A genetic algorithm approach to school timetabling

Journal of the Operational Research Society

Volumn 60, Issue 1, 2009, Pages 23-42

  Beligiannis, G N ^a,b   Moschopoulos, C ^c   Likothanassis, S D ^c  

^a Achaikis Sympoliteias 55 57   (Greece)

^b UNIVERSITY OF IOANNINA   (Greece)

^c UNIVERSITY OF PATRAS   (Greece)

Author keywords

Artificial intelligence; Computers; Education; GAs; Timetabling

Indexed keywords

ADAPTIVE ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTERS; EDUCATION; GALLIUM; GENETIC ALGORITHMS;

ADAPTIVE CAPABILITIES; COMPUTATIONAL INTELLIGENCE TECHNIQUES; EDUCATIONAL ORGANIZATIONS; EFFECTIVE ALGORITHMS; GENETIC ALGORITHM APPROACH; OPERATIONAL RESEARCH; TIMETABLING; TIMETABLING PROBLEM;

SCHEDULING;

EID: 57649146372     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/palgrave.jors.2602525     Document Type: Conference Paper

References (75)

1. Abdennadher S and Marte M (2000). University course timetabling using constraint handling rules. Appl Artif Intell 14: 311-325.
2. Abraham A, Jain LC and Goldberg R (eds) (2005). Evolutionary Multi-objective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing). Springer: London.
3. Adriaen M, De Causmaecker P, Demeester P and Berghe GV (2006). Tackling the university course timetabling problem with an aggregation approach. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 330-335.
4. Asmuni H, Burke EK, Garibaldi JM and McCollum B (2006). A novel fuzzy approach to evaluate the quality of examination timetabling. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 82-102.
5. Bäck T, Fogel D and Michalewicz Z (eds) (2000a). Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics: Bristol.
6. Bäck T, Fogel D and Michalewicz Z (eds) (2000b). Evolutionary Computation 2: Advanced Algorithms and Operators. Institute of Physics: Bristol.
7. Baker JE (1987). Reducing bias and inefficiency in the selection algorithm. In: Grefenstette JJ (ed). Proceedings of the 2nd International Conference on Genetic Algorithms and their Application. Lawrence Erlbaum Associates: Hillsdale, NJ, USA, pp. 14-21.
8. Bardadym VA (1996). Computer aided school and timetabling: the new wave. In: Burke EK and Ross P (eds). The Practice and Theory of Automated Timetabling: Selected Papers from the 1st International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 1153. Springer-Verlag: Berlin, pp. 22-45.
9. Bilgin B, Özcan E and Korkmaz EE (2006). An experimental study on hyper-heuristics and exam timetabling. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 123-140.
10. Birattari M, Rossi-Doria O, Sampels M and Chiarandini M (2003). A comparison of the performance of different metaheuristics on the timetabling problem. In: Burke EK and De Causmaecker P (eds). Practice and Theory of Automated Timetabling IV: 4th International Conference, PATAT 2002. Lecture Notes in Computer Science, Vol. 2740. Springer-Verlag: Berlin, pp. 329-351.
11. Blickle T and Thiele L (1996). A comparison of selection schemes used in genetic algorithms. Evolutionary Computation 4: 361-394.
12. Bufé M, Fischer T, Gubbels H, Häcker C, Hasprich O, Scheibel C, Weicker K, Weicker N, Wenig M and Wolfangel C (2001). Automated solution of a highly constrained school timetabling problem-preliminary results. In: Boers EJW et al. (eds). Proceedings of EvoWorkshop 2001. Lecture Notes in Computer Science, Vol. 2037. Springer-Verlag: Berlin, pp. 431-440.
13. Burke EK and De Causmaecker P (eds) (2003). Practice and Theory of Automated Timetabling IV: Revised Selected Papers from the 4th International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 2740. Springer-Verlag: Berlin.
14. Burke EK and Erben W (eds) (2001). Practice and Theory of Automated Timetabling III: Selected Papers from the 3rd International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 2079, Springer-Verlag: Berlin.
15. Burke EK and Newall JP (2004). Solving examination timetabling problems through adaption of heuristic orderings. Ann Opns Res 129: 107-134.
16. Burke EK and Petrovic S (2002). Recent research directions in automated timetabling. Eur J Opl Res 140: 266-280.
17. Burke EK and Trick M (eds) (2005). The Practice and Theory of Automated Timetabling V: Revised Selected Papers from the 5th International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 3616, Springer-Verlag: Berlin.
18. Burke EK, Jackson KS, Kingston JH and Weare RF (1997). Automated timetabling: the state of the art. Comput J 40: 565-571.
19. Burke EK, Bykov Y and Petrovic S (2001a). A multicriteria approach to examination timetabling. In: Burke EK and Erben W (eds). Practice and Theory of Automated Timetabling III: Selected Papers from the 3rd International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 2079. Springer-Verlag: Berlin, pp. 118-131.
20. Burke EK, MacCarthy B, Petrovic S and Qu R (2001b). Case-based reasoning in course timetabling: an attribute graph approach. In: Aha DW and Watson I (eds). Case-Based Reasoning Research and Development: 4th International Conference on Case-Based Reasoning, ICCBR-01. Lecture Notes in Computer Science, Vol. 2080. Springer-Verlag: Berlin, pp. 90-104.
21. Burke EK, Kendall G and Soubeiga E (2003). A Tabu-search hyperheuristic for timetabling and rostering. J Heuristics 9: 451-470.
22. Burke EK, Bykov Y, Newall J and Petrovic S (2004). A time-predefined local search approach to exam timetabling problems. IIE Trans 36: 509-528.
23. Burke EK, McCollum B, Meisels A, Petrovic S and Qu R (2007). A graph-based hyper heuristic for educational timetabling problems. Eur J Opl Res 176: 177-192.
24. Caldeira JP and Rosa A (1997). School timetabling using genetic search. In: Burke EK and Carter M (eds). Practice and Theory of Automated Timetabling II: Second International Conference on the Practice and Theory of Automated Timetabling (PATAT'97). Lecture Notes in Computer Science, Vol. 1408. Springer-Verlag: Berlin, pp. 115-122.
25. Cambazard H, Demazeau F, Jussien N and David P (2005). Interactively solving school timetabling problems using extensions of constraint programming. In: Burke EK and Trick M (eds). Practice and Theory of Automated Timetabling III: Selected Papers from the 5th International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 3616. Springer-Verlag: Berlin, pp. 190-207.
26. Carrasco MP and Pato MV (2001). A multiobjective genetic algorithm for the class\teacher timetabling problem. In: Burke EK and Erben W(eds). Practice and Theory of Automated Timetabling III: Selected Papers from the 3rd International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 2079. Springer-Verlag: Berlin, pp. 3-17.
27. Carter MW and Laporte G (1998). Recent developments in practical course timetabling. In: Burke EK and Carter MW (eds). Practice and Theory of Automated Timetabling II: Second International Conference on the Practice and Theory of Automated Timetabling (PATAT'97). Lecture Notes in Computer Science, Vol. 1408. Springer-Verlag: Berlin, pp. 3-19.
28. Coello CA (1999). A comprehensive survey of evolutionary-based multi objective optimization techniques. Int J Knowledge Inform Systems 1: 269-308.
29. Coello CA, Van Veldhuizen DA and Lamont GB (eds) (2002) Evolutionary Algorithms for Solving Multi-objective Problems. Kluwer Academic Publishers: London.
30. Collette Y and Siarry P (2004). Multiobjective Optimization: Principles and Case Studies (Decision Engineering). Springer-Verlag: Berlin.
31. Deb K (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons: New York.
32. Di Gaspero L and Schaerf A (2001). Tabu search techniques for examination timetabling. In: Burke EK and Erben W (eds). Practice and Theory of Automated Timetabling III: Selected Papers from the 3rd International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 2079. Springer-Verlag: Berlin, p. 104.
33. Dimopoulou M and Miliotis P (2001). Implementation of a university course and examination timetabling system. Eur J Opl Res 130: 202-213.
34. Di Stefano C and Tettamanzi AGB (2001). An evolutionary algorithm for solving the school timetabling problem. In: Boers EJW (ed). Proceedings of EvoWorkshop 2001. Lecture Notes in Computer Science, Vol. 2037. Springer-Verlag: Berlin, pp. 452-462.
35. Elley M (2006). Ant algorithms for the exam timetabling problem. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 167-180.
36. Fernandes C, Caldeira JP, Melicio F and Rosa AC (1999). High school weekly timetabling by evolutionary algorithms. Proceedings of the 1999 ACM Symposium on Applied Computing. ACM Press: New York, pp. 344-350.
37. Fernandes C, Caldeira JP, Melicio F and Rosa AC (1999). Evolutionary algorithm for school timetabling. In: Banzhaf W, Daida J, Eiben A, Garzon M, Honavar V, Jakiela M and Smith R (eds). Proceedings of the Genetic and Evolutionary Computation Conference (GECCO' 99), Vol. 2. Morgan Kaufmann: San Francisco, CA, pp. 1777-1783.
38. Filho GR and Lorena LAN (2001). A constructive evolutionary approach to school timetabling. In: Boers EJW (ed). Proceedings of EvoWorkshop 2001. Lecture Notes in Computer Science, Vol. 2037. Springer-Verlag: Berlin, pp. 130-139.
39. Goldberg DE (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley: Reading.
40. De Haan P, Landman R, Post G and Ruizenaar H (2006). A four-phase approach to a timetabling problem in secondary schools. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 423-425.
41. Jacobsen F, Bortfeldt A and Gehring H (2006). Timetabling at German secondary schools: Tabu search versus constraint programming. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 439-442.
42. Kingston JH (2006). The KTS high school timetabling system. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 181-195.
43. Kochetov Y, Obuhovskaya P and Paschenko M (2006). Local search heuristics for the teacher\class timetabling problem. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 454-457.
44. Kwan M, Chung K, Yip K and Tam V (2003). An automated school timetabling system using hybrid intelligent techniques. In: Zhong N, Rás Z, Tsumoto S and Suzuki E (eds). Proceedings of the 14th International Symposium on Foundations of Intelligent Systems (ISMIS 2003). Lecture Notes in Computer Science, Vol. 2871. Springer-Verlag: Berlin, pp. 124-134.
45. Malim MR, Khader AT and Mustafa A (2006). Artificial immune algorithms for university timetabling. In: Burke EK and Rudová H(eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 234-245.
46. McCollum B (1998). The Implementation of a Centrally computerised timetabling system in a large British Civic University. In: Burke EK and Carter MW (eds). The Practice and Theory of Automated Timetabling II: Selected Papers from the 2nd International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 1408. Springer-Verlag: Berlin, pp. 237-254.
47. McCollum B (2006). University timetabling: bridging the gap between research and practice. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 15-35.
48. McCollum B and Newall J (2000). Introducing optime: examination timetabling software. In: Burke EK and Erben W (eds). Practice and Theory of Automated Timetabling III: Selected Papers from the 3rd International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 2079. Springer-Verlag: Berlin, pp. 485-490.
49. McCollum B, McMullan P, Newall J and Lane JP (2003). A workable scheduling algorithm. Proceedings of the 1st Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2003). Nottingham: Berlin, pp. 570-572.
50. Michalewicz Z (1999). Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag: Berlin.
51. Mitchell M (1995). An Introduction to Genetic Algorithms. A Bradford
52. Book: The MIT Press, London. Nedjah N and de Macedo Mourelle L (2004). Evolutionary time scheduling. Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04), Vol. 2. IEEE Computer Society: Las Vegas, Nevada, USA, pp. 357-361.
53. Papoutsis K, Valouxis C and Housos E (2003). A column generation approach for the timetabling problem of greek high schools. JOpl Res Soc 54(3): 230-238.
54. Perzina R (2006). Solving the university timetabling problem with optimized enrolment of students by a parallel self-adaptive genetic algorithm. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 264-280.
55. Petrovic S and Burke EK (2004). Educational timetabling. In: Leung JY-T and Anderson JH (eds). Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman & Hall\CRC Press: Danvers, MA, USA, pp. 45-1-45-23.
56. Petrovic S, Patel V and Yang Y (2005). Examination timetabling with fuzzy constraints. In: Burke EK and Trick M (eds). The Practice and Theory of Automated Timetabling V: Revised Selected Papers from the 5th International conference, Pittsburgh. Lecture Notes in Computer Science, Vol. 3616. Springer-Verlag: Berlin, pp. 313-333.
57. Qu R, Burke E, McCollum B, Merlot L and Lee S (2006). A survey of search methodologies and automated approaches for examination timetabling, Technical Report No. NOTTCS-TR-2006-4, School of Computer Science & IT, University of Nottigham.
58. Ranson D and Ahmadi S (2006). An extensible modelling framework for the examination timetabling problem. In: Burke EK and Rudová H (eds). PATAT 2006-Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling. Masaryk University: Brno, Czech Republic, pp. 281-292.
59. Ross P, Corne D and Fang H (1994). Successful lecture timetabling with evolutionary algorithms. In: Eiben AE et al., Manderick B and Ruttkay Z (eds). Applied Genetic and other Evolutionary Algorithms: Proceedings of the ECAI'94 Workshop. Springer: Berlin.
60. Ross P, Hart E and Corne D (2003). Genetic algorithms and timetabling. In: Ashish G and Tsutsui S (eds). Natural Computing Series, Advances in Evolutionary Computing. Springer-Verlag: New York, pp. 755-777.
61. Rossi-Doria O, Sampels M, Biratarri M, Chiarandini M, Dorigo M, Gambardella L, Knowles J, Manfrin M, Mastrolilli M, Paechter B, Paquete L and Schützle T (2003). A comparison of the performance of different metaheuristics on the timetabling problem. In: Burke EK, De Causmaecker P et al. (eds). The Practice and Theory of Automated Timetabling IV: Revised Selected Papers from the 4th International Conference, Gent. Lecture Notes in Computer Science, Vol. 2740. Springer-Verlag: Berlin, pp. 329-351.
62. Rudová H and Murray K (2003). University course timetabling with soft constraints. In: Burke EK and De Causmaecker P (eds). Practice and Theory of Automated Timetabling IV: 4th International Conference, PATAT 2002. Lecture Notes in Computer Science, Vol. 2740. Springer-Verlag: Berlin, pp. 310-328.
63. Santiago-Mozos R, Salcedo-Sanz S, De Prado-Cumplido M and Bousoño-Calzón C (2005). A two-phase heuristic evolutionary algorithm for personalizing course timetables: a case study in a Spanish university. Comput Opns Res 32: 1761-1776.
64. Schaerf A (1999). Local search techniques for large high school timetabling problems. IEEE Trans Systems Man and Cybernet-Part A: Systems Hum 29: 368-377.
65. Schaerf A (1999). A survey of automated timetabling. Artif Intel Rev 13: 87-127.
66. Schaerf A and Meisels A (2000). Solving employee timetabling problems by generalized local search. Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence. Lecture Notes in Computer Science, Vol. 1792. Springer-Verlag: Berlin, pp. 380-389.
67. Smith KA, Abramson D and Duke D (2003). Hopfield neural networks for timetabling: formulations, methods, and comparative results. Comput Indus Eng 44: 283-305.
68. Socha K, Knowles J and Sampels M (2002). A MAX-MIN ant system for the university course timetabling problem. In: Goos G, Hartmanis J and van Leeuwen J (eds). Ant Algorithms: Third International Workshop. Lecture Notes in Computer Science, Vol. 2463. Springer-Verlag: Berlin, pp. 1-13.
69. Syswerda G (1989). Uniform crossover in genetic algorithms. In: Shaeffer JD (ed). Proceedings of Third International Conference on Genetic Algorithms and Their Applications. Morgan Kaufmann: San Mateo, CA, USA, pp. 2-9.
70. Tavares R, Teofilo A, Silva P and Rosa A (1999). Infected genes evolutionary algorithm. In: Bryant B, Lamont G, Haddad H and Caroll J (eds). Proceedings of the 1999 ACM Symposium on Applied Computing (SAC'99). ACM Press: New York, pp. 333-338.
71. Ten Eikelder HMM and Willemen RJ (2001). Some complexity aspects of secondary school timetabling problems. In: Burke EK and Erben W (eds). Practice and Theory of Automated Timetabling III: Selected Papers from the 3rd International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 2079. Springer-Verlag: Berlin, pp. 18-27.
72. Trick MA (2001). A Schedule-then-Break Approach to Sports Timetabling. Lecture Notes in Computer Science, Vol. 2079. Springer-Verlag: Berlin, pp. 242-253.
73. Wilke P, Gröbner M, Oster N (2002). A Hybrid Genetic Algorithm for School Timetabling. Lecture Notes in Computer Science, Vol. 2557. Springer-Verlag: Berlin, pp. 455-464.
74. Wren A (1996). Scheduling, Timetabling and Rostering-A Special Relationship? In: Burke EK and Ross P (eds). The Practice and Theory of Automated Timetabling: Selected Papers from the 1st International Conference on the Practice and Theory of Automated Timetabling. Lecture Notes in Computer Science, Vol. 1153. Springer-Verlag: Berlin, pp. 46-75.
75. De Werra D (1985). An introduction to timetabling. Eur J Opl Res 19: 151-162.