Graham Kendall
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Professor Graham Kendall

University of Nottingham, UK

I am a Professor of Computer Science at the University of Nottingham (UK). I am currently the Vice-Provost (Research and Knowledge Transfer) at our campus in Malaysia. I am a member of the Automated Scheduling, Optimisation and Planning (ASAP) Research Group. My interests include Operational Research, Evolutionary Computing, Scheduling (particularly sports scheduling), Cutting and Packing, Timetabling and Games (both games in the usual sense of the word as well as mathematical games such as the Iterated Prisoners Dilemma).

Latest Blog Post

My First Java Project

Random Blog Post

MISTA Conference: Plenary Talk (Moshe Dror)

News

I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T

Publication

General Video Game Playing
http://bit.ly/1a2Vjvz

Publication

An Investigation of an Adaptive Scheduling for Multi Headed Placement Machines Using a Greedy Search
http://bit.ly/g0DYmz

Publication

Chapter 7: Opponent Modelling, Evolution, and the Iterated Prisoner's Dilemma
http://bit.ly/1eJzj8n

Publication

A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine
http://bit.ly/dJZXFI

Graham Kendall: Details of Requested Publication


Citation

Bai, R; Burke, E. K; Kendall, G; Li, J and McCollum, B A Hybrid Evolutionary Approach to the Nurse Rostering Problem. IEEE Transactions on Evolutionary Computation, 14 (4): 580-590, 2010.


Abstract

Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.


pdf

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doi

The doi for this publication is 10.1109/TEVC.2009.2033583 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


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2012 (4.810), 2011 (3.341), 2010 (4.403), 2009 (4.589), 2008 (3.736), 2007 (2.426), 2006 (3.770), 2005 (3.257), 2004 (3.688), 2003 (2.713), 2002 (1.486), 2001 (1.708)

URL

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Bibtex

@ARTICLE{bbklm2010, author = {R. Bai and E. K. Burke and G. Kendall and J. Li and B. McCollum},
title = {A Hybrid Evolutionary Approach to the Nurse Rostering Problem},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2010},
volume = {14},
pages = {580--590},
number = {4},
month = {July 2010},
abstract = {Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.},
doi = {10.1109/TEVC.2009.2033583},
keywords = {Nurse Rostering, Personnel Rostering, Genetic Algorithms, Simulated Annealing, Hyper-heuristics, Meta-heuristics, hyperheuristics, metaheuristics},
owner = {gxk},
timestamp = {2010.10.12},
webpdf = {http://www.graham-kendall.com/papers/bbklm2010.pdf} }