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 a few papers on Sports Scheduling.
http://bit.ly/gVaUqT
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

Non-symmetric Vehicle Routing

Publication(s)

Memory Length in Hyper-heuristics: An Empirical Study
http://bit.ly/eXAo7v
A nozzle selection heuristic to optimise the hybrid pick and place machine
http://bit.ly/eAjsEX
An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems
http://bit.ly/29btjbV
The Importance of Look-Ahead Depth in Evolutionary Checkers
http://bit.ly/1bh6fGH

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K; Kendall, G and Soubeiga, E A Tabu-Search Hyperheuristic for Timetabling and Rostering. Journal of Heuristics, 9 (6): 451-470, 2003.


Abstract

Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem. In this paper we report the investigation of a hyperheuristic approach and evaluate it on various instances of two distinct timetabling and rostering problems. In the framework of our hyperheuristic approach, heuristics compete using rules based on the principles of reinforcement learning. A tabu list of heuristics is also maintained which prevents certain heuristics from being chosen at certain times during the search. We demonstrate that this tabu-search hyperheuristic is an easily re-usable method which can produce solutions of at least acceptable quality across a variety of problems and instances. In effect the proposed method is capable of producing solutions that are competitive with those obtained using state-of-the-art problem-specific techniques for the problems studied here, but is fundamentally more general than those techniques.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1023/B:HEUR.0000012446.94732.b6 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.

2014 (1.135), 2013 (1.359), 2012 (1.471), 2011 (1.262), 2010 (1.623), 2009 (1.264), 2008 (1.064), 2007 (0.644), 2006 (0.740), 2005 (0.551), 2004 (1.113), 2003 (0.633), 2002 (0.655), 2001 (0.404), 2000 (0.650)

URL

This pubication does not have a URL associated with it.

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

@ARTICLE{bks2003, author = {E. K. Burke and G. Kendall and E. Soubeiga},
title = {A Tabu-Search Hyperheuristic for Timetabling and Rostering},
journal = {Journal of Heuristics},
year = {2003},
volume = {9},
pages = {451--470},
number = {6},
month = {December},
abstract = {Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem. In this paper we report the investigation of a hyperheuristic approach and evaluate it on various instances of two distinct timetabling and rostering problems. In the framework of our hyperheuristic approach, heuristics compete using rules based on the principles of reinforcement learning. A tabu list of heuristics is also maintained which prevents certain heuristics from being chosen at certain times during the search. We demonstrate that this tabu-search hyperheuristic is an easily re-usable method which can produce solutions of at least acceptable quality across a variety of problems and instances. In effect the proposed method is capable of producing solutions that are competitive with those obtained using state-of-the-art problem-specific techniques for the problems studied here, but is fundamentally more general than those techniques.},
doi = {10.1023/B:HEUR.0000012446.94732.b6},
issn = {1381-1231},
keywords = {hyperheuristic, tabu search, heuristic, scheduling, rostering, timetabling, local search, hyper-heuristics},
timestamp = {2008.02.28},
webpdf = {http://www.graham-kendall.com/papers/bks2003.pdf} }