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

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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.

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Notes

  1. http://www.cs.qub.ac.uk/itc2007/winner/atsuta.htm.

  2. These instances are actually 20, given that for our formulation two of them, namely comp03 and comp15, are identical.

  3. https://code.google.com/archive/p/udinettgen/.

  4. https://matilda.unimelb.edu.au.

  5. https://cdlab-artis.dbai.tuwien.ac.at/papers/cb-ctt/.

  6. https://matilda.unimelb.edu.au/matilda/problems/opt/tt#tt.

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Acknowledgements

The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. This work was also supported by the Austrian Development Cooperation: Project HERAS – Higher Education, Research and Applied Science. Support from the Australian Research Council is also acknowledged through the Laureate Fellowship grant FL140100012.

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Authors and Affiliations

  1. Database and Artificial Intelligence Group, Institute of Logic and Computation, TU Wien, Vienna, Austria

    Arnaud De Coster & Johannes Schoisswohl

  2. Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling DBAI, TU Wien, Vienna, Austria

    Nysret Musliu

  3. DPIA, University of Udine, Udine, Italy

    Andrea Schaerf

  4. School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia

    Kate Smith-Miles

Authors
  1. Arnaud De Coster
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  2. Nysret Musliu
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  3. Andrea Schaerf
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  4. Johannes Schoisswohl
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  5. Kate Smith-Miles
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Correspondence to Arnaud De Coster or Johannes Schoisswohl.

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De Coster, A., Musliu, N., Schaerf, A. et al. Algorithm selection and instance space analysis for curriculum-based course timetabling. J Sched 25, 35–58 (2022). https://doi.org/10.1007/s10951-021-00701-x

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