Skip to main content
Springer Nature Link
Log in

Investigation of Data Regularization and Optimization of Timetables by Lithuanian High Schools Example

  • Chapter
  • First Online:
Advances in Stochastic and Deterministic Global Optimization

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 107))

  • 1397 Accesses

Abstract

In practice, we must first assign teachers and students to subject-groups for school applications. In the Lithuanian high schools, the number of subject-groups can be very large, since students are free to select just a small subset of optional subjects.

The experimental investigation of this chapter did show that in such conditions, some regularization of subject-groups is needed for prior to optimization. The regularization is a sequential elimination of the timetable-breakers. A timetable-breaker is a student or teacher the presence of which in a subject-group is most harmful for the timetable. The automatic elimination of breakers is difficult due to many subjective factors. In practice it is done by an expert trying to change the subject-group accordingly. In the case of teachers the personal communication is used, if the group changes do not help.

The application of optimization algorithms for timetabling data regularization is the new result of this work. New also is the experimental investigation applying optimization algorithms in 39 Lithuanian high schools.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Beligiannis, G., Moschopoulos, C., Kaperonis, G., Likothanassis, S.: Applying evolutionary computation to the school timetabling problem: the Greek case. Comput. Oper. Res. 35, 1265–1280 (2008)

    Article  MATH  Google Scholar 

  2. Bello, G., Rangel, M., Boeres, M.: An approach for the class/teacher timetabling problem. In: Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling PATAT2008, 18–22 August 2008. Universite de Montreal, Montreal (2008)

    Google Scholar 

  3. Birbas, T., Daskalaki, S., Housos, E.: School timetabling for quality student and teacher schedules. J. Scheduling 12, 177–197 (2009)

    Article  MATH  Google Scholar 

  4. Cohen-Zamir, I., Bar., D.: School time tabling ITT software. In: Practice and Theory of Automated Timetabling (PATAT 2012), 29–31 August 2012, pp. 466–477. Son, Norway (2012)

    Google Scholar 

  5. Fudenberg, D., Tirole, J.: Game Theory. MIT, Boston (1983)

    MATH  Google Scholar 

  6. Kingston, J.: The KTS high school timetabling system. http://sydney.edu.au/engineering/it/~jeff/kts.cgi (2009)

  7. Kingston, J.: Data formats for exchange of real-world timetabling problem instances and solutions. In: Proceedings of PATAT10, Belfast, August 2010, pp. 513–516. Queen’s University, Belfast (2010)

    Google Scholar 

  8. Kingston, J.: Solving the general high school timetabling problem. In: Proceedings of PATAT10, Belfast, August 2010, pp. 517–518. Queen’s University, Belfast (2010)

    Google Scholar 

  9. Kingston, J.: Timetable construction: the algorithms and complexity perspective. In: Proceedings of PATAT10, Belfast, August 2010, pp. 26–36. Queen’s University, Belfast (2010)

    Google Scholar 

  10. Kingston, J.H.: Repairing high school timetables with polymorphic ejection chains. In: Practice and Theory of Automated Timetabling (PATAT 2012), 29–31 August 2012, pp. 16–30. Son, Norway (2012)

    Google Scholar 

  11. Minh, K.N., Thanh, N.D., Trang, K.T., Hue, N.T.: Using tabu search for solving a high school timetabling problem. Stud. Comput. Intell. 283, 305–313 (2010)

    Google Scholar 

  12. Mockus, J.: Bayesian heuristic approach to global optimization and examples. J. Global Optim. 22, 191–203 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  13. Moura, A.V., Scaraficci, R.A.: A GRASP strategy for a more constrained school timetabling problem. Int. J. Oper. Res. 7, 152–170 (2010)

    Article  MATH  Google Scholar 

  14. Ostler, J., Wilke, P.: The Erlangen advanced timetabling system (EATTS) unified xml file format for the specification of timetabling systems. In: Proceedings of PATAT10, Belfast, August 2010, pp. 447–464. Queen’s University, Belfast (2010)

    Google Scholar 

  15. Pillay, N.: A study into hyper-heuristics for the school timetabling problem. In: SAICSIT 2010: fountains of computing research, pp. 258–264. ACM for Computing Machinery, New York (2010)

    Google Scholar 

  16. Pillay, N.: An overview for school timetabling. In: Proceedings of PATAT10, Belfast, August 2010, pp. 321–335. Queen’s University, Belfast (2010)

    Google Scholar 

  17. Pillay, N.: A survey of school timetabling research. Ann. Oper. Res. 218, 261–293, 261–293 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  18. Post, G., Kingston, J.H., Ahmadi, S., Daskalaki, S., Gogos, C., Kyngas, J., Nurmi, C., Santos, H., Rorije, B., Schaerf, A.: An XML format for benchmarks in high school. In: Proceedings of PATAT10, Belfast, August 2010, pp. 347–352. Queen’s University, Belfast (2010)

    Google Scholar 

  19. Post, G., Ahmadi, S., Daskalaki, S., Kingston, J.H., Kyngas, J., Nurmi, C.: An XML format for benchmarks in high school timetabling. Ann. Oper. Res. 194, 385–397 (2012)

    Article  MATH  Google Scholar 

  20. Post, G., Kingston, J.H., Ahmadi, S., Daskalaki, S., Gogos, C., Kyngas, J., Nurmi, C., Musliu, N., Pillay, N., Santos, H., Schaerf, A.: XHSTT: an XML archive for high school timetabling problems in different countries. Ann. Oper. Res. 218, 295–301 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  21. Pupeikiene, L., Mockus, J.: School Scheduling Optimization, Investigation and Applications. Lambert Academic Publishing, Saarbrücken (2010)

    Google Scholar 

  22. Raghavjee, R., Pillay, N.: An application of genetic algorithms to the school timetabling problem. In: Proceedings of SAICSIT 6–8 October 2008, Nelson Mandela Metropolitan University Port Elizabeth South Africa, pp. 193–199. ACM, New York (2008)

    Google Scholar 

  23. Raghavjee, R., Pillay, N.: An informed genetic algorithm for the high school timetabling problem. In: SAICSIT 2010: Fountains of Computing Research, pp. 408–412. ACM for Computing Machinery, New York (2010)

    Google Scholar 

  24. Raghavjee, R., Pillay, N.: Using genetic algorithms to solve the South African school timetabling problem. In: Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC’10), pp. 286–292. IEEE, New York (2010)

    Google Scholar 

  25. Ribic, S., Konjicija, S.: A two phase integer programming approach to solving the school timetabling problem. In: Proceedings of the International Conference on Information Technology Interfaces (ITI), pp. 651–656 (2010)

    Google Scholar 

  26. Santos, H., Uchoa, E., Ochi, L., Maculan, N.: Strong bounds with cut and column generation for class-teacher timetabling. In: Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling PATAT2008, 18–22 August 2008. Universite de Montreal, Montreal (2008)

    Google Scholar 

  27. Sørensen, M., Stidsen, T.R.: High school timetabling: modeling and solving a large number of cases in Denmark. In: Practice and Theory of Automated Timetabling (PATAT 2012), 29–31 August 2012, pp. 359–364. Son, Norway (2012)

    Google Scholar 

  28. Tao, B., Dwyer, R.: Aspen scheduler: a web-based automated master schedule builder for secondary schools. In: Practice and Theory of Automated Timetabling PATAT 2012, pp. 29–31. Sun, Norway (2004)

    Google Scholar 

  29. Valouxis, C., Gogos, C., Alefragis, P., Housos, E.: Decomposing the high school timetable problem. In: Practice and Theory of Automated Timetabling (PATAT 2012), 29–31 August 2012, pp. 209–221. Son, Norway (2012)

    Google Scholar 

  30. Wilke, P., Killer, H.: Comparison of algorithms solving school and course time tabling problems using the Erlangen Advanced Time Tabling System (EATTS). In: Proceedings of PATAT10, Belfast, August 2010, pp. 427–439. Queen’s University, Belfast (2010)

    Google Scholar 

  31. Wilke, P., Killer, H.: Walk up jump down - a new hybrid algorithm for time tabling problems. In: Proceedings of PATAT10, Belfast, August 2010, pp. 440–447. Queen’s University, Belfast (2010)

    Google Scholar 

  32. Wilke, P., Ostler, J.: Solving the school timetabling problem using Tabu search, simulated annealing, genetic and branch and bound algorithms. In: Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling PATAT2008, 18–22 August 2008. Universite de Montreal, Montreal (2008)

    Google Scholar 

  33. Wilke, P., Ostler, J.: The Erlangen Advanced Time Tabling System (EATTS) unified XML file format for the specification of time tabling systems. In: Proceedings of PATAT10, Belfast, August 2010, pp. 447–465. Queen’s University, Belfast (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Mathematics and Informatics, Vilnius University, Akademijos 4, 08663, Vilnius, Lithuania

    Jonas Mockus & Lina Pupeikiene

Authors
  1. Jonas Mockus
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lina Pupeikiene
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonas Mockus .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Cardiff School of Mathematics, Cardiff University, Cardiff, United Kingdom

    Anatoly Zhigljavsky

  3. Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania

    Julius Žilinskas

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Mockus, J., Pupeikiene, L. (2016). Investigation of Data Regularization and Optimization of Timetables by Lithuanian High Schools Example. In: Pardalos, P., Zhigljavsky, A., Žilinskas, J. (eds) Advances in Stochastic and Deterministic Global Optimization. Springer Optimization and Its Applications, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-29975-4_9

Download citation

Publish with us

Policies and ethics