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
Does AI have a place in the board room?
http://bit.ly/1DXreuW

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

How can academics use Twitter effectively?

Publication(s)

Multi-drop container loading using a multi-objective evolutionary algorithm
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Studying the Effect that a Linear Transformation has on the Time-Series Prediction Ability of an Evolutionary Neural Network
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Exploring hyper-heuristic methodologies with genetic programming
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On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
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Graham Kendall: Details of Requested Publication


Citation

Kirke, T; While, L and Kendall, G Multi-drop container loading using a multi-objective evolutionary algorithm. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), pages 165-172, 2013.


Abstract

We describe a new algorithm MOCL (multiobjective container loading) for the multi-drop single container loading problem. MOCL extends the recent biased random-key genetic algorithm due to Goncalves & Resende to the multidrop problem by enhancing its genetic representation, its fitness calculations, and its initialisation procedure. MOCL optimises packings both for volume utilisation and for the accessibility of the packed objects, by minimising the number of objects that block each other relative to a pre-defined unpacking schedule. MOCL derives solutions that are competitive with state-of-the-art algorithms for the single-drop case (where blocking is irrelevant), plus it derives solutions for 2-50 drops that give very good utilisation with no or very little blocking. This flexibility makes MOCL a useful tool for a variety of 3D packing applications.


pdf

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doi

The doi for this publication is 10.1109/CEC.2013.6557567 You can link directly to the original paper, via the doi, from here

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URL

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Bibtex

@INPROCEEDINGS{kwk2013, author = {T. Kirke and L. While and G. Kendall},
title = {Multi-drop container loading using a multi-objective evolutionary algorithm},
booktitle = {Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)},
year = {2013},
pages = {165--172},
abstract = {We describe a new algorithm MOCL (multiobjective container loading) for the multi-drop single container loading problem. MOCL extends the recent biased random-key genetic algorithm due to Goncalves & Resende to the multidrop problem by enhancing its genetic representation, its fitness calculations, and its initialisation procedure. MOCL optimises packings both for volume utilisation and for the accessibility of the packed objects, by minimising the number of objects that block each other relative to a pre-defined unpacking schedule. MOCL derives solutions that are competitive with state-of-the-art algorithms for the single-drop case (where blocking is irrelevant), plus it derives solutions for 2-50 drops that give very good utilisation with no or very little blocking. This flexibility makes MOCL a useful tool for a variety of 3D packing applications.},
doi = {10.1109/CEC.2013.6557567},
keywords = {packing, cutting, nesting},
owner = {Graham},
timestamp = {2013.08.17},
webpdf = {http://www.graham-kendall.com/papers/kwk2013.pdf} }