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 a number of papers on Cutting and Packing
http://bit.ly/dQPw7T
Does AI have a place in the board room?
http://bit.ly/1DXreuW

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

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Welcome

Publication(s)

Sports Scheduling: Minimizing Travel for English Football Supporters
http://bit.ly/19JwmYd
Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems
http://bit.ly/1L6OJ8g
Evolving Reusable 3D Packing Heuristics with Genetic Programming.
http://bit.ly/e75y7F
Youth Sports Leagues Scheduling
http://bit.ly/f1i7SE

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

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{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} }