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
What do we spend so much in supermarkets?
http://bit.ly/1yW6If7

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

PATAT 2012 Conference

Publication(s)

Maximising Stadium Attendance: A Case Study of Malaysian Football
http://bit.ly/eFTURu
Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
http://bit.ly/19D3Bqv
A Tabu Search hyper-heuristic strategy for t-way test suite generation
http://bit.ly/1W2GjGR
Real-time Scheduling for Multi Headed Placement Machine
http://bit.ly/eVWnGn

Graham Kendall: Details of Requested Publication


Citation

Bai, R.; Burke, E.K.; Kendall, G. and van Woensel, T. A New Model and a Hyper-heuristic Approach for Two-dimensional Shelf Space Allocation. 4OR - A Quarterly Journal of Operations Research, 11 (1): 31-55, 2013.


Abstract

In this paper, we propose a two-dimensional shelf space allocation model. The second dimension stems from the height of the shelf. This results in an integer nonlinear programming model with a complex form of objective function.We propose a multiple neighborhood approach which is a hybridization of a simulated annealing algorithmwith a hyper-heuristic learning mechanism. Experiments based on empirical data from both real-world and artificial instances show that the shelf space utilization and the resulting sales can be greatly improvedwhen compared with a gradient method. Sensitivity analysis on the input parameters and the shelf space show the benefits of the proposed algorithm both in sales and in robustness.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/s10288-012-0211-2 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{bbkw2013, author = {Bai, R. and Burke, E.K. and Kendall, G. and van Woensel, T.},
title = {A New Model and a Hyper-heuristic Approach for Two-dimensional Shelf Space Allocation},
journal = {4OR - A Quarterly Journal of Operations Research},
year = {2013},
volume = {11},
pages = {31--55},
number = {1},
abstract = {In this paper, we propose a two-dimensional shelf space allocation model. The second dimension stems from the height of the shelf. This results in an integer nonlinear programming model with a complex form of objective function.We propose a multiple neighborhood approach which is a hybridization of a simulated annealing algorithmwith a hyper-heuristic learning mechanism. Experiments based on empirical data from both real-world and artificial instances show that the shelf space utilization and the resulting sales can be greatly improvedwhen compared with a gradient method. Sensitivity analysis on the input parameters and the shelf space show the benefits of the proposed algorithm both in sales and in robustness.},
doi = {10.1007/s10288-012-0211-2},
issn = {1619-4500},
keywords = {hyper-heuristic, hyperheuristic, shelf allocation},
owner = {gxk},
timestamp = {2011.07.07},
webpdf = {http://www.graham-kendall.com/papers/bbkw2013.pdf} }