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 published some papers on timetabling.
http://bit.ly/hSGAhZ
How are university examinations scheduled?
http://bit.ly/1z0pG4s

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Tracking Paper Downloads for MISTA

Publication(s)

An Adaptive Length Chromosome Hyperheuristic Genetic Algorithm for a Trainer Scheduling Problem
http://bit.ly/ezw9NR
Handling diversity in evolutionary multiobjective optimization
http://bit.ly/hN8VPE
Youth Sports Leagues Scheduling
http://bit.ly/f1i7SE
Regulators as Ďagentsí: power and personality in risk regulation and a role for agent-based simulation
http://bit.ly/evaXWn

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K and Kendall, G A New Approach to Packing Non-Convex Polygons Using the No Fit Polygon and Meta-Heuristic and Evolutionary Algorithms. In Proceedings of the 5th International Conference on Adapative Computing in Design and Manufacture (ACDM 2002), pages 193-204, Springer-Verlag, University of Exeter, UK, 16-18 April, 2002.


Abstract

Earlier work by the authors has presented a method for packing convex polygons, using a construction known as the no fit polygon. Although the method was shown to be successful, it could only deal with convex polygons. This paper addresses the issue by showing how a non-convex, no fit polygon algorithm can (and should) produce better quality solutions. We demonstrate the approach on two test problems that the authors have used in previous work. The new algorithm does, however, have an increased computational complexity. We tackle this by presenting an algorithm that allows the non-convex, no fit polygon to be approximated. This means that much more of the search space can be considered and computational results show that better quality solutions can be achieved, in less time than when using the full no fit polygon algorithm.


pdf

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doi

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URL

The URL for additional information is http://www.springer.com/engineering/mechanical+eng/book/978-1-85233-605-9

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Bibtex

@INPROCEEDINGS{bk2002a, author = {E.K. Burke and G. Kendall},
title = {A New Approach to Packing Non-Convex Polygons Using the No Fit Polygon and Meta-Heuristic and Evolutionary Algorithms},
booktitle = {Proceedings of the 5th International Conference on Adapative Computing in Design and Manufacture (ACDM 2002)},
year = {2002},
editor = {C.I. Parmee},
pages = {193--204},
address = {University of Exeter, UK, 16-18 April},
publisher = {Springer-Verlag},
abstract = {Earlier work by the authors has presented a method for packing convex polygons, using a construction known as the no fit polygon. Although the method was shown to be successful, it could only deal with convex polygons. This paper addresses the issue by showing how a non-convex, no fit polygon algorithm can (and should) produce better quality solutions. We demonstrate the approach on two test problems that the authors have used in previous work. The new algorithm does, however, have an increased computational complexity. We tackle this by presenting an algorithm that allows the non-convex, no fit polygon to be approximated. This means that much more of the search space can be considered and computational results show that better quality solutions can be achieved, in less time than when using the full no fit polygon algorithm.},
comment = {ISBN 1-85233-605-6},
keywords = {No Fit Polygon, NFP, Minkowski Sum, packing, nesting, heuristics, evolutinary algorithms, genetic algorithm, tabu search, simulated annealing, ant algorithm, memetic algorithm},
timestamp = {2007.03.29},
url = {http://www.springer.com/engineering/mechanical+eng/book/978-1-85233-605-9},
webpdf = {http://www.graham-kendall.com/papers/bk2002a.pdf} }