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

How are football fixtures worked out?
http://bit.ly/1z0oTAH
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

We should be just a number, and we should embrace it: Extend the use of ORCID?

Publication(s)

Collective Behavior and Kin Selection in Evolutionary IPD
http://bit.ly/if34nF
A Squeaky Wheel Optimisation Methodology for Two Dimensional Strip Packing
http://bit.ly/ibMskY
A Parallel Branch-and-Bound Approach to the Rectangular Guillotine Strip Cutting Problem
http://bit.ly/fVXMjm
Good Laboratory Practice for optimization research
http://bit.ly/1TFr8zD

Graham Kendall: Details of Requested Publication


Citation

Bai, R and Kendall, G Chapter 4: An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-Heuristic. In Metaheuristics: Progress as Real Problem Solvers (Operations Research/Computer Science Interfaces Series, Volume 32, Part III), pages 87-108, Springer, 2005.

A previous version of this paper was published in the 2003 Metaheuristics International Conference (MIC) proceedings


Abstract

This paper formulates the shelf space allocation problem as a non-linear function of the product net profit and store-inventory. We show that this model is an extension of multi-knapsack problem, which is itself an NP-hard problem. A two-stage relaxation is carried out to get an upper bound of the model. A simulated annealing based hyper-heuristic algorithm is proposed to solve several problem instances with different problem sizes and space ratios. The results show that the simulated annealing hyper-heuristic significantly outperforms two conventional simulated annealing algorithms and other hyper-heuristics for all problem instances. The experimental results show that our approach is a robust and efficient approach for the shelf space allocation problem.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/0-387-25383-1_4 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



URL

The URL for additional information is http://dx.doi.org/10.1007/b107306

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

@INBOOK{bk2005, chapter = {Metaheuristics: Progress as Real Problem Solvers (Operations Research/Computer Science Interfaces Series, Volume 32, Part III)},
pages = {87--108},
title = {Chapter 4: An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-Heuristic},
publisher = {Springer},
year = {2005},
editor = {T. Ibaraki and K. Nonobe and M. Yagiura},
author = {R. Bai and G. Kendall},
note = {A previous version of this paper was published in the 2003 Metaheuristics International Conference (MIC) proceedings},
abstract = {This paper formulates the shelf space allocation problem as a non-linear function of the product net profit and store-inventory. We show that this model is an extension of multi-knapsack problem, which is itself an NP-hard problem. A two-stage relaxation is carried out to get an upper bound of the model. A simulated annealing based hyper-heuristic algorithm is proposed to solve several problem instances with different problem sizes and space ratios. The results show that the simulated annealing hyper-heuristic significantly outperforms two conventional simulated annealing algorithms and other hyper-heuristics for all problem instances. The experimental results show that our approach is a robust and efficient approach for the shelf space allocation problem.},
booktitle = {Meta-heuristics: Progress as Real Problem Solvers, Selected Papers from the 5th Metaheuristics International Conference (MIC 2003)},
comment = {ISBN 0-387-25382-3},
doi = {10.1007/0-387-25383-1_4},
keywords = {shelf packing, planograms, simulated annealing, heuristics, hyper-heuristics, hyperheuristics},
timestamp = {2007.03.29},
url = {http://dx.doi.org/10.1007/b107306},
webpdf = {http://www.graham-kendall.com/papers/bk2005.pdf} }