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Graham Kendall: Details of Requested Publication

Citation

Cowling, P; Kendall, G and Soubeiga, E Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling. In Proceedings of Parallel Problem Solving from Nature (PPSN VII), pages 851-860, Springer-Verlag, Granada, Spain, 7-11 September, Lecture Notes in Computer Science 2439, 2002.

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.

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doi

The doi for this publication is 10.1007/3-540-45712-7_82 You can link directly to the original paper, via the doi, from here

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information

URL

The URL for additional information is http://dx.doi.org/10.1007/3-540-45712-7

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Bibtex

@INPROCEEDINGS{cks2002, author = {P. Cowling and G. Kendall and E. Soubeiga},
title = {Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling},
booktitle = {Proceedings of Parallel Problem Solving from Nature (PPSN VII)},
year = {2002},
editor = {J. J. Merelo and P. Adamidis and H-G. Beyer},
volume = {2439},
series = {Lecture Notes in Computer Science},
pages = {851--860},
address = {Granada, Spain, 7-11 September},
publisher = {Springer-Verlag},
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.},
doi = {10.1007/3-540-45712-7_82},
keywords = {hyperheuristics. hyper-heuristics, nurse, personnel rostering, tabu search, genetic algorithms},
timestamp = {2007.03.29},
url = {http://dx.doi.org/10.1007/3-540-45712-7},
webpdf = {http://www.graham-kendall.com/papers/cks2002.pdf} }