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

Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX
I have published some papers on timetabling.
http://bit.ly/hSGAhZ

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Data Visualisation Competition

Publication(s)

Evolving human-competitive reusable 2D strip packing heuristics
http://bit.ly/f4msct
Solving Multi-objective Optimisation Problems Using the Potential Pareto Regions Evolutionary Algorithm
http://bit.ly/fCOMDK
Investigating the impact of alternative evolutionary selection strategies on multi-method global optimization
http://bit.ly/1a2YEuE
An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems
http://bit.ly/29btjbV

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Kendall, G and Whitwell, G A New Placement Heuristic for the Orthogonal Stock-Cutting Problem. Operations Research, 52 (4): 655-671, 2004.


Abstract

This paper presents a new best-fit heuristic for the two-dimensional rectangular stock-cutting problem and demonstrates its effectiveness by comparing it against other published approaches. A placement algorithm usually takes a list of shapes, sorted by some property such as increasing height or decreasing area, and then applies a placement rule to each of these shapes in turn. The proposed method is not restricted to the first shape encountered but may dynamically search the list for better candidate shapes for placement. We suggest an efficient implementation of our heuristic and show that it compares favourably to other heuristic and metaheuristic approaches from the literature in terms of both solution quality and execution time. We also present data for new problem instances to encourage further research and greater comparison between this and future methods.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1287/opre.1040.0109 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 (1.743), 2013 (1.500), 2012 (1.786), 2011 (1.665), 2010 (1.995), 2009 (1.576), 2008 (1.463), 2007 (1.467), 2006 (1.234), 2005 (1.219), 2004 (0.803), 2003 (0.672), 2002 (0.892), 2001 (0.813), 2000 (1.006)

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{bkg2004b, author = {E.K. Burke and G. Kendall and G. Whitwell},
title = {A New Placement Heuristic for the Orthogonal Stock-Cutting Problem},
journal = {Operations Research},
year = {2004},
volume = {52},
pages = {655--671},
number = {4},
month = {July-August 2004},
abstract = {This paper presents a new best-fit heuristic for the two-dimensional rectangular stock-cutting problem and demonstrates its effectiveness by comparing it against other published approaches. A placement algorithm usually takes a list of shapes, sorted by some property such as increasing height or decreasing area, and then applies a placement rule to each of these shapes in turn. The proposed method is not restricted to the first shape encountered but may dynamically search the list for better candidate shapes for placement. We suggest an efficient implementation of our heuristic and show that it compares favourably to other heuristic and metaheuristic approaches from the literature in terms of both solution quality and execution time. We also present data for new problem instances to encourage further research and greater comparison between this and future methods.},
doi = {10.1287/opre.1040.0109},
issn = {0030-364X},
keywords = {Cutting, Packing, Stock Cutting, Heuristic, Best Fit},
webpdf = {http://www.graham-kendall.com/papers/bkg2004b.pdf} }