Graham Kendall
Various Images

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

How are football fixtures worked out?
http://bit.ly/1z0oTAH
A Conversation article celebrating Pi
http://bit.ly/1DXuXbV

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Claude Shannon, Edward Thorp, Roulette and Blackjack

Publication(s)

A novel approach to independent taxi scheduling problem based on stable matching
http://bit.ly/1A3GUfR
Evolutionary Strategies vs. Neural Networks: An Inflation Forecasting Experiment
http://bit.ly/h6J8Xv
Detecting change and dealing with uncertainty in imperfect evolutionary environments
http://bit.ly/1zSwgTX
Evaluating the performance of a EuroDivisia index using artificial intelligence techniques
http://bit.ly/gaswDm

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

You can download the pdf of this publication from here


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} }