Graham Kendall
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Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

I have published some papers on timetabling.
http://bit.ly/hSGAhZ
I have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Football fixture forecasting. Are you any good?

Publication(s)

Comparison of meta-heuristic algorithms for clustering rectangles
http://bit.ly/eQQ0Kd
The Importance of Look-Ahead Depth in Evolutionary Checkers
http://bit.ly/1bh6fGH
Learning with imperfections - a multi-agent neural-genetic trading system with differing levels of social learning
http://bit.ly/hBQypU
Solving Examination Timetabling Problems using Honey-bee Mating Optimization (ETP-HBMO)
http://bit.ly/eactEZ

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.


pdf

You can download the pdf of this publication from here


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

The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


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} }