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 133 articles , 1 standard article )

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  1. Aslan, Ayse; Bakir, Ilke; Vis, Iris F. A.: A dynamic Thompson sampling hyper-heuristic framework for learning activity planning in personalized learning (2020)
  2. Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: Recent advances in selection hyper-heuristics (2020)
  3. Lamghari, Amina; Dimitrakopoulos, Roussos: Hyper-heuristic approaches for strategic mine planning under uncertainty (2020)
  4. Moreno, Alfredo; Munari, Pedro; Alem, Douglas: Decomposition-based algorithms for the crew scheduling and routing problem in road restoration (2020)
  5. Ahmed, Leena; Mumford, Christine; Kheiri, Ahmed: Solving urban transit route design problem using selection hyper-heuristics (2019)
  6. Chaurasia, Sachchida Nand; Kim, Joong Hoon: An artificial bee colony based hyper-heuristic for the single machine order acceptance and scheduling problem (2019)
  7. Oude Vrielink, R. A.; Jansen, E. A.; Hans, E. W.; van Hillegersberg, J.: Practices in timetabling in higher education institutions: a systematic review (2019)
  8. Pillay, Nelishia; Özcan, Ender: Automated generation of constructive ordering heuristics for educational timetabling (2019)
  9. 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)
  10. Nagata, Yuichi: Random partial neighborhood search for the post-enrollment course timetabling problem (2018)
  11. 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)
  12. Corus, Dogan; He, Jun; Jansen, Thomas; Oliveto, Pietro S.; Sudholt, Dirk; Zarges, Christine: On easiest functions for mutation operators in bio-inspired optimisation (2017)
  13. Goh, Say Leng; Kendall, Graham; Sabar, Nasser R.: Improved local search approaches to solve the post enrolment course timetabling problem (2017)
  14. Karapetyan, Daniel; Punnen, Abraham P.; Parkes, Andrew J.: Markov chain methods for the bipartite Boolean quadratic programming problem (2017)
  15. Kozik, Andrzej: Handling precedence constraints in scheduling problems by the sequence pair representation (2017)
  16. Lei, Yu; Shi, Jiao: A NNIA scheme for timetabling problems (2017)
  17. Rajaram, R.; Castellani, B.; Wilson, A. N.: Advancing Shannon entropy for measuring diversity in systems (2017)
  18. Smith, Stephen L.; Imeson, Frank: GLNS: an effective large neighborhood search heuristic for the generalized traveling salesman problem (2017)
  19. 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)
  20. Burke, Edmund K.; Bykov, Yuri: An adaptive flex-deluge approach to university exam timetabling (2016)

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