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

What do we spend so much in supermarkets?
http://bit.ly/1yW6If7
Does AI have a place in the board room?
http://bit.ly/1DXreuW

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

The science that makes us spend more in supermarkets, and feel good while we do it

Publication(s)

Chapter 3: Learning IPD Strategies through Coevolution
http://bit.ly/1eUlKoP
Evolutionary Strategies vs. Neural Networks: An Inflation Forecasting Experiment
http://bit.ly/h6J8Xv
A New Dynamic Point Specification Approach to Optimise Surface Mount Placement Machine in Printed Circuit Board Assembly
http://bit.ly/hWCyZA
A hyper-heuristic methodology to generate adaptive strategies for games
http://bit.ly/1ipWJDQ

Graham Kendall: Details of Requested Publication


Citation

Kendall, G Scheduling English football fixtures over holiday periods. Journal of the Operational Research Society, 59 (6): 743-755, 2008.


Abstract

Every year the English football authorities produce a set of fixtures for the four main divisions in England. Over the Christmas and New Year period every team has to play two fixtures; one being played at their home venue and the other at an opponent's venue. There are various other constraints that also have to be respected with the overall objective being to minimize the total distance travelled by all teams. In this paper, I formally define the problem, discuss the data collection that I have undertaken and present the algorithm (which is based on depth first search, followed by a local search) I have developed. Using data from four seasons, I show that I am able to produce better schedules than those currently used.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1057/palgrave.jors.2602382 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 (0.953), 2013 (0.911), 2012 (0.989), 2011 (0.971), 2010 (1.102), 2009 (1.009), 2008 (0.839), 2007 (0.784), 2006 (0.597), 2005 (0.603), 2004 (0.515), 2003 (0.416), 2002 (0.493), 2001 (0.438), 2000 (0.648)

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{k2008, author = {G. Kendall},
title = {Scheduling English football fixtures over holiday periods},
journal = {Journal of the Operational Research Society},
year = {2008},
volume = {59},
pages = {743--755},
number = {6},
abstract = {Every year the English football authorities produce a set of fixtures for the four main divisions in England. Over the Christmas and New Year period every team has to play two fixtures; one being played at their home venue and the other at an opponent's venue. There are various other constraints that also have to be respected with the overall objective being to minimize the total distance travelled by all teams. In this paper, I formally define the problem, discuss the data collection that I have undertaken and present the algorithm (which is based on depth first search, followed by a local search) I have developed. Using data from four seasons, I show that I am able to produce better schedules than those currently used.},
doi = {10.1057/palgrave.jors.2602382},
issn = {0160-5682},
keywords = {scheduling, sport, optimization, soccer, football, optimisation},
owner = {est},
timestamp = {2010.02.22},
webpdf = {http://www.graham-kendall.com/papers/k2008.pdf} }