Applying evolutionary computation to the school timetabling problem: The Greek case

Computers and Operations Research

Volumn 35, Issue 4, 2008, Pages 1265-1280

  Beligiannis, Grigorios N ^a,b   Moschopoulos, Charalampos N ^a   Kaperonis, Georgios P ^a   Likothanassis, Spiridon D ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Artificial intelligence; Computer application; Educational organizations; Evolutionary algorithms; School timetabling problem

Indexed keywords

ADAPTIVE ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; RESOURCE ALLOCATION; SOCIETIES AND INSTITUTIONS;

EDUCATIONAL ORGANIZATIONS; SCHOOL TIMETABLING PROBLEMS;

EVOLUTIONARY ALGORITHMS;

EID: 34548643392     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2006.08.010     Document Type: Article

References (44)

1. Burke E.K., Jackson K., Kingston J.H., and Weare R.F. Automated university timetabling: the state of the art. The Computer Journal 40 9 (1997) 565-571
2. Kingston JH. A software architecture for timetable construction. In: Proceedings of the third international conference on practice and theory of automated timetabling, 2000. p. 472-80.
3. Kingston JH. A tiling algorithm for high school timetabling. In: Proceedings of the fifth international conference on practice and theory of automated timetabling, 2004. p. 233-49.
4. Abdennadher S., and Marte M. University course timetabling using constraint handling rules. Applied Artificial Intelligence 14 4 (2000) 311-325
5. Trick MA. A schedule-then-break approach to sports timetabling. Lecture notes in computer science, vol. 2079. Berlin: Springer; 2001. p. 242-53.
6. Burke E, Bykov Y, Petrovic S. A multicriteria approach to examination timetabling. Lecture notes in computer science, vol. 2079, Berlin: Springer; 2001. p. 118-31.
7. Rudová H, Murray K. University course timetabling with soft constraints. Lecture notes in computer science, vol 2740, Berlin: Springer; 2003. p. 310-28.
8. Dimopoulou M., and Miliotis P. Implementation of a university course and examination timetabling system. European Journal of Operational Research 130 1 (2001) 202-213
9. Schaerf A, Meisels A. Solving employee timetabling problems by generalized local search. Lecture notes in computer science, vol. 1792, Berlin: Springer; 2000. p. 380-89.
10. Burke E., Bykov Y., Newall J., and Petrovic S. A time-predefined local search approach to exam timetabling problems. IIE Transactions 36 6 (2004) 509-528
11. Burke EK, MacCarthy B, Petrovic S, Qu R. Case-based reasoning in course timetabling: an attribute graph approach. Lecture notes in computer science, vol. 2080, Berlin: Springer; 2001. p. 90-104.
12. Burke E.K., and Newall J.P. Solving examination timetabling problems through adaption of heuristic orderings. Annals of Operations Research 129 1-4 (2004) 107-134
13. Ross P, Hart E, Corne D. Genetic algorithms and timetabling. Natural computing series, advances in evolutionary computing: theory and applications. 2003. p. 755-77.
14. Carrasco MP and Pato MV. A multiobjective genetic algorithm for the class/teacher timetabling problem. Lecture notes in computer science, vol. 2079, Berlin: Springer; 2001. p. 3-17.
15. Fernandes C., Caldeira JP, Melicio F, Rosa AC. High school weekly timetabling by evolutionary algorithms. In: Symposium on applied computing, Proceedings of the 1999 ACM symposium on applied computing. 1999. p. 344-50.
16. Nedjah N, de Macedo Mourelle L. Evolutionary time scheduling. In: Proceedings of the international conference on information technology, coding and computing (ITCC'04), vol. 2, 2004. p. 357-61.
17. Di Gaspero L, Schaerf A. Tabu search techniques for examination timetabling. Lecture notes in computer science, vol. 2079, Berlin: Springer; 2001. p. 104-17.
18. Burke E.K., Kendall G., and Soubeiga E. A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics 9 6 (2003) 451-470
19. Socha K, Knowles J, Sampels M. A MAX-MIN ant system for the university course timetabling problem. Lecture notes in computer science, vol. 2463, Berlin: Springer; 2002. p. 1-13.
20. Rossi-Doria O, Sampels M, Birattari M, Chiarandini M, Dorigo M, Gambardella LM, et al. A comparison of the performance of different metaheuristics on the timetabling problem. Lecture notes in computer science, vol. 2740, Berlin: Springer; 2003. p. 329-51.
21. Smith K.A., Abramson D., and Duke D. Hopfield neural networks for timetabling: formulations, methods, and comparative results. Computers and Industrial Engineering 44 2 (2003) 283-305
22. Cambazard H, Demazeau F, Jussien N, David P. Interactively solving school timetabling problems using extensions of constraint programming. Lecture notes in computer science, vol. 3616, Berlin: Springer; 2005. p. 190-207.
23. Wren A. Scheduling, Timetabling and rostering-a special relationship. The practice and theory of automated timetabling. Lecture notes in computer science, vol. 1153, Berlin: Springer; 1996, p. 46-76.
24. Schaerf A. Local search techniques for large high school timetabling problems. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans 29 4 (1999) 368-377
25. Ten Eikelder HMM, Willemen RJ. Some complexity aspects of secondary school timetabling problems. Lecture notes in computer science, vol. 2079, Berlin: Springer; 2001. p. 18-27.
26. Papoutsis K., Valouxis C., and Housos E. A column generation approach for the timetabling problem of greek high schools. Journal of the Operational Research Society 54 3 (2003) 230-238
27. Valouxis C., and Housos E. Constraint programming approach for school timetabling. Computers and Operations Research 30 10 (2003) 1555-1572
28. Caldeira JP, Rosa A. School timetabling using genetic search. In: Proceedings of the second international conference on the practice and theory of automated timetabling (PATAT'97), 1997. p. 115-22.
29. Fernandes C, Caldeira JP, Melicio F, Rosa AC. Evolutionary algorithm for school timetabling. In: Proceedings of the genetic and evolutionary computation conference (GECCO' 99), vol. 2, 1999. p. 1777-83.
30. Bufé M, Fischer T, Gubbels H, Häcker C, Hasprich O, Scheibel C, et al. Automated solution of a highly constrained school timetabling problem-preliminary results. Lecture notes in computer science, vol. 2037, Berlin: Springer; 2001. p. 431-40.
31. Tavares R, Teofilo A, Silva P, Rosa A. Infected genes evolutionary algorithm. In: Symposium on applied computing (SAC'99), 1999. p. 333-8.
32. Santiago-Mozos R., Salcedo-Sanz S., DePrado-Cumplido M., and Bousoño-Calzón C. A two-phase heuristic evolutionary algorithm for personalizing course timetables: a case study in a Spanish University. Computers and Operations Research 32 7 (2005) 1761-1776
33. Di Stefano C, Tettamanzi AGB. An evolutionary algorithm for solving the school timetabling problem. In: Boers EJW et al. editors. Lecture notes in computer science, vol. 2037, Berlin, Heidelberg: Springer; 2001. p. 452-62.
34. Filho GR, Lorena LAN. A constructive evolutionary approach to school timetabling. Lecture notes in computer science, vol. 2037, Berlin: Springer; 2001. p. 130-39.
35. Wilke P, Gröbner M, Oster N. A hybrid genetic algorithm for school timetabling. Lecture notes in computer science, vol. 2557, Berlin: Springer; 2002. p. 455-64.
36. Alvin C, Kwan M, Ken C, Chung K, Kammy K, Yip K, Tam V. An automated school timetabling system using hybrid intelligent techniques. Lecture notes in computer science, vol. 2871, Berlin: Springer; 2003. p. 124-34.
37. Beligiannis GN, Moschopoulos CN, Likothanassis SD. A genetic algorithm approach to school timetabling. Journal of the Operational Research Society, submitted for publication.
38. Ross P, Corne D, Fang H. Successful lecture timetabling with evolutionary algorithms. In: Proceedings of the workshop on applied genetic and other evolutionary algorithms (ACAI'94), 1994.
39. Goldberg D.E. Genetic algorithms in search, optimization and machine learning (1989), Addison-Wesley, Reading, MA
40. Michalewicz Z. Genetic algorithms + data structures = evolution programs. 3rd ed. (1996), Springer, New York
41. Blickle T., and Thiele L. A comparison of selection schemes used in genetic algorithms. Evolutionary Computation 4 4 (1997) 361-394
42. Mitchell M. An introduction to genetic algorithms (1995), The MIT Press, Cambridge, MA, London, UK
43. Bäck T. Evolutionary algorithms in theory and practice (1996), Oxford University Press, Oxford
44. Reeves CR, Wright CC. Epistasis in genetic algorithms: an experimental design perspective. In: Proceedings of the sixth international conference on genetic algorithms, 1995. p. 217-24.