Advertisement

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

  • Jonas MockusEmail author
  • Lina Pupeikiene
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 107)

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.

Keywords

School Timetabling Optimization Data regularization Experimental investigation 

References

  1. 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)CrossRefzbMATHGoogle Scholar
  2. 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. 3.
    Birbas, T., Daskalaki, S., Housos, E.: School timetabling for quality student and teacher schedules. J. Scheduling 12, 177–197 (2009)CrossRefzbMATHGoogle Scholar
  4. 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. 5.
    Fudenberg, D., Tirole, J.: Game Theory. MIT, Boston (1983)zbMATHGoogle Scholar
  6. 6.
    Kingston, J.: The KTS high school timetabling system. http://sydney.edu.au/engineering/it/~jeff/kts.cgi (2009)
  7. 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. 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. 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. 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. 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. 12.
    Mockus, J.: Bayesian heuristic approach to global optimization and examples. J. Global Optim. 22, 191–203 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 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)CrossRefzbMATHGoogle Scholar
  14. 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. 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. 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. 17.
    Pillay, N.: A survey of school timetabling research. Ann. Oper. Res. 218, 261–293, 261–293 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 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. 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)CrossRefzbMATHGoogle Scholar
  20. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Pupeikiene, L., Mockus, J.: School Scheduling Optimization, Investigation and Applications. Lambert Academic Publishing, Saarbrücken (2010)Google Scholar
  22. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

Personalised recommendations