An online learning selection hyper-heuristic for educational timetabling

Steenson, Alexander and Özcan, Ender and Kheiri, Ahmed and McCollum, Barry and McMullan, Paul (2022) An online learning selection hyper-heuristic for educational timetabling. In: Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling. UNSPECIFIED, BEL, pp. 129-146. ISBN 978-0-9929984-3-1

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Abstract

Examination and course timetabling are computationally difficult real-world resource allocation problems. In 2007, an International Timetabling Competition (ITC) consisting of three classes: (i) examination timetabling, (ii) post enrollment-based, and (iii) curriculum-based course timetabling was organised. One of the competing algorithms, referred to as CPSolver, successfully achieved the first place in two out of these three tracks. This study investigates the performance of various multi-stage selection hyper-heuristics sequencing low-level heuristics/operators extending the CPSolver framework which executes hill climbing and two well-known local search metaheuristics in stages. The proposed selection hyper-heuristic is a multi-stage approach making use of a matrix which maintains transitional probabilities between each low-level heuristic to select the next heuristic in the sequence. A second matrix tracks the probabilities of ending the sequence on a given low-level heuristic. The best configuration for the selection hyper-heuristic is explored tailoring the heuristic selection process for the given timetabling problem class. The empirical results on the ITC 2007 problem instances show that the proposed selection hyper-heuristics can reduce the number of soft constraint violations, producing improved solutions over CPSolver as well as some other previously proposed solvers, particularly, in examination and curriculum-based course timetabling.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
146836
Deposited By:
Deposited On:
21 Jun 2021 15:05
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Aug 2021 07:26