Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s10479-015-2061-8 | Annals of Operations Research |
Keywords | DocType | Volume |
Examination timetabling,Local search,Simulated annealing,Metaheuristics,Linear regression,Feature-based parameter tuning | Journal | 252 |
Issue | ISSN | Citations |
2 | 0254-5330 | 2 |
PageRank | References | Authors |
0.38 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| michele battistutta | 1 | 2 | 0.38 |
| Andrea Schaerf | 2 | 2513 | 324.50 |
| Tommaso Urli | 3 | 79 | 8.66 |