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 wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE
Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

3D Bin Packing, help Santa and share $10,000

Publication(s)

A Hyper-Heuristic Approach to Strip Packing Problems
http://bit.ly/fkNqJz
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
http://bit.ly/egxt0d
An artificial neural network for predicting domestic hot water characteristics
http://bit.ly/dNtSFu
Frequency analysis for dendritic cell population tuning
http://bit.ly/go1Ihk

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Kendall, G and Whitwell, G A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem. INFORMS Journal on Computing, 21 (3): 505-516, 2009.


Abstract

The best-fit heuristic is a simple yet powerful one-pass approach for the two-dimensional rectangular stockcutting problem. It had achieved the best published results on a wide range of benchmark problems until the development of the approaches described in this paper. Here, we illustrate how improvements in solution quality can be achieved by the hybridisation of the best-fit heuristic together with simulated annealing and the bottom-left-fill algorithm. We compare and contrast the new hybrid approach with other approaches from the literature in terms of execution times and the quality of the solutions achieved. Using a range of standard benchmark problems from the literature, we demonstrate how the new approach achieves significantly better results than previously published methods on almost all of the problem instances. In addition, we provide results on 10 new benchmark problems to encourage further research and greater comparison between current and future methods.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1287/ijoc.1080.0306 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 (1.077), 2013 (1.120), 2012 (1.370), 2011 (1.076), 2010 (1.172), 2009 (1.318), 2008 (1.041), 2007 (0.907), 2006 (0.865), 2005 (1.762), 2004 (1.522), 2003 (0.761), 2002 (0.979), 2001 (0.729)

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{bkw2009, author = {E.K. Burke and G. Kendall and G. Whitwell},
title = {A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem},
journal = {INFORMS Journal on Computing},
year = {2009},
volume = {21},
pages = {505--516},
number = {3},
month = {August 2009},
abstract = {The best-fit heuristic is a simple yet powerful one-pass approach for the two-dimensional rectangular stockcutting problem. It had achieved the best published results on a wide range of benchmark problems until the development of the approaches described in this paper. Here, we illustrate how improvements in solution quality can be achieved by the hybridisation of the best-fit heuristic together with simulated annealing and the bottom-left-fill algorithm. We compare and contrast the new hybrid approach with other approaches from the literature in terms of execution times and the quality of the solutions achieved. Using a range of standard benchmark problems from the literature, we demonstrate how the new approach achieves significantly better results than previously published methods on almost all of the problem instances. In addition, we provide results on 10 new benchmark problems to encourage further research and greater comparison between current and future methods.},
doi = {10.1287/ijoc.1080.0306},
issn = {1091-9856},
keywords = {stock cutting, simulated annealing, heuristics, industries, manufacturing},
owner = {rxj},
timestamp = {2008.09.10},
webpdf = {http://www.graham-kendall.com/papers/bkw2009.pdf} }