Graham Kendall
Various Images

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 blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Claude Shannon, Edward Thorp, Roulette and Blackjack

Publication(s)

The Application of a Dendritic Cell Algorithm to a Robotic Classifier
http://bit.ly/hTMQ5K
Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives
http://bit.ly/er0RSP
Investigation of an Adaptive Cribbage Player
http://bit.ly/eVPybN
The effect of memory size on the evolutionary stability of strategies in iterated prisoner's dilemma
http://bit.ly/1HXMzXa

Graham Kendall: Details of Requested Publication


Citation

Bai, R and Kendall, G An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-heuristics. In Proceedings of The Fifth Metaheuristics International Conference (MIC 2003), pages 03-1-03-7, Kyoto International Conference Hall, Kyoto, Japan, 2003.

An extended version of this paper was published in post-conference, selected papers book.


Abstract

In this paper, we use a more practical shelf space allocation model to generate automatic planograms. Several hyper-heuristic approaches are applied to solve this problem. As an extension of the multi-knapsack problem, the planogram problem is difficult to solve. We provide a set of simple low-level heuristics which have been shown to be very successful in bin packing and knapsack problem. The hyper-heuristic operates on the low-level heuristics and adapts its choice decision to the current search space. The experimental results show that the hyper-heuristics used in this paper produced much better results than a greedy heuristic and in the four hyper-heuristics, our simulated annealing based hyper-heuristic produced the best results in most cases. One of the problems with simulated annealing is defining a suitable cooling schedule. Although our schedule appears to be effective, we plan to investigate tuning the cooling schedule to the problem in hand. We will also investigate on different problems in an attempt to demonstrate the generalisation of this approach.


pdf

You can download the pdf of this publication from here


doi

This publication does not have a doi, so we cannot provide a link to the original source

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

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

@INPROCEEDINGS{bk2003, author = {R. Bai and G. Kendall},
title = {An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-heuristics},
booktitle = {Proceedings of The Fifth Metaheuristics International Conference (MIC 2003)},
year = {2003},
pages = {03-1--03-7},
address = {Kyoto International Conference Hall, Kyoto, Japan},
month = {23-25 August},
note = {An extended version of this paper was published in post-conference, selected papers book.},
abstract = {In this paper, we use a more practical shelf space allocation model to generate automatic planograms. Several hyper-heuristic approaches are applied to solve this problem. As an extension of the multi-knapsack problem, the planogram problem is difficult to solve. We provide a set of simple low-level heuristics which have been shown to be very successful in bin packing and knapsack problem. The hyper-heuristic operates on the low-level heuristics and adapts its choice decision to the current search space. The experimental results show that the hyper-heuristics used in this paper produced much better results than a greedy heuristic and in the four hyper-heuristics, our simulated annealing based hyper-heuristic produced the best results in most cases. One of the problems with simulated annealing is defining a suitable cooling schedule. Although our schedule appears to be effective, we plan to investigate tuning the cooling schedule to the problem in hand. We will also investigate on different problems in an attempt to demonstrate the generalisation of this approach.},
keywords = {shelf packing, planograms, simulated annealing, heuristics, hyper-heuristics, hyperheuristics},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/bk2003.pdf} }