmouth

Journal of the Operational Research Society, 2003, vol. 54, issue 11, 1155-1166

Abstract: Abstract The timetabling problem is generally large, highly constrained and discrete in nature. This makes solution by exact optimisation methods difficult. Therefore, often a heuristic search is deemed acceptable providing a simple (non-optimal) solution. This paper discusses the timetabling problem for a university department, where a large-scale integer goal programming (IGP) formulation is implemented for its efficient optimal solution in two phases. The first phase allocates lectures to rooms and the second allocates start-times to lectures. Owing to the size and complicated nature of the model, an initial analysis procedure is employed to manipulate the data to produce a more manageable model, resulting in considerable reductions in problem size and increase of performance. Both phases are modelled as IGPs. Phase 1 is solved using a state-of-the-art IGP optimisation package. However, due to the scale of the model, phase 2 is solved to optimality using a genetic algorithm approach.

Keywords: goal programming; timetabling; integer goal programming; multi-objective genetic algorithms (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (7)

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DOI: 10.1057/palgrave.jors.2601628

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