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.
Keywords for this software
References in zbMATH (referenced in 120 articles , 1 standard article )
Showing results 1 to 20 of 120.
Sorted by year (- 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)
- Nagata, Yuichi: Random partial neighborhood search for the post-enrollment course timetabling problem (2018)
- 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)
- Corus, Dogan; He, Jun; Jansen, Thomas; Oliveto, Pietro S.; Sudholt, Dirk; Zarges, Christine: On easiest functions for mutation operators in bio-inspired optimisation (2017)
- Goh, Say Leng; Kendall, Graham; Sabar, Nasser R.: Improved local search approaches to solve the post enrolment course timetabling problem (2017)
- Karapetyan, Daniel; Punnen, Abraham P.; Parkes, Andrew J.: Markov chain methods for the bipartite Boolean quadratic programming problem (2017)
- Kozik, Andrzej: Handling precedence constraints in scheduling problems by the sequence pair representation (2017)
- Lei, Yu; Shi, Jiao: A NNIA scheme for timetabling problems (2017)
- Smith, Stephen L.; Imeson, Frank: GLNS: an effective large neighborhood search heuristic for the generalized traveling salesman problem (2017)
- 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)
- Burke, Edmund K.; Bykov, Yuri: An adaptive flex-deluge approach to university exam timetabling (2016)
- Butelle, Franck; Alfandari, Laurent; Coti, Camille; Finta, Lucian; Létocart, Lucas; Plateau, Gérard; Roupin, Frédéric; Rozenknop, Antoine; Wolfler Calvo, Roberto: Fast machine reassignment (2016)
- Bykov, Yuri; Petrovic, Sanja: A step counting hill climbing algorithm applied to university examination timetabling (2016)
- Kheiri, Ahmed; Özcan, Ender: An iterated multi-stage selection hyper-heuristic (2016)
- Kheiri, Ahmed; Özcan, Ender; Parkes, Andrew J.: A stochastic local search algorithm with adaptive acceptance for high-school timetabling (2016)
- 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)
- Mühlenthaler, Moritz; Wanka, Rolf: Fairness in academic course timetabling (2016)
- Pillay, Nelishia: A review of hyper-heuristics for educational timetabling (2016)
- Talbi, El-Ghazali: Combining metaheuristics with mathematical programming, constraint programming and machine learning (2016)
- Woumans, Gert; De Boeck, Liesje; Beliën, Jeroen; Creemers, Stefan: A column generation approach for solving the examination-timetabling problem (2016)