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 99 articles , 1 standard article )

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  1. Kheiri, Ahmed; Özcan, Ender; Parkes, Andrew J.: A stochastic local search algorithm with adaptive acceptance for high-school timetabling (2016)
  2. 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)
  3. Mühlenthaler, Moritz; Wanka, Rolf: Fairness in academic course timetabling (2016)
  4. Pillay, Nelishia: A review of hyper-heuristics for educational timetabling (2016)
  5. Talbi, El-Ghazali: Combining metaheuristics with mathematical programming, constraint programming and machine learning (2016)
  6. Burke, Edmund K.; Drake, John H.; McCollum, Barry; Özcan, Ender: Comments on: “An overview of curriculum-based course timetabling” (2015)
  7. Hiermann, Gerhard; Prandtstetter, Matthias; Rendl, Andrea; Puchinger, Jakob; Raidl, Günther R.: Metaheuristics for solving a multimodal home-healthcare scheduling problem (2015)
  8. 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)
  9. Muggy, Luke; Easton, Todd: Generating class schedules within a complex modular environment with application to secondary schools (2015)
  10. Wu, Qinghua; Hao, Jin-Kao: A review on algorithms for maximum clique problems (2015)
  11. 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)
  12. Abdul-Rahman, Syariza; Burke, Edmund K.; Bargiela, Andrzej; McCollum, Barry; Özcan, Ender: A constructive approach to examination timetabling based on adaptive decomposition and ordering (2014)
  13. Al-Betar, Mohammed Azmi; Khader, Ahamad Tajudin; Doush, Iyad Abu: Memetic techniques for examination timetabling (2014)
  14. Burke, Edmund K.; Qu, Rong; Soghier, Amr: Adaptive selection of heuristics for improving exam timetables (2014)
  15. Caraffini, Fabio; Neri, Ferrante; Picinali, Lorenzo: An analysis on separability for memetic computing automatic design (2014)
  16. Pillay, Nelishia: A survey of school timetabling research (2014)
  17. Segredo, Eduardo; Segura, Carlos; León, Coromoto: Memetic algorithms and hyperheuristics applied to a multiobjectivised two-dimensional packing problem (2014)
  18. Smet, Pieter; Bilgin, Burak; De Causmaecker, Patrick; Vanden Berghe, Greet: Modelling and evaluation issues in nurse rostering (2014)
  19. Smith-Miles, Kate; Baatar, Davaatseren: Exploring the role of graph spectra in graph coloring algorithm performance (2014)
  20. Soria-Alcaraz, Jorge A.; Ochoa, Gabriela; Swan, Jerry; Carpio, Martin; Puga, Hector; Burke, Edmund K.: Effective learning hyper-heuristics for the course timetabling problem (2014)

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