Abstract
The University Course Timetabling Problem is a combinatorial optimisation problem in which feasible assignments of lectures are sought. Weighted sums of violations of various constraints are used as a quality measure, with lower scores (costs) being more desirable. In this study, we develop a domain-specific many-objective optimiser, based on constructive heuristics and NSGA-III, in which the violations of different constraints are cast as separate objectives to be minimised concurrently. We show that feasible solutions can be attained consistently in a first phase and that a targeted objective can be fully optimised in a second phase. A set of non-dominated solutions is returned, representing a well-spread approximation to the Pareto front, from which a decision maker could ultimately choose according to a posteriori preferences.
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Sakal, J., Fieldsend, J., Keedwell, E. (2023). Towards a Many-Objective Optimiser for University Course Timetabling. In: Legrand, P., et al. Artificial Evolution. EA 2022. Lecture Notes in Computer Science, vol 14091. Springer, Cham. https://doi.org/10.1007/978-3-031-42616-2_10
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