Hyperheuristics: A robust optimisation method applied to nurse scheduling

Abstract

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 hyperheuristic 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.