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
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Football Prediction: A decision to be made

Publication(s)

A Tabu Search hyper-heuristic strategy for t-way test suite generation
http://bit.ly/1W2GjGR
A path-oriented encoding evolutionary algorithm for network coding resource minimization
http://bit.ly/1t0cd2K
A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem
http://bit.ly/fsNXXk
An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics
http://bit.ly/dFh029

Graham Kendall: Details of Requested Publication


Citation

Yang, S; Wang, D; Chai, T and Kendall, G An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13 (1): 17-38, 2010.


Abstract

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.


pdf

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doi

The doi for this publication is 10.1007/s10951-009-0106-z 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 (1.028), 2013 (1.186), 2012 (0.941), 2011 (1.051), 2010 (1.297), 2009 (1.265), 2008 (1.050), 2007 (1.000), 2006 (0.811), 2005 (0.852), 2004 (0.660), 2003 (0.702)

URL

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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{ywck2010, author = {S. Yang and D. Wang and T. Chai and G. Kendall},
title = {An improved constraint satisfaction adaptive neural network for job-shop scheduling},
journal = {Journal of Scheduling},
year = {2010},
volume = {13},
pages = {17--38},
number = {1},
abstract = {This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.},
doi = {10.1007/s10951-009-0106-z},
issn = {1094-6136},
keywords = {Job-shop scheduling, Constraint satisfaction, adaptive neural network, Heuristics, Active schedule, Non-delay schedule, Priority rule, Computational complexity},
owner = {est},
timestamp = {2010.02.22},
webpdf = {http://www.graham-kendall.com/papers/ywck2010.pdf} }