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 125 articles , 1 standard article )

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  1. Ahmed, Leena; Mumford, Christine; Kheiri, Ahmed: Solving urban transit route design problem using selection hyper-heuristics (2019)
  2. Chaurasia, Sachchida Nand; Kim, Joong Hoon: An artificial bee colony based hyper-heuristic for the single machine order acceptance and scheduling problem (2019)
  3. M. Pour, Shahrzad; Drake, John H.; Burke, Edmund K.: A choice function hyper-heuristic framework for the allocation of maintenance tasks in Danish railways (2018)
  4. Nagata, Yuichi: Random partial neighborhood search for the post-enrollment course timetabling problem (2018)
  5. Chen, Yujie; Cowling, Peter; Polack, Fiona; Remde, Stephen; Mourdjis, Philip: Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system (2017)
  6. Corus, Dogan; He, Jun; Jansen, Thomas; Oliveto, Pietro S.; Sudholt, Dirk; Zarges, Christine: On easiest functions for mutation operators in bio-inspired optimisation (2017)
  7. Goh, Say Leng; Kendall, Graham; Sabar, Nasser R.: Improved local search approaches to solve the post enrolment course timetabling problem (2017)
  8. Karapetyan, Daniel; Punnen, Abraham P.; Parkes, Andrew J.: Markov chain methods for the bipartite Boolean quadratic programming problem (2017)
  9. Kozik, Andrzej: Handling precedence constraints in scheduling problems by the sequence pair representation (2017)
  10. Lei, Yu; Shi, Jiao: A NNIA scheme for timetabling problems (2017)
  11. Rajaram, R.; Castellani, B.; Wilson, A. N.: Advancing Shannon entropy for measuring diversity in systems (2017)
  12. Smith, Stephen L.; Imeson, Frank: GLNS: an effective large neighborhood search heuristic for the generalized traveling salesman problem (2017)
  13. Soria-Alcaraz, Jorge A.; Ochoa, Gabriela; Sotelo-Figeroa, Marco A.; Burke, Edmund K.: A methodology for determining an effective subset of heuristics in selection hyper-heuristics (2017)
  14. Burke, Edmund K.; Bykov, Yuri: An adaptive flex-deluge approach to university exam timetabling (2016)
  15. Butelle, Franck; Alfandari, Laurent; Coti, Camille; Finta, Lucian; Létocart, Lucas; Plateau, Gérard; Roupin, Frédéric; Rozenknop, Antoine; Wolfler Calvo, Roberto: Fast machine reassignment (2016)
  16. Bykov, Yuri; Petrovic, Sanja: A step counting hill climbing algorithm applied to university examination timetabling (2016)
  17. Kheiri, Ahmed; Özcan, Ender: An iterated multi-stage selection hyper-heuristic (2016)
  18. Kheiri, Ahmed; Özcan, Ender; Parkes, Andrew J.: A stochastic local search algorithm with adaptive acceptance for high-school timetabling (2016)
  19. 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)
  20. Mühlenthaler, Moritz; Wanka, Rolf: Fairness in academic course timetabling (2016)

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