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

Author

Listed:
  • Michele Battistutta

    (University of Udine)

  • Andrea Schaerf

    (University of Udine)

  • Tommaso Urli

    (NICTA and The Australian National University)

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.

Suggested Citation

  • Michele Battistutta & Andrea Schaerf & Tommaso Urli, 2017. "Feature-based tuning of single-stage simulated annealing for examination timetabling," Annals of Operations Research, Springer, vol. 252(2), pages 239-254, May.
    • Handle: RePEc:spr:annopr:v:252:y:2017:i:2:d:10.1007_s10479-015-2061-8
    DOI: 10.1007/s10479-015-2061-8
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    References listed on IDEAS

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    1. Barry McCollum & Andrea Schaerf & Ben Paechter & Paul McMullan & Rhyd Lewis & Andrew J. Parkes & Luca Di Gaspero & Rong Qu & Edmund K. Burke, 2010. "Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 120-130, February.
    2. David S. Johnson & Cecilia R. Aragon & Lyle A. McGeoch & Catherine Schevon, 1989. "Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning," Operations Research, INFORMS, vol. 37(6), pages 865-892, December.
    3. D. Abramson, 1991. "Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms," Management Science, INFORMS, vol. 37(1), pages 98-113, January.
    4. Edmund Burke & Rong Qu & Amr Soghier, 2014. "Adaptive selection of heuristics for improving exam timetables," Annals of Operations Research, Springer, vol. 218(1), pages 129-145, July.
    5. Michael W. Carter, 1986. "OR Practice—A Survey of Practical Applications of Examination Timetabling Algorithms," Operations Research, INFORMS, vol. 34(2), pages 193-202, April.
    6. Christos Gogos & Panayiotis Alefragis & Efthymios Housos, 2012. "An improved multi-staged algorithmic process for the solution of the examination timetabling problem," Annals of Operations Research, Springer, vol. 194(1), pages 203-221, April.
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    Cited by:

    1. Ceschia, Sara & Di Gaspero, Luca & Schaerf, Andrea, 2023. "Educational timetabling: Problems, benchmarks, and state-of-the-art results," European Journal of Operational Research, Elsevier, vol. 308(1), pages 1-18.
    2. Mats Carlsson & Sara Ceschia & Luca Gaspero & Rasmus Ørnstrup Mikkelsen & Andrea Schaerf & Thomas Jacob Riis Stidsen, 2023. "Exact and metaheuristic methods for a real-world examination timetabling problem," Journal of Scheduling, Springer, vol. 26(4), pages 353-367, August.
    3. Saeedeh Bazari & Alireza Pooya & Omid Soleimani Fard & Pardis Roozkhosh, 2023. "Modeling and solving the problem of scheduling university exams in terms of new constraints on the conflicts of professors' exams and the concurrence of exams with common questions," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 877-915, June.

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