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Algorithm selection and instance space analysis for curriculum-based course timetabling

Author

Listed:
  • Arnaud Coster

    (TU Wien)

  • Nysret Musliu

    (TU Wien)

  • Andrea Schaerf

    (University of Udine)

  • Johannes Schoisswohl

    (TU Wien)

  • Kate Smith-Miles

    (The University of Melbourne)

Abstract

We propose an algorithm selection approach and an instance space analysis for the well-known curriculum-based course timetabling problem (CB-CTT), which is an important problem for its application in higher education. Several state of the art algorithms exist, including both exact and metaheuristic methods. Results of these algorithms on existing instances in the literature show that there is no single algorithm outperforming the others. Therefore, a deep analysis of the strengths and weaknesses of these algorithms, depending on the instance, is an important research question. In this work, a detailed analysis of the instance space for CB-CTT is performed, charting the regions where these algorithms perform best. We further investigate the application of machine learning methods to automated algorithm selection for CB-CTT, strengthening the insights gained through the instance space analysis. For our research, we contribute new real-life instances and extend the generation of synthetic instances to better correspond to these new instances. Finally, this work shows how instance space analysis and the application of algorithm selection complement each other, underlining the value of both approaches in understanding algorithm performance.

Suggested Citation

  • Arnaud Coster & Nysret Musliu & Andrea Schaerf & Johannes Schoisswohl & Kate Smith-Miles, 2022. "Algorithm selection and instance space analysis for curriculum-based course timetabling," Journal of Scheduling, Springer, vol. 25(1), pages 35-58, February.
    • Handle: RePEc:spr:jsched:v:25:y:2022:i:1:d:10.1007_s10951-021-00701-x
    DOI: 10.1007/s10951-021-00701-x
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    References listed on IDEAS

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    1. Roberto Asín Achá & Robert Nieuwenhuis, 2014. "Curriculum-based course timetabling with SAT and MaxSAT," Annals of Operations Research, Springer, vol. 218(1), pages 71-91, July.
    2. Leo Lopes & Kate Smith-Miles, 2013. "Generating Applicable Synthetic Instances for Branch Problems," Operations Research, INFORMS, vol. 61(3), pages 563-577, June.
    3. 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.
    4. Mutsunori Banbara & Katsumi Inoue & Benjamin Kaufmann & Tenda Okimoto & Torsten Schaub & Takehide Soh & Naoyuki Tamura & Philipp Wanko, 2019. "$${\varvec{teaspoon}}$$ teaspoon : solving the curriculum-based course timetabling problems with answer set programming," Annals of Operations Research, Springer, vol. 275(1), pages 3-37, April.
    5. Edmund K. Burke & Jakub Mareček & Andrew J. Parkes & Hana Rudová, 2008. "Penalising Patterns in Timetables: Novel Integer Programming Formulations," Operations Research Proceedings, in: Jörg Kalcsics & Stefan Nickel (ed.), Operations Research Proceedings 2007, pages 409-414, Springer.
    6. Alex Bonutti & Fabio Cesco & Luca Gaspero & Andrea Schaerf, 2012. "Benchmarking curriculum-based course timetabling: formulations, data formats, instances, validation, visualization, and results," Annals of Operations Research, Springer, vol. 194(1), pages 59-70, April.
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    Cited by:

    1. Liu, Chang & Smith-Miles, Kate & Wauters, Tony & Costa, Alysson M., 2024. "Instance space analysis for 2D bin packing mathematical models," European Journal of Operational Research, Elsevier, vol. 315(2), pages 484-498.
    2. 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.

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