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A stochastic local search algorithm with adaptive acceptance for high-school timetabling

Author

Listed:
  • Ahmed Kheiri

    (University of Nottingham)

  • Ender Özcan

    (University of Nottingham)

  • Andrew J. Parkes

    (University of Nottingham)

Abstract

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective ‘heuristic to choose heuristics’ to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism.

Suggested Citation

  • Ahmed Kheiri & Ender Özcan & Andrew J. Parkes, 2016. "A stochastic local search algorithm with adaptive acceptance for high-school timetabling," Annals of Operations Research, Springer, vol. 239(1), pages 135-151, April.
    • Handle: RePEc:spr:annopr:v:239:y:2016:i:1:d:10.1007_s10479-014-1660-0
    DOI: 10.1007/s10479-014-1660-0
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    References listed on IDEAS

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    1. Barry McCollum & Andrea Schaerf & Ben Paechter & Paul McMullan & Rhyd Lewis & Andrew J. Parkes & Luca Di Gaspero & Rong Qu & Edmund K. Burke, 2010. "Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 120-130, February.
    2. Gerhard Post & Samad Ahmadi & Sophia Daskalaki & Jeffrey Kingston & Jari Kyngas & Cimmo Nurmi & David Ranson, 2012. "An XML format for benchmarks in High School Timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 385-397, April.
    3. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    4. D. Abramson, 1991. "Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms," Management Science, INFORMS, vol. 37(1), pages 98-113, January.
    5. Helena R. Lourenço & Olivier C. Martin & Thomas Stützle, 2010. "Iterated Local Search: Framework and Applications," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 363-397, Springer.
    6. de Werra, D., 1997. "The combinatorics of timetabling," European Journal of Operational Research, Elsevier, vol. 96(3), pages 504-513, February.
    7. Arnaldo Vieira Moura & Rafael Augusto Scaraficci, 2010. "A GRASP strategy for a more constrained School Timetabling Problem," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 7(2), pages 152-170.
    8. Anjuli Kannan & Gerald van den Berg & Adeline Kuo, 2012. "iSchedule to Personalize Learning," Interfaces, INFORMS, vol. 42(5), pages 437-448, October.
    9. Michael Marte, 2007. "Towards constraint-based school timetabling," Annals of Operations Research, Springer, vol. 155(1), pages 207-225, November.
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    Cited by:

    1. Drake, John H. & Kheiri, Ahmed & Özcan, Ender & Burke, Edmund K., 2020. "Recent advances in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 405-428.

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