Improved local search approaches to solve the post enrolment course timetabling problem

Goh, Say Leng; Kendall, G.; Sabar, Nasser R.

Abstract

In this work, we are addressing the post enrollment course timetabling (PE-CTT) problem. We combine different local search algorithms into an iterative two stage procedure. In the first stage, Tabu Search with Sampling and Perturbation (TSSP) is used to generate feasible solutions. In the second stage, we propose an improved variant of Simulated Annealing (SA), which we call Simulated Annealing with Reheating (SAR), to improve the solution quality of feasible solutions. SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. SAR eliminates the need for extensive tuning as is often required in conventional SA. The proposed methodologies are tested on the three most studied datasets from the scientific literature. Our algorithms perform well and our results are competitive, if not better, compared to the benchmarks set by the state of the art methods. New best known results are provided for many instances.

Citation

Goh, S. L., Kendall, G., & Sabar, N. R. (2017). Improved local search approaches to solve the post enrolment course timetabling problem. European Journal of Operational Research, 261(1),

• Journal Article Type: Article
• Acceptance Date: Jan 26, 2017
• Online Publication Date: Jan 28, 2017
• Publication Date: Aug 16, 2017
• Deposit Date: Feb 5, 2018
• Publicly Available Date: Jan 29, 2019
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Electronic ISSN: 1872-6860
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 261
• Issue: 1
• Keywords: Timetabling, Combinatorial optimization, Local search, Tabu Search with Sampling and Perturbation (TSSP), Simulated Annealing with Reheating (SAR)
• Public URL: https://nottingham-repository.worktribe.com/output/877777
• Publisher URL: https://doi.org/10.1016/j.ejor.2017.01.040
• Contract Date: Feb 5, 2018