A Shuffled Complex Evolution Based Algorithm for Examination Timetabling - Benchmarks and a New Problem Focusing Two Epochs

Nuno Leite, Fernando Melício, Agostinho C. Rosa

2014

Abstract

In this work we present a memetic algorithm for solving examination timetabling problems. Two problems are analysed and solved. The first one is the well-studied single-epoch problem. The second problem studied is an extension of the standard problem where two examination epochs are considered, with different durations. The proposed memetic algorithm inherits the population structure of the Shuffled Complex Evolution algorithm, where the population is organized into sets called complexes. These complexes are evolved independently and then shuffled in order to generate the next generation complexes. In order to explore new solutions, a crossover between two complex’s solutions is done. Then, a random solution selected from the top best solutions is improved, by applying a local search step where the Great Deluge algorithm is employed. Experimental evaluation was carried out on the public uncapacitated Toronto benchmarks (single epoch) and on the ISEL-DEETC department examination benchmark (two epochs). Experimental results show that the proposed algorithm is efficient and competitive on the Toronto benchmarks with other algorithms from the literature. Relating the ISEL-DEETC benchmark, the algorithm attains a lower cost when compared with the manual solution.

References

  1. Abdullah, S. and Alzaqebah, M. (2013). A hybrid selfadaptive bees algorithm for examination timetabling problems. Appl. Soft Comput., 13(8):3608-3620.
  2. Abdullah, S., Turabieh, H., and McCollum, B. (2009). A hybridization of electromagnetic-like mechanism and great deluge for examination timetabling problems. In Blesa, M. J., Blum, C., Gaspero, L. D., Roli, A., Sampels, M., and Schaerf, A., editors, Hybrid Metaheuristics, volume 5818 of Lecture Notes in Computer Science, pages 60-72. Springer.
  3. Abdullah, S., Turabieh, H., McCollum, B., and McMullan, P. (2010). A tabu-based memetic approach for examination timetabling problems. In Yu, J., Greco, S., Lingras, P., Wang, G., and Skowron, A., editors, RSKT, volume 6401 of Lecture Notes in Computer Science, pages 574-581. Springer.
  4. Alba, E. and Dorronsoro, B. (2008). Cellular Genetic Algorithms. Springer Publishing Company, Incorporated, 1st edition.
  5. Alzaqebah, M. and Abdullah, S. (2014). An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling. J. Scheduling, 17(3):249-262.
  6. Burke, E., Bykov, Y., Newall, J., and Petrovic, S. (2004). A time-predefined local search approach to exam timetabling problems. IIE Transactions, 36(6):509- 528.
  7. Burke, E., Eckersley, A., McCollum, B., Petrovic, S., and Qu, R. (2010). Hybrid variable neighbourhood approaches to university exam timetabling. European Journal of Operational Research, 206(1):46 - 53.
  8. Burke, E. and Newall, J. (2004). Solving examination timetabling problems through adaption of heuristic orderings. Annals of Operations Research, 129(1- 4):107-134.
  9. Burke, E. K. and Bykov, Y. (2006). Solving Exam Timetabling Problems with the Flex-Deluge Algorithm. In Proceedings of the Sixth International Conference on the Practice and Theory of Automated Timetabling, pages 370-372. ISBN: 80-210-3726-1.
  10. Burke, E. K. and Bykov, Y. (2008). A late acceptance strategy in Hill-Climbing for exam timetabling problems. In PATAT 7808 Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling.
  11. Burke, E. K., McCollum, B., McMullan, P., and Parkes, A. J. (2008). Multi-objective aspects of the examination timetabling competition track. Proceedings of PATAT 2008.
  12. Burke, E. K. and Newall, J. P. (2002). Enhancing timetable solutions with local search methods. In Burke, E. K. and Causmaecker, P. D., editors, PATAT, volume 2740 of Lecture Notes in Computer Science, pages 195- 206. Springer.
  13. Carter, M., Laporte, G., and Lee, S. Y. (1996). Examination Timetabling: Algorithmic Strategies and Applications. Journal of the Operational Research Society, 47(3):373-383.
  14. Chu, S.-C., Chen, Y.-T., and Ho, J.-H. (2006). Timetable scheduling using particle swarm optimization. In Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3, ICICIC 7806, pages 324-327, Washington, DC, USA. IEEE Computer Society.
  15. Demeester, P., Bilgin, B., Causmaecker, P. D., and Berghe, G. V. (2012). A hyperheuristic approach to examination timetabling problems: benchmarks and a new problem from practice. J. Scheduling, 15(1):83-103.
  16. Dowsland, A. and Thompson, M. (2005). Ant colony optimization for the examination scheduling problem. Journal of the Operational Research Society, 56(4):426-438.
  17. Duan, Q., Gupta, V., and Sorooshian, S. (1993). Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76(3):501-521.
  18. Dueck, G. (1993). New optimization heuristics: The great deluge algorithm and the record-to-record travel. Journal of Computational Physics, 104(1):86 - 92.
  19. Eley, M. (2006). Ant algorithms for the exam timetabling problem. In PATAT VI, volume 3867 of Lecture Notes in Computer Science, pages 364-382. Springer.
  20. Eusuff, M., Lansey, K., and Pasha, F. (2006). Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering Optimization, 38(2):129-154.
  21. Kamil, A., Krebs, J., and Pulliam, H. (1987). Foraging behavior. Plenum Press.
  22. Kendall, G. and Hussin, N. (2005). An investigation of a tabu-search-based hyper-heuristic for examination timetabling. In Multidisciplinary Scheduling: Theory and Applications, pages 309-328. Springer US.
  23. Leite, N., Melício, F., and Rosa, A. (2013). Solving the examination timetabling problem with the shuffled frogleaping algorithm. In Proceedings of the 5th International Joint Conference on Computational Intelligence, pages 175-180.
  24. Leite, N., Neves, R. F., Horta, N., Melicio, F., and Rosa, A. C. (2012). Solving an Uncapacitated Exam Timetabling Problem Instance using a Hybrid NSGAII. In Proceedings of the 4th International Joint Conference on Computational Intelligence, pages 106- 115.
  25. McCollum, B., McMullan, P., Parkes, A. J., Burke, E. K., and Qu, R. (2012). A New Model for Automated Examination Timetabling. Annals of Operations Research, 194:291-315.
  26. Merlot, L., Boland, N., Hughes, B., and Stuckey, P. (2003). A hybrid algorithm for the examination timetabling problem. In PATAT IV, volume 2740 of Lecture Notes in Computer Science, pages 207-231. Springer.
  27. Neri, F. (2012). Diversity management in memetic algorithms. In (Neri et al., 2012), pages 153-165.
  28. Neri, F., Cotta, C., and Moscato, P., editors (2012). Handbook of Memetic Algorithms, volume 379 of Studies in Computational Intelligence. Springer.
  29. Qu, R., Burke, E., McCollum, B., Merlot, L. T. G., and Lee, S. Y. (2009). A Survey of Search Methodologies and Automated System Development for Examination Timetabling. Journal of Scheduling, 12:55-89.
  30. Sabar, N. R., Ayob, M., and Kendall, G. (2009). Solving examination timetabling problems using honey-bee mating optimization (etp-hbmo). In Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 10- 12 Aug 2009, Dublin, Ireland, pages 399-408.
  31. Talbi, E.-G. (2009). Metaheuristics - From Design to Implementation. Wiley.
  32. Turabieh, H. and Abdullah, S. (2011a). A hybrid fish swarm optimisation algorithm for solving examination timetabling problems. In LION, number 6683 in Lecture Notes in Computer Science, pages 539-551. Springer.
  33. Turabieh, H. and Abdullah, S. (2011b). An integrated hybrid approach to the examination timetabling problem. Omega, 39(6):598-607.
  34. Yang, Y. and Petrovic, S. (2005). A novel similarity measure for heuristic selection in examination timetabling.
  35. In PATAT V, volume 3616 of Lecture Notes in Computer Science, pages 247-269. Springer.
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Paper Citation


in Harvard Style

Leite N., Melício F. and Rosa A. (2014). A Shuffled Complex Evolution Based Algorithm for Examination Timetabling - Benchmarks and a New Problem Focusing Two Epochs . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 112-124. DOI: 10.5220/0005164801120124


in Bibtex Style

@conference{ecta14,
author={Nuno Leite and Fernando Melício and Agostinho C. Rosa},
title={A Shuffled Complex Evolution Based Algorithm for Examination Timetabling - Benchmarks and a New Problem Focusing Two Epochs},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={112-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005164801120124},
isbn={978-989-758-052-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - A Shuffled Complex Evolution Based Algorithm for Examination Timetabling - Benchmarks and a New Problem Focusing Two Epochs
SN - 978-989-758-052-9
AU - Leite N.
AU - Melício F.
AU - Rosa A.
PY - 2014
SP - 112
EP - 124
DO - 10.5220/0005164801120124