Hyperheuristics

The term hyperheuristic was introduced by the authors as a high-level heuristic that adaptively controls several low-level knowledge-poor heuristics so that while using only cheap, easy-to-implement low-level heuristics, we may achieve solution quality approaching that of an expensive knowledge-rich approach. For certain classes of problems, this allows us to rapidly produce effective solutions, in a fraction of the time needed for other approaches, and using a level of expertise common among non-academic IT professionals. Hyperheuristics have been successfully applied by the authors to a real-world problem of personnel scheduling. In this paper, the authors report another successful application of hyperheuristics to a rather different real-world problem of personnel scheduling occuring at a UK academic institution. Not only did the hyperheuristics produce results of a quality much superior to that of a manual solution but also these results were produced within a period of only three weeks due to the savings resulting from using the existing hyperheuristic software framework.


References in zbMATH (referenced in 112 articles , 1 standard article )

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  1. Corus, Dogan; He, Jun; Jansen, Thomas; Oliveto, Pietro S.; Sudholt, Dirk; Zarges, Christine: On easiest functions for mutation operators in bio-inspired optimisation (2017)
  2. Kozik, Andrzej: Handling precedence constraints in scheduling problems by the sequence pair representation (2017)
  3. Burke, Edmund K.; Bykov, Yuri: An adaptive flex-deluge approach to university exam timetabling (2016)
  4. Butelle, Franck; Alfandari, Laurent; Coti, Camille; Finta, Lucian; Létocart, Lucas; Plateau, Gérard; Roupin, Frédéric; Rozenknop, Antoine; Wolfler Calvo, Roberto: Fast machine reassignment (2016)
  5. Bykov, Yuri; Petrovic, Sanja: A step counting hill climbing algorithm applied to university examination timetabling (2016)
  6. Kheiri, Ahmed; Özcan, Ender: An iterated multi-stage selection hyper-heuristic (2016)
  7. Kheiri, Ahmed; Özcan, Ender; Parkes, Andrew J.: A stochastic local search algorithm with adaptive acceptance for high-school timetabling (2016)
  8. Lopez-Loces, Mario C.; Musial, Jedrzej; Pecero, Johnatan E.; Fraire-Huacuja, Hector J.; Blazewicz, Jacek; Bouvry, Pascal: Exact and heuristic approaches to solve the Internet shopping optimization problem with delivery costs (2016)
  9. Mühlenthaler, Moritz; Wanka, Rolf: Fairness in academic course timetabling (2016)
  10. Pillay, Nelishia: A review of hyper-heuristics for educational timetabling (2016)
  11. Talbi, El-Ghazali: Combining metaheuristics with mathematical programming, constraint programming and machine learning (2016)
  12. Woumans, Gert; De Boeck, Liesje; Beliën, Jeroen; Creemers, Stefan: A column generation approach for solving the examination-timetabling problem (2016)
  13. Burke, Edmund K.; Drake, John H.; McCollum, Barry; Özcan, Ender: Comments on: “An overview of curriculum-based course timetabling” (2015)
  14. Hiermann, Gerhard; Prandtstetter, Matthias; Rendl, Andrea; Puchinger, Jakob; Raidl, Günther R.: Metaheuristics for solving a multimodal home-healthcare scheduling problem (2015)
  15. Johnes, Jill: Operational research in education (2015)
  16. Li, Jingpeng; Bai, Ruibin; Shen, Yindong; Qu, Rong: Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling (2015)
  17. Muggy, Luke; Easton, Todd: Generating class schedules within a complex modular environment with application to secondary schools (2015)
  18. Qin, Hu; Ming, Wei; Zhang, Zizhen; Xie, Yubin; Lim, Andrew: A tabu search algorithm for the multi-period inspector scheduling problem (2015)
  19. Wu, Qinghua; Hao, Jin-Kao: A review on algorithms for maximum clique problems (2015)
  20. Abdul Rahman, Syariza; Bargiela, Andrzej; Burke, Edmund K.; Özcan, Ender; McCollum, Barry; McMullan, Paul: Adaptive linear combination of heuristic orderings in constructing examination timetables (2014)

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