Abstract
One of the annual issues that has to be addressed in English football is producing a fixture schedule for the holiday periods that reduces the travel distance for the fans and players. This problem can be seen as a minimisation problem which must abide to the constraints set by the Football Association. In this study, the performance of selection hyper-heuristics is investigated as a solution methodology. Hyper-heuristics aim to automate the process of selecting and combining simpler heuristics to solve computational search problems. A selection hyper-heuristic stores a single candidate solution in memory and iteratively applies selected low level heuristics to improve it. The results show that the learning hyper-heuristics outperform some previously proposed approaches and solutions published by the Football Association.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Denzinger, J., Fuchs, M., Fuchs, M.: High performance atp systems by combining several ai methods. In: Proceedings of the 4th Asia-Pacific Conference on SEAL, IJCAI, pp. 102–107 (1997)
Özcan, E., Bykov, Y., Birben, M., Burke, E.K.: Timetabling using late acceptance hyper-heuristics. In: Proc. of the IEEE Congress on Evolutionary Computation, pp. 997–1004. IEEE Press, Los Alamitos (2009)
Özcan, E., Bilgin, B., Korkmaz, E.: A comprehensive analysis of hyper-heuristics. In: Intelligent Data Analysis, pp. 3–23 (2008)
Özcan, E., Mısır, M., Ochoa, G., Burke, E.K.: A reinforcement learning - great-deluge hyper-heuristic for examination timetabling. International Journal of Applied Metaheuristic Computing 1(1), 39–59 (2010)
Bilgin, B., Özcan, E., Korkmaz, E.E.: An experimental study on hyper-heuristics and exam timetabling. In: Proceedings of the 6th Practice and Theory of Automated Timetabling (PATAT 2006). LNCS, vol. 3867, pp. 394–412. Springer, Heidelberg (2006)
Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)
Burke, E.K., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer, Dordrecht (2003)
Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 529–556. Springer, Heidelberg (2005)
Kendall, G., Knust, S., Ribeiro, C., Urrutia, S.: Scheduling in sports: An annotated bibliography. Computers & Operations Research 37, 1–19 (2010)
Applegate, D.L., Bixby, R.E., Chvatal, V., Cook, W.J.: The Traveling Salesman Problem: A Computational Study. Princeton Series in Applied Mathematics. Princeton University Press, Princeton (2007)
Kendall, G.: Scheduling english football fixtures over holiday periods. Journal of the Operational Research Society 59(6), 743–755 (2008)
Kendall, G.: Hybridising cplex with simulated annealing to minimise travel distances for english football fixtures (2009) (in review)
Özcan, E., Bilgin, B., Korkmaz, E.E.: Hill climbers and mutational heuristics in hyperheuristics. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 202–211. Springer, Heidelberg (2006)
Kendall, G., Cowling, P., Soubeiga, E.: Choice function and random hyper-heuristics. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning, SEAL, pp. 667–671 (2002)
Nareyek, A.: Choosing search heuristics by non-stationary reinforcement learning. In: Resende, M.G.C., de Sousa, J.P. (eds.) Metaheuristics: Computer Decision-Making, pp. 523–544. Kluwer, Dordrecht (2003)
Burke, E., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.: A classification of hyper-heuristic approaches. In: Handbook of Metaheuristics. Springer, Heidelberg (to appear, 2010)
Wiering, M.: Qv(lambda)-learning: A new on-policy reinforcement learning algorithm. In: Proceedings of the 7th European Workshop on Reinforcement Learning (2005)
Aydin, M., Öztemel, E.: Dynamic job-shop scheduling using reinforcement learning agents. In: Robotics and Autonomous Systems, vol. 33, pp. 39–59. Elsevier, Amsterdam (2000)
Luiz, A., Ribeiro, C., Costa, A., Bianchi, R.: Heuristic reinforcement learning applied to robocup simulation agents. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds.) RoboCup 2007: Robot Soccer World Cup XI. LNCS (LNAI), vol. 5001, pp. 220–227. Springer, Heidelberg (2008)
Wang, Y., Usher, J.: Application of reinforcement learning for agent-based production scheduling. In: Engineering Applications of Artificial Intelligence, vol. 18, pp. 73–82 (2005)
Zhang, W., Dietterich, T.: A reinforcement learning approach to job-shop scheduling. In: Proceedings of the 14th international joint conference on Artificial intelligence, vol. 1, pp. 1114–1120 (1995)
Bai, R., Kendall, G.: An investigation of automated planograms using a simulated annealing based hyper-heuristics. In: Ibaraki, T., Nonobe, K., Yagiura, M. (eds.) Metaheuristics: Progress as Real Problem Solver. Operations Research/Computer Science Interface Serices, vol. 32, pp. 87–108. Springer, Heidelberg (2005)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Dueck, G.: New optimization heuristics: The great deluge algorithm and the record-to record travel. Journal of Computational Physics 104, 86–92 (1993)
Kendall, G., Mohamad, M.: Channel assignment optimisation using a hyper-heuristic. In: Proceedings of the 2004 IEEE Conference on Cybernetic and Intelligent Systems (CIS 2004), Singapore, December 1-3, pp. 790–795 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gibbs, J., Kendall, G., Özcan, E. (2010). Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-15844-5_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15843-8
Online ISBN: 978-3-642-15844-5
eBook Packages: Computer ScienceComputer Science (R0)