Application of a hybrid genetic algorithm to airline crew scheduling
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Local search techniques for large high school timetabling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Finding good nurse duty schedules: a case study
Journal of Scheduling
A new dispatching rule based genetic algorithm for the multi-objective job shop problem
Journal of Heuristics
Improving the performance of vector hyper-heuristics through local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Journal of Global Optimization
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A hyperheuristic is a high-level heuristic which adaptively chooses between 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, in a reasonable amount of CPU time. For certain classes of problems, this generic method has been shown to yield high-quality practical solutions in a much shorter development time than that of other approaches such as tabu search and genetic algorithms, and using relatively little domain-knowledge. Hyperheuristics have previously been successfully applied by the authors to two real-world problems of personnel scheduling. In this paper, a hyperheuristic approach is used to solve 52 instances of an NP-hard nurse scheduling problem occuring at a major UK hospital. Compared with tabu-search and genetic algorithms, which have previously been used to solve the same problem, the hyper-heuristic proves to be as robust as the former and more reliable than the latter in terms of solution feasibility. The hyperheuristic also compares favourably with both methods in terms of ease-of-implementation of both the approach and the low-level heuristics used.