Monte Carlo hyper-heuristics for examination timetabling

Annals of Operations Research

Volumn 196, Issue 1, 2012, Pages 73-90

  Burke, Edmund K ^a   Kendall, Graham ^a   Mısır, Mustafa ^b   Özcan, Ender ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b YEDITEPE UNIVERSITY   (Turkey)

Author keywords

Examination timetabling; Hyper heuristics; Meta heuristics; Reinforcement learning; Simulated annealing

Indexed keywords

EID: 84863196106     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-010-0782-2     Document Type: Article

References (64)

1. Abdullah, S., Ahmadi, S., Burke, E. K., Dror, M., & McCollum, B. (2007). A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem. Journal of the Operational Research Society, 58, 1494-1502.
2. Alkan, A., & Özcan, E. (2003). Memetic algorithms for timetabling. In Proc. of the congress on evolutionary computation (Vol. 3, pp. 1796-1802).
3. Asmuni, H., Burke, E. K., & Garibaldi, J. M. (2005). Fuzzy multiple ordering criteria for examination timetabling. In Lecture notes in computer science: Vol. 3616. Selected papers from the 5th international conference on the practice and theory of automated timetabling (pp. 334-353). Berlin: Springer.
4. Ayob, M., & Kendall, G. (2003). A Monte Carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In Proceedings of the international conference on intelligent technologies (InTech'03), Chiang Mai, Thailand (pp. 132-141).
5. Azimi, Z. N. (2005). Hybrid heuristics for examination timetabling problem. Applied Mathematics and Computation, 163(2), 705-733.
6. Bai, R., & Kendall, G. (2005). An investigation of automated planograms using a simulated annealing based hyper-heuristics. In T. Ibaraki, K. Nonobe, & M. Yagiura (Eds.), Operations research/computer science interface series: Vol. 32. Metaheuristics: progress as real problem solver (pp. 87-108). Berlin: Springer.
7. Bai, R., Blazewicz, J., Burke, E. K., Kendall, G., & McCollum, B. (2007). A simulated annealing hyper-heuristic methodology for flexible decision support (Tech. Rep. NOTTCS-TR-2007-8). School of CSiT, University of Nottingham.
8. Bilgin, B., Özcan, E., & Korkmaz, E. E. (2007). An experimental study on hyper-heuristics and exam timetabling. In Lecture notes in computer science: Vol. 3867. Practice and theory of automated timetabling VI (PATAT 2006) (pp. 394-412). Berlin: Springer.
9. Broder, S. (1964). Final examination scheduling. Communications of the ACM, 7, 494-498.
10. Burke, E. K., & Newall, J. P. (1999). A multistage evolutionary algorithm for the timetable problem. IEEE Trans Evolutionary Computation, 3(1), 63-74.
11. Burke, E. K., & Newall, J. P. (2004). Solving examination timetabling problems through adaption of heuristic orderings. Annals of Operations Research, 129, 107-134.
12. Burke, E. K., & Petrovic, S. (2002). Recent research directions in automated timetabling. European Journal of Operational Research, 140(2), 266-280.
13. Burke, E. K., Newall, J. P., & Weare, R. F. (1996a). A memetic algorithm for university exam timetabling. In Lecture notes in computer science: Vol. 1153. Selected papers from the first international conference on practice and theory of automated timetabling (pp. 241-250). Berlin: Springer.
14. Burke, E. K., Elliman, D. G., Ford, P. H., & Weare, R. F. (1996b). Examination timetabling in British universities: a survey. In Lecture notes in computer science: Vol. 1153. Selected papers from the first international conference on practice and theory of automated timetabling (pp. 76-90). Berlin: Springer.
15. Burke, E. K., Hart, E., Kendall, G., Newall, J., Ross, P., & Schulenburg, S. (2003). Hyper-heuristics: An emerging direction in modern search technology. In F. Glover & G. Kochenberger (Eds.), Handbook of metaheuristics (pp. 457-474). Norwell: Kluwer Academic.
16. Burke, E. K., Hyde, M. R., & Kendall, G. (2006). Evolving bin packing heuristics with genetic programming. In Lecture notes in computer science: Vol. 4193. Proceedings of the 9th international conference on parallel problem solving from nature (PPSN 2006), Reykjavik, Iceland (pp. 860-869). Berlin: Springer.
17. Burke, E. K., Hyde, M. R., Kendall, G., & Woodward, J. (2007a). Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In GECCO '07: proceedings of the 9th annual conference on genetic and evolutionary computation (pp. 1559-1565). New York: ACM. doi: 10. 1145/1276958. 1277273.
18. Burke, E. K., McCollum, B., Meisels, A., Petrovic, S., & Qu, R. (2007b). A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research, 176(1), 177-192.
19. Caramia, M., Dell'Olmo, P., & Italiano, G. F. (2001). New algorithms for examination timetabling. In Lecture notes in computer science: Vol. 1982. WAE '00: the 4th international workshop on algorithm engineering (pp. 230-242). London: Springer.
20. Caramia, M., Dellolmo, P., & Italiano, G. F. (2008). Novel local search-based approaches to university examination timetabling. INFORMS Journal on Computing, 20(1), 86-99.
21. Carter, M. W. (1986). A survey of practical applications of examination timetabling algorithms. Operations Research Society of America, 34(2), 193-202.
22. Carter, M. W., & Laporte, G. (1996). Recent developments in practical examination timetabling. In Lecture notes in computer science: Vol. 1153. Selected papers from the first international conference on practice and theory of automated timetabling (pp. 373-383). Berlin: Springer.
23. Carter, M. W., Laporte, G., & Lee, S. (1996). Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society, 47(3), 373-383.
24. Cerny, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51.
25. Cole, A. J. (1964). The preparation of examination timetables using a small-store computer. The Computer Journal, 7, 117-121.
26. Corne, D., Ross, P., & Fang, H. L. (1994). Fast practical evolutionary timetabling. In Selected papers from AISB workshop on evolutionary computing (pp. 250-263).
27. Corr, P. H., McCollum, B., McGreevy, M. A. J., & McMullan, P. (2006). A new neural network based construction heuristic for the examination timetabling problem. In Parallel problem solving from nature-PPSN IX (pp. 392-401).
28. Cowling, P., Kendall, G., & Soubeiga, E. (2001a). A hyperheuristic approach to scheduling a sales summit. In PATAT '00: selected papers from the third international conference on practice and theory of automated timetabling III (pp. 176-190). London: Springer.
29. Cowling, P., Kendall, G., & Soubeiga, E. (2001b). A parameter-free hyperheuristic for scheduling a sales summit. In Proceedings of the 4th metaheuristic international conference (pp. 127-131).
30. Cowling, P., Kendall, G., & Soubeiga, E. (2002). Hyperheuristics: A tool for rapid prototyping in scheduling and optimisation. In Lecture notes in computer science: Vol. 4193. EvoWorkShops (pp. 1-10). Berlin: Springer.
31. Dowsland, K., & Thompson, J. (2005). Ant colony optimization for the examination scheduling problem. Journal of the Operational Research Society, 56(4), 426-438.
32. Dowsland, K. A., Soubeiga, E., & Burke, E. (2007). A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation. European Journal of Operational Research, 179(3), 759-774.
33. Eley, M. (2006). Ant algorithms for the exam timetabling problem. In Proc. of the 5th international conference on the practice and theory of automated timetabling (pp. 364-382).
34. Ergul, A. (1996). Ga-based examination scheduling experience at middle east technical university. In Lecture notes in computer science: Vol. 1153. Practice and theory of automated timetabling (pp. 212-226). Berlin: Springer.
35. Ersoy, E., Özcan, E., & Uyar, S. (2007). Memetic algorithms and hyperhill-climbers. In Proc. of the 3rd multidisciplinary int. conf. on scheduling: theory and applications (MISTA'07) (pp. 159-166).
36. Even, S., Itai, A., & Shamir, A. (1976). On the complexity of timetable and multicommodity flow problems. SIAM Journal on Computing, 5(4), 691-703.
37. Gaspero, L. D., & Schaerf, A. (2001). Tabu search techniques for examination timetabling. In E. K. Burke & W. Erben (Eds.), Lecture notes in computer science: Vol. 2079. Third international conference on practice and theory of automated timetabling, PATAT2000 (pp. 104-117). Berlin: Springer.
38. Gogos, C., Alefragis, P., & Housos, E. (2010). An improved multi-staged algorithmic process for the solution of the examination timetabling problem. Annals of Operations Research. doi: 10. 1007/s10479-010-0712-3.
39. Goldberg, DE (1989). Genetic algorithms in search, optimization and machine learning. Boston: Addison-Wesley.
40. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4, 237-285.
41. Kendall, G., & Hussin, N. M. (2005). A tabu search hyper-heuristic approach to the examination timetabling problem at the Mara University of Technology. In Lecture notes in computer science: Vol. 3616. Practice and theory of automated timetabling V (pp. 270-293). Berlin: Springer.
42. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671-680.
43. Krasnogor, N., & Gustafson, S. (2004). A study on the use of 'self-generation' in memetic algorithms. Natural Computing, 3(1), 53-76.
44. Leighton, F. T. (1979). A graph coloring algorithm for large scheduling problems. Journal of Research of the National Bureau of Standards, 84, 489-506.
45. Lundy, M., & Mees, A. (1986). Convergence of an annealing algorithm. Mathematical Programming, 34, 111-124.
46. Marin, H. T. (1998). Combinations of ga and csp strategies for solving the examination timetabling problem (PhD thesis). Instituto Technologico y de Estudios Superiores de Monterrey.
47. McCollum, B. (2006). University timetabling: Bridging the gap between research and practice. In Proc. of the 5th international conference on the practice and theory of automated timetabling (pp. 15-35). Berlin: Springer.
48. McCollum, B., Schaerf, A., Paechter, B., McMullan, P., Lewis, R., Parkes, A. J., Gaspero, L., Qu, R., & Burke, E. K. (2010). Setting the research agenda in automated timetabling: the second international timetabling competition. INFORMS Journal on Computing, 22, 120-130.
49. Merlot, L. T., Boland, N., Hughes, B. D., & Stuckey, P. J. (2003). A hybrid algorithm for the examination timetabling problem. In Lecture notes in computer science: Vol. 1153. Practice and theory of automated timetabling IV, PATAT 2002 (pp. 207-231). Berlin: Springer.
50. Müller, T. (2009). Itc2007 solver description: A hybrid approach. Annals of Operations Research, 172(1), 429-446.
51. Nareyek, A. (2003). Choosing search heuristics by non-stationary reinforcement learning. In M. G. C. Resende & J. P. de Sousa (Eds.), Metaheuristics: computer decision-making (pp. 523-544). Norwell: Kluwer Academic, Chap. 9.
52. Ong, Y. S., Lim, M. H., Zhu, N., & Wong, K. W. (2006). Classification of adaptive memetic algorithms: a comparative study. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 36(1), 141-152.
53. Özcan, E., & Alkan, A. (2007). A memetic algorithm for solving a timetabling problem: An incremental strategy. In: P. Baptiste, G. Kendall, A. M. Kordon & F. Sourd (Eds.), Proc. of the 3rd multidisciplinary int. conf. on scheduling: theory and applications (pp. 394-401).
54. Özcan, E., & Ersoy, E. (2005). Final exam scheduler-fes. In Proc. of the congress on evolutionary computation (pp. 1356-1363). New York: IEEE Press.
55. Özcan, E., Bilgin, B., & Korkmaz, E. E. (2006). Hill climbers and mutational heuristics in hyperheuristics. In Lecture notes in computer science: Vol. 4193. Proceedings of the 9th international conference on parallel problem solving from nature (PPSN 2006), Reykjavik, Iceland (pp. 202-211). Berlin: Springer.
56. Özcan, E., Bilgin, B., & Korkmaz, E. E. (2008). A comprehensive survey of hyperheuristics. Intelligent Data Analysis, 12(1), 3-23.
57. Paquete, L. F., & Fonseca, C. M. (2001). A study of examination timetabling with multiobjective evolutionary algorithms. In Proc. of the 4th metaheuristics international conference (MIC 2001) (pp. 149-154).
58. Petrovic, S., Patel, V., & Yang, Y. (2005). Examination timetabling with fuzzy constraints. In Lecture notes in computer science: Vol. 3616. The 5th int. conf. on the practice and theory of automated timetabling (pp. 313-333). Berlin: Springer.
59. Petrovic, S., Yang, Y., & Dror, M. (2007). Case-based selection of initialisation heuristics for metaheuristic examination timetabling. Expert Systems with Applications, 33(3), 772-785.
60. Qu, R., Burke, E. K., McCollum, B., Merlot, L., & Lee, S. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12(1), 55-89.
61. Ross, P., Corne, D., & Fang, H. L. (1994). Improving evolutionary timetabling with delta evaluation and directed mutation. In PPSN III: proceedings of the international conference on evolutionary computation. The third conference on parallel problem solving from nature (pp. 556-565). London: Springer.
62. Schaerf, A. (1999). A survey of automated timetabling. Artificial Intelligence Review, 13(2), 87-127.
63. Schaerf, A. & Gaspero, L. D. (2006). Measurability and reproducibility in timetabling research: State-of the-art and discussion(invited paper). In Proc. of the 6th int. conf. on the practice and theory of automated timetabling (pp. 53-62).
64. Wong, T., Cote, P., & Gely, P. (2002). Final exam timetabling: a practical approach. In Proc. of the IEEE canadian conference on electrical and computer engineering (Vol. 2, pp. 726-731).