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 am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC
I have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

General Algebraic Modeling System (GAMS)

Publication(s)

A scheme for determining vehicle routes based on Arc-based service network design
http://bit.ly/2iaUTxA
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
http://bit.ly/gJdaAs
Throughput Maximization of Queueing Networks with Simultaneous Minimization of Service Rates and Buffers
http://bit.ly/1cJuWLM
Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
http://bit.ly/1ib50Nd

Graham Kendall: Details of Requested Publication


Citation

Bai, R; Blazewicz, J; Burke, E. K; Kendall, G and McCollum, B A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support. 4OR - A Quarterly Journal of Operations Research, 10 (1): 43-66, 2012.


Abstract

Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/s10288-011-0182-8 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 (1.000), 2013 (0.918), 2012 (0.730), 2011 (0.323), 2010 (0.690), 2009 (0.750)

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{bbbkm2099, author = {R. Bai and J. Blazewicz and E. K. Burke and G. Kendall and B. McCollum},
title = {A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support},
journal = {4OR - A Quarterly Journal of Operations Research},
year = {2012},
volume = {10},
pages = {43--66},
number = {1},
abstract = {Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.},
doi = {10.1007/s10288-011-0182-8},
issn = {1619-4500},
keywords = {hyper-heuristic, hyperheuristic, simulated annealing, bin packing, packing, timetabling, course timetabling},
owner = {gxk},
timestamp = {2011.07.07},
webpdf = {http://www.graham-kendall.com/papers/bbbkm2012.pdf} }