Feature-based tuning of single-stage simulated annealing for examination timetabling
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
http://link.springer.com/content/pdf/10.1007/s10479-015-2061-8.pdf
Reference41 articles.
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2. Alzaqebah, M., & Abdullah, S. (2014). An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling. Journal of Scheduling, 17, 249–262.
3. Alzaqebah, M., & Abdullah, S. (2015). Hybrid bee colony optimization for examination timetabling problems. Computers and Operations Research, 54, 142–154.
4. Bellio, R., Ceschia, S., Di Gaspero, L., Schaerf, A., & Urli, T. (2016). Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem. Computers & Operations Research, 65, 83–92.
5. Bellio, R., Di Gaspero, L., & Schaerf, A. (2012). Design and statistical analysis of a hybrid local search algorithm for course timetabling. Journal of Scheduling, 15(1), 49–61.
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