Skip to main content
SpringerLink
Log in

A new model for curriculum-based university course timetabling

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

The Curriculum Based university Course Timetabling (CCT) problem consists in determining the best scheduling of university course lessons in a given time interval, assigning the lessons of each course to classrooms and time periods, so that a series of constraints is satisfied. These constraints are divided into two categories: hard constraints, necessary so that the programming can actually be implemented, and soft constraints, which involve qualitative measures. This paper deals with the study of the CCT problem. We formulate a new and complete model that satisfies both the planning constraints and those on the compactness of the curricula, the distribution of the lessons (in the examined time frame), the teachers’ preferences, the minimum number of working days, maximum capacity and stability of the classrooms (which aims to minimize the daily movements of students among classrooms) so that the resulting timetable is of high quality. The formulated model, with appropriate adaptations, has been applied to the real case study of the first year of the Mathematics Degree Course of the University of Catania, Italy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abdennadher, S., Marte, M.: University timetabling using constraint handling rules. In: JFPLC, pp. 39–50 (1998)

  2. Akkoyunlu, E.A.: A linear algorithm for computing the optimum university timetable. Comput. J. 16(4), 347–350 (1973)

    Article  Google Scholar 

  3. Arbaoui, T., Boufflet, J.P., Moukrim, A.: L ower bounds and compact mathematical formulations for spacing soft constraints for university examination timetabling problems. Comput. Operat. Res. 106, 133–142 (2019)

    Article  Google Scholar 

  4. Bellio, R., Ceschia, S., Di Gaspero, L., Schaerf, A., Urli, T.: Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem. Comput. Operat. Res. 65, 83–92 (2016)

    Article  MathSciNet  Google Scholar 

  5. Bettinelli, A., Cacchiani, V., Roberti, R., Toth, P.: An overview of curriculum-based course timetabling. Top 23(2), 313–349 (2015)

    Article  MathSciNet  Google Scholar 

  6. Birbas, T., Daskalaki, S., Housos, E.: Timetabling for Greek high schools. J. Oper. Res. Soc. 48, 1191–1200 (1997)

    Article  Google Scholar 

  7. Bonutti, A., De Cesco, F., Di Gaspero, L., Schaerf, A.: Benchmarking curriculum-based course timetabling: formulations, dataformats, instances, validation, visualization, and results. Ann. Oper. Res. 194(1), 59–70 (2012)

    Article  Google Scholar 

  8. Burke, E., Marecek, J., Parkes, A., Rudova, H.: Decomposition, reformulation, and diving in university course timetabling. Comput. Oper. Res. 37(3), 582–597 (2010)

    Article  MathSciNet  Google Scholar 

  9. Burke, E., Petrovic, S.: Recent research directions in automated timetabling. Eur. J. Oper. Res. 140(2), 266–280 (2002)

    Article  Google Scholar 

  10. Cacchiani, V., Caprara, A., Roberti, R., Toth, P.: A new lower bound for curriculum-based course timetabling. Comput. Oper. Res. 40(10), 2466–2477 (2013)

    Article  MathSciNet  Google Scholar 

  11. Colajanni, G.: An Integer Programming Formulation for University Course Timetabling. AIRO Springer Series, To appear (2019)

    Book  Google Scholar 

  12. Colajanni, G., Daniele, P.: A cloud computing network and an optimization algorithm for IaaS providers. In: Proceedings of the Second International Conference on Internet of things and Cloud Computing, 5, ACM (2017)

  13. Colajanni, G., Daniele, P.: A Financial Model for a Multi-Period Portfolio Optimization Problem with a variational formulation. In: Khan, E., Kobis, C., A, Tammer (eds.) Variational Analysis and Set Optimization: Developments and Applications in Decision Making, pp. 31–54 (2018)

  14. Colajanni, G., Daniele, P.: A financial optimization model with short selling and transfer of securities. In: Daniele, P., Scrimali, L. (eds.) New Trends in Emerging Complex Real Life Problems, pp. 189–197. AIRO Springer Series, Cham (2018)

    Chapter  Google Scholar 

  15. Dimopoulou, M., Miliotis, P.: Implementation of a university course and examination timetabling system. Eur. J. Oper. Res. 130, 202–213 (2001)

    Article  Google Scholar 

  16. Di Gaspero, L., McCollum, B., Schaerf, A.: The second international timetabling competition (ITC-2007): Curriculum-based course timetabling (track 3). Technical Report. School of Electronics, Electrical Engineering and Computer Science, Queens University SARC Building, Belfast (2007)

  17. Fizzano, P., Swanson, S.: Scheduling classes on a college campus. Comput. Optim. Appl. 16(3), 279–294 (2000)

    Article  MathSciNet  Google Scholar 

  18. Hidalgo-Herrero, M., Rabanal, P., Rodriguez, I., Rubio, F.: Comparing problem solving strategies for NP-hard optimization problems. Fundam. Informa. 124(1–2), 1–25 (2013)

    Article  MathSciNet  Google Scholar 

  19. Lawrie, N.L.: An integer linear programming model of a school timetabling problem. Comput. J. 12, 307–316 (1969)

    Article  MathSciNet  Google Scholar 

  20. Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectr. 30(1), 167–90 (2008)

    Article  MathSciNet  Google Scholar 

  21. Lindahl, M., Mason, A.J., Stidsen, T., Sorensen, M.: A strategic view of University timetabling. Eur. J. Oper. Res. 266(1), 35–45 (2018)

    Article  MathSciNet  Google Scholar 

  22. Lopez, C., Beasley, J.E.: A note on solving MINLP’s using formulation space search. Optim. Lett. 8(3), 1167–1182 (2014)

    Article  MathSciNet  Google Scholar 

  23. McCollum, B., Schaerf, A., Paechter, B., McMullan, P., Lewis, R., Parkes, A., Di Gaspero, L., Qu, R., Burke, E.: Setting the research agenda in automated timetabling: the second international timetabling competition. INFORMS J. Comput. 22(1), 120–30 (2010)

    Article  Google Scholar 

  24. Petrovic, S., Burke, E.: University timetabling. Handbook of scheduling: algorithms, models, and performance analysis. Chapman Hall/CRC Press; 45 (2004)

  25. Schaerf, A.: A survey of automated timetabling. Artificial Intelligence Review 13(2), 87–127 (1999)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the Università degli Studi di Catania, “Piano della Ricerca 2016/2018 Linea di intervento 2”.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, 95125, Catania, Italy

    Gabriella Colajanni & Patrizia Daniele

Authors
  1. Gabriella Colajanni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrizia Daniele
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriella Colajanni.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Colajanni, G., Daniele, P. A new model for curriculum-based university course timetabling. Optim Lett 15, 1601–1616 (2021). https://doi.org/10.1007/s11590-020-01588-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-020-01588-x

Keywords

Search

Navigation