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

Help solve Santa's logistics problems
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Help solve Santa's logistics problems
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Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Vehicle Routing Datasets

Publication(s)

An ant algorithm hyperheuristic for the project presentation scheduling problem
http://bit.ly/gbUor9
Memory Length in Hyper-heuristics: An Empirical Study
http://bit.ly/eXAo7v
Iterated Local Search vs. Hyper-heuristics: Towards General-Purpose Search Algorithms
http://bit.ly/gWFcuw
The Importance of Look-Ahead Depth in Evolutionary Checkers
http://bit.ly/1bh6fGH

Graham Kendall: Details of Requested Publication


Citation

Angiz, M. L. Z; Nawawi, M. K. M; Khalid, R; Mustafa, A; Emrouznejad, A; John, R and Kendall, G Evaluating decision-making units under uncertainty using fuzzy multi-objective nonlinear programming. INFOR: Information Systems and Operational Research, 55 (1): 1-15, 2017.


Abstract

This paper proposes a new method to evaluate decision-making units (DMUs) under uncertainty using fuzzy data envelopment analysis (DEA). In the proposed multi-objective nonlinear programming methodology, both the objective functions and the constraints are considered fuzzy. This model is comprehensive in dealing with uncertainty, in the sense that coefficients of the decision variables in the objective functions and in the constraints, as well as the DMUs under assessment, are assumed to be fuzzy numbers with triangular membership functions. A comparison between the current fuzzy DEA models and the proposed method is illustrated by a numerical example.


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doi

The doi for this publication is 10.1080/03155986.2016.1240944 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.

2015 (0.095), 2014 (0.171), 2013 (0.410), 2012 (0.395), 2011 (0.295), 2010 (0.318), 2009 (0.738), 2008 (0.324), 2007 (0.275), 2006 (0.095), 2005 (0.195), 2004 (0.095), 2003 (0.125), 2002 (0.224), 2001 (0.319), 2000 (0.133)

URL

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Bibtex

@ARTICLE{ankmejk2017, author = {M. L. Z. Angiz and M. K. M. Nawawi and R. Khalid and A. Mustafa and A. Emrouznejad and R. John and G. Kendall},
title = {Evaluating decision-making units under uncertainty using fuzzy multi-objective nonlinear programming},
journal = {INFOR: Information Systems and Operational Research},
year = {2017},
volume = {55},
pages = {1--15},
number = {1},
abstract = {This paper proposes a new method to evaluate decision-making units (DMUs) under uncertainty using fuzzy data envelopment analysis (DEA). In the proposed multi-objective nonlinear programming methodology, both the objective functions and the constraints are considered fuzzy. This model is comprehensive in dealing with uncertainty, in the sense that coefficients of the decision variables in the objective functions and in the constraints, as well as the DMUs under assessment, are assumed to be fuzzy numbers with triangular membership functions. A comparison between the current fuzzy DEA models and the proposed method is illustrated by a numerical example.},
doi = {10.1080/03155986.2016.1240944},
eprint = { http://dx.doi.org/10.1080/03155986.2016.1240944 },
owner = {Graham},
timestamp = {2016.12.18} }