Graham Kendall
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Professor Graham Kendall

University of Nottingham, UK

I am a Professor of Computer Science at the University of Nottingham (UK). I am currently the Vice-Provost (Research and Knowledge Transfer) at our campus in Malaysia. I am a member of the Automated Scheduling, Optimisation and Planning (ASAP) Research Group. My interests include Operational Research, Evolutionary Computing, Scheduling (particularly sports scheduling), Cutting and Packing, Timetabling and Games (both games in the usual sense of the word as well as mathematical games such as the Iterated Prisoners Dilemma).

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How academics can engage with policy

News

I am a director of a spin out company that specialises in Nesting Software
http://bit.ly/i9wYHy

Publication

Evolving Neural Networks with Evolutionary Strategies: A New Application to Divisa Money
http://bit.ly/dKzEAy

Publication

Academic Timetabling: Linking Research and Practice
http://bit.ly/eroN3m

Publication

Evolving Neural Networks with Evolutionary Strategies: A New Application to Divisa Money
http://bit.ly/dKzEAy

Publication

Guided Operators for a Hyper-Heuristic Genetic Algorithm
http://bit.ly/hd4Erh

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Hyde, M.R and Kendall, G A Squeaky Wheel Optimisation Methodology for Two Dimensional Strip Packing. Computers & Operations Research, 38 (7): 1035-1044, 2011.


Abstract

The two-dimensional strip packing problem occurs in industries such as metal, wood, glass, paper, and textiles. The problem involves cutting shapes from a larger stock sheet or roll of material, while minimising waste. This is a well studied problem for which many heuristic methodologies are available in the literature, ranging from the basic one-pass best-fit heuristic, to the state of the art Reactive GRASP and SVC(SubKP) iterative procedures. The contribution of this paper is to present a much simpler but equally competitive iterative packing methodology based on squeaky wheel optimisation. After each complete packing (iteration), a penalty is applied to pieces that directly decreased the solution quality. These penalties inform the packing in the next iteration, so that the offending pieces are packed earlier. This methodology is deterministic and very easy to implement, and can obtain some best results on benchmark instances from the literature.


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doi

The doi for this publication is 10.1016/j.cor.2010.10.005 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.

2013 (1.718), 2012 (1.909), 2011 (1.720), 2010 (1.769), 2009 (2.116), 2008 (1.366), 2007 (1.147), 2006 (0.893), 2005 (0.746), 2004 (0.562), 2003 (0.486), 2002 (0.446), 2001 (0.375), 2000 (0.337)

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Bibtex

@ARTICLE{bhk2011, author = {E.K. Burke and M.R. Hyde and G. Kendall},
title = {A Squeaky Wheel Optimisation Methodology for Two Dimensional Strip Packing},
journal = {Computers \& Operations Research},
year = {2011},
volume = {38},
pages = {1035--1044},
number = {7},
month = {July},
abstract = {The two-dimensional strip packing problem occurs in industries such as metal, wood, glass, paper, and textiles. The problem involves cutting shapes from a larger stock sheet or roll of material, while minimising waste. This is a well studied problem for which many heuristic methodologies are available in the literature, ranging from the basic one-pass best-fit heuristic, to the state of the art Reactive GRASP and SVC(SubKP) iterative procedures. The contribution of this paper is to present a much simpler but equally competitive iterative packing methodology based on squeaky wheel optimisation. After each complete packing (iteration), a penalty is applied to pieces that directly decreased the solution quality. These penalties inform the packing in the next iteration, so that the offending pieces are packed earlier. This methodology is deterministic and very easy to implement, and can obtain some best results on benchmark instances from the literature.},
doi = {10.1016/j.cor.2010.10.005},
keywords = {cutting, packing, two-dimensional, strip packing, Cutting stock, Heuristics, Local search, Artificial intelligence},
owner = {gxk},
timestamp = {2010.12.03},
webpdf = {http://www.graham-kendall.com/papers/bhk2011.pdf} }