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
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

The future of scientific publishing: let’s make sure it’s fair as well as transparent

Publication(s)

Collective Behavior and Kin Selection in Evolutionary IPD
http://bit.ly/if34nF
Finite iterated prisoner's dilemma revisited: belief change and end-game effect
http://bit.ly/hathrT
Searching the Hyper-heuristic Design Space
http://bit.ly/1sD4RoY
A Study of Simulated Annealing Hyperheuristics
http://bit.ly/ifevCO

Graham Kendall: Details of Requested Publication


Citation

Cruz, F. R. B; Kendall, G; While, L; Duarte, A. R and Brito, N. L. C Throughput Maximization of Queueing Networks with Simultaneous Minimization of Service Rates and Buffers. Mathematical Problems in Engineering, 2012.


Abstract

The throughput of an acyclic, general-service time queueing network was optimized, and the total number of buffers and the overall service rate was reduced. To satisfy these conflicting objectives, a multiobjective genetic algorithm was developed and employed. Thus, our method produced a set of efficient solutions for more than one objective in the objective function. A comprehensive set of computational experiments was conducted to determine the efficacy and efficiency of the proposed approach. Interesting insights obtained from the analysis of a complex network may assist practitioners in planning general-service queueing networks.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1155/2012/692593 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.

2013 (1.082), 2012 (1.383), 2011 (0.777), 2010 (0.689), 2009 (0.553), 2008 (0.545), 2007 (0.376), 2006 (0.452), 2005 (0.237), 2004 (0.204), 2003 (0.157), 2002 (0.238), 2001 (0.192), 2000 (0.196)

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{ckwdb2012, author = {F. R. B. Cruz and G. Kendall and L. While and A. R. Duarte and N. L. C. Brito},
title = {Throughput Maximization of Queueing Networks with Simultaneous Minimization of Service Rates and Buffers},
journal = {Mathematical Problems in Engineering},
year = {2012},
abstract = {The throughput of an acyclic, general-service time queueing network was optimized, and the total number of buffers and the overall service rate was reduced. To satisfy these conflicting objectives, a multiobjective genetic algorithm was developed and employed. Thus, our method produced a set of efficient solutions for more than one objective in the objective function. A comprehensive set of computational experiments was conducted to determine the efficacy and efficiency of the proposed approach. Interesting insights obtained from the analysis of a complex network may assist practitioners in planning general-service queueing networks.},
doi = {10.1155/2012/692593},
issn = {1024-123X},
keywords = {queuing, multi-objective, networks},
owner = {est},
timestamp = {2010.02.22},
webpdf = {http://www.graham-kendall.com/papers/ckwdb2012.pdf} }