Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics

  • Jonathon Gibbs
  • Graham Kendall
  • Ender Özcan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6238)


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.


Hyper-heuristic Metaheuristic Local Search Machine Learning Sports Scheduling 


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  1. 1.
    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)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Özcan, E., Bilgin, B., Korkmaz, E.: A comprehensive analysis of hyper-heuristics. In: Intelligent Data Analysis, pp. 3–23 (2008)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Kendall, G., Knust, S., Ribeiro, C., Urrutia, S.: Scheduling in sports: An annotated bibliography. Computers & Operations Research 37, 1–19 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Kendall, G.: Scheduling english football fixtures over holiday periods. Journal of the Operational Research Society 59(6), 743–755 (2008)zbMATHCrossRefGoogle Scholar
  12. 12.
    Kendall, G.: Hybridising cplex with simulated annealing to minimise travel distances for english football fixtures (2009) (in review)Google Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    Wiering, M.: Qv(lambda)-learning: A new on-policy reinforcement learning algorithm. In: Proceedings of the 7th European Workshop on Reinforcement Learning (2005)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    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)CrossRefGoogle Scholar
  23. 23.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Dueck, G.: New optimization heuristics: The great deluge algorithm and the record-to record travel. Journal of Computational Physics 104, 86–92 (1993)zbMATHCrossRefGoogle Scholar
  25. 25.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jonathon Gibbs
    • 1
  • Graham Kendall
    • 1
  • Ender Özcan
    • 1
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK

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