Advertisement

Automated generation of constructive ordering heuristics for educational timetabling

  • Nelishia Pillay
  • Ender Özcan
PATAT 2016
  • 118 Downloads

Abstract

Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For examination timetabling and university course timetabling problems essentially graph colouring heuristics have been used for this purpose. The process of deriving heuristics manually for educational timetabling is a time consuming task. Furthermore, according to the no free lunch theorem different heuristics will perform well for different problems and problem instances. Hence, automating the induction of construction heuristics will reduce the man hours involved in creating such heuristics, allow for the derivation of problem specific heuristics and possibly result in the derivation of heuristics that humans have not thought of. This paper presents generation construction hyper-heuristics for educational timetabling. The study investigates the automatic induction of two types of construction heuristics, namely, arithmetic heuristics and hierarchical heuristics. Genetic programming is used to evolve arithmetic heuristics. Genetic programming, genetic algorithms and the generation of random heuristic combinations is examined for the generation of hierarchical heuristics. The hyper-heuristics generating both types of heuristics are applied to the examination timetabling and the curriculum based university course timetabling problems. The evolved heuristics were found to perform much better than the existing graph colouring heuristics used for this domain. Furthermore, it was found that the while the arithmetic heuristics were more effective for the examination timetabling problem, the hierarchical heuristics produced better results than the arithmetic heuristics for the curriculum based course timetabling problem. Genetic algorithms proved to be the most effective at inducing hierarchical heuristics.

Keywords

Educational timetabling Construction heuristics Hyper-heuristics Genetic programming Genetic algorithms 

Notes

Acknowledgements

The authors would like to thank the reviewers for their helpful comments to improve the quality of the paper. The facilities made available by the Centre for High Performance Computing (CHPC) in South Africa and University of Nottingham High Performance Computing facility to run simulations for the experiments is acknowledged.

References

  1. Bader-El-Den, M., Poli, R., & Fatima, S. (2009). Evolving timetabling heuristics using grammar-based genetic programming hyper-heuristic framework. Memetic Computing, 1, 205–219.CrossRefGoogle Scholar
  2. Branke, J., Nguyean, S., Pickardt, C. W., & Zhang, M. (2015). Automated design of production scheduling heuristics: A review. IEEE Transactions on Evolutionary Computation, 20(1), 110–124.CrossRefGoogle Scholar
  3. Burke, E. K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., & Ozcan, E. (2013). Hyper-heuristics: A survey of the state of the art. Journal of Operational Research Society, 64, 1695–1724.CrossRefGoogle Scholar
  4. Burke, E. K., Hyde, M., Kendall, G., & Woodward, J. (2010). A genetic programming hyper-heuristic approach for evolving two dimensional strip packing heuristics. IEEE Transactions on Evolutionary Computation, 14, 942–958.CrossRefGoogle Scholar
  5. Burke, E. K., McCollum, B., Meisels, A., Petrovic, S., & Qu, R. (2007). A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research, 176, 177–192.CrossRefGoogle Scholar
  6. Drake, J. H., Hyde, M., Ibrahim, K., & Özcan, E. (2014). A genetic programming hyper-heuristic for the multidimensional knapsack problem. Kybernetes, 43(9/10), 1500–1511.CrossRefGoogle Scholar
  7. Hyde, M. A. (2010). Genetic programming hyper-heuristic approach to automated packing. Ph.D. thesis, School of Computer Science.Google Scholar
  8. Koza, J. (1992). Genetic programming: On the programming of computers by means of natural selection (1st ed.). Cambridge: MIT.Google Scholar
  9. McCollum, B., McMullan, P., Paechter, B., Lewis, R., Schaerf, A., DiGaspero, L., et al. (2008). Setting the research agenda in automated timetabling: The second international timetabling competition. INFORMS Journal of Computing, 22(1), 120–130.CrossRefGoogle Scholar
  10. McKay, R. I., Hoai, N. X., Whigham, P., & O’Neill, M. (2010). Grammar-based genetic programming: A survey. Genetic Programming and Evolvable Machines, 11(3), 365–396.CrossRefGoogle Scholar
  11. O’Neill, M., & Ryan, C. (2003). Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Berlin: Springer.CrossRefGoogle Scholar
  12. Özcan, E., & Parkes, A. (2011). Policy matrix evolution for generation of heuristics. In Proceedings of the 13th annual conference on genetic and evolutionary computation (pp. 2011–2018).Google Scholar
  13. Pillay, N. (2009). Evolving hyper-heuristics for the uncapacitated examination timetabling problem. In Proceedings of the multidisciplinary international conference on scheduling (pp. 409–422).Google Scholar
  14. Pillay, N. (2011). Evolving heuristics for the school timetabling problem. In Proceedings of the 2011 IEEE conference on intelligent computing and intelligent systems (ICIS 2011), vol. 3, (pp. 281–286). IEEE.Google Scholar
  15. Qu, R., Burke, E., 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.CrossRefGoogle Scholar
  16. Sim, K., & Hart, E. A. (2016). combined generative and selective hyper-heuristic for the vehicle routing problem. In Proceedings of the genetic and evolutionary computation conference (GECCO ’16) (pp. 1093–1100). ACM.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PretoriaPretoriaSouth Africa
  2. 2.ASAP, School of Computer ScienceUniversity of NottinghamNottinghamUK

Personalised recommendations