Graham Kendall
Various Images

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 number of papers on Cutting and Packing
http://bit.ly/dQPw7T
I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

The Christmas Present Problem: It’s Hard – NP-Hard

Publication(s)

Scheduling in sports: An annotated bibliography
http://bit.ly/eCfi42
Heuristic Space Diversity Management in a Meta-Hyper-Heuristic Framework
http://bit.ly/1uuQW45
Enumerating knight's tours using an ant colony algorithm
http://bit.ly/fMCY7C
Tabu assisted guided local search approaches for freight service network design
http://bit.ly/1mlrwTF

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} }