Feature-based tuning of single-stage simulated annealing for examination timetabling

Michele Battistutta (), Andrea Schaerf () and Tommaso Urli ()
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Michele Battistutta: University of Udine
Andrea Schaerf: University of Udine
Tommaso Urli: NICTA and The Australian National University

Annals of Operations Research, 2017, vol. 252, issue 2, No 3, 239-254

Abstract: Abstract We propose a simulated annealing approach for the examination timetabling problem, as formulated in the 2nd International Timetabling Competition. We apply a single-stage procedure in which infeasible solutions are included in the search space and dealt with using suitable penalties. Upon our approach, we perform a statistically-principled experimental analysis, in order to understand the effect of parameter selection on the performance of our algorithm, and to devise a feature-based parameter tuning strategy, which can achieve better generalization on unseen instances with respect to a one-fits-all parameter setting. The outcome of this work is that this rather straightforward search method, if properly tuned, is able to compete with all state-of-the-art specialized solvers on the available instances. As a byproduct of this analysis, we propose and publish a new, larger set of (artificial) instances that could be used for tuning and also as a ground for future comparisons.

Keywords: Examination timetabling; Local search; Simulated annealing; Metaheuristics; Linear regression; Feature-based parameter tuning (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-015-2061-8

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