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 141 articles , 1 standard article )

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  1. Swan, Jerry; Adriaensen, Steven; Brownlee, Alexander E. I.; Hammond, Kevin; Johnson, Colin G.; Kheiri, Ahmed; Krawiec, Faustyna; Merelo, J. J.; Minku, Leandro L.; Özcan, Ender; Pappa, Gisele L.; García-Sánchez, Pablo; Sörensen, Kenneth; Voß, Stefan; Wagner, Markus; White, David R.: Metaheuristics “In the large” (2022)
  2. Zhang, Yuchang; Bai, Ruibin; Qu, Rong; Tu, Chaofan; Jin, Jiahuan: A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties (2022)
  3. Ibrahim, Rehab Ali; Abd Elaziz, Mohamed; Ewees, Ahmed A.; El-Abd, Mohammed; Lu, Songfeng: New feature selection paradigm based on hyper-heuristic technique (2021)
  4. Kheiri, Ahmed; Gretsista, Angeliki; Keedwell, Ed; Lulli, Guglielmo; Epitropakis, Michael G.; Burke, Edmund K.: A hyper-heuristic approach based upon a hidden Markov model for the multi-stage nurse rostering problem (2021)
  5. Nijimbere, Dieudonné; Zhao, Songzheng; Gu, Xunhao; Esangbedo, Moses Olabhele; Dominique, Nyiribakwe: Tabu search guided by reinforcement learning for the max-mean dispersion problem (2021)
  6. Aslan, Ayse; Bakir, Ilke; Vis, Iris F. A.: A dynamic Thompson sampling hyper-heuristic framework for learning activity planning in personalized learning (2020)
  7. Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: Recent advances in selection hyper-heuristics (2020)
  8. Lamghari, Amina; Dimitrakopoulos, Roussos: Hyper-heuristic approaches for strategic mine planning under uncertainty (2020)
  9. Leng, Longlong; Zhang, Jingling; Zhang, Chunmiao; Zhao, Yanwei; Wang, Wanliang; Li, Gongfa: Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects (2020)
  10. Moreno, Alfredo; Munari, Pedro; Alem, Douglas: Decomposition-based algorithms for the crew scheduling and routing problem in road restoration (2020)
  11. Pandiri, Venkatesh; Singh, Alok: Two multi-start heuristics for the (k)-traveling salesman problem (2020)
  12. Ahmed, Leena; Mumford, Christine; Kheiri, Ahmed: Solving urban transit route design problem using selection hyper-heuristics (2019)
  13. Chaurasia, Sachchida Nand; Kim, Joong Hoon: An evolutionary algorithm based hyper-heuristic framework for the set packing problem (2019)
  14. Chaurasia, Sachchida Nand; Kim, Joong Hoon: An artificial bee colony based hyper-heuristic for the single machine order acceptance and scheduling problem (2019)
  15. Oude Vrielink, R. A.; Jansen, E. A.; Hans, E. W.; van Hillegersberg, J.: Practices in timetabling in higher education institutions: a systematic review (2019)
  16. Pillay, Nelishia; Özcan, Ender: Automated generation of constructive ordering heuristics for educational timetabling (2019)
  17. 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)
  18. Nagata, Yuichi: Random partial neighborhood search for the post-enrollment course timetabling problem (2018)
  19. 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)
  20. Corus, Dogan; He, Jun; Jansen, Thomas; Oliveto, Pietro S.; Sudholt, Dirk; Zarges, Christine: On easiest functions for mutation operators in bio-inspired optimisation (2017)

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