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 the chair of the MISTA (Multidisciplinary International Conference on Scheduling: Theory and Applications)
http://bit.ly/hvZIaN
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Football fixture forecasting. Are you any good?

Publication(s)

A Tabu-Search Hyperheuristic for Timetabling and Rostering
http://bit.ly/i7823Y
Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives
http://bit.ly/1hWKuwV
Chapter 1: Introduction
http://bit.ly/1goQv51
The Limitations of Frequency Analysis for Dendritic Cell Population Modelling
http://bit.ly/h5VYIQ

Graham Kendall: Details of Requested Publication


Citation

Ayob, M and Kendall, G A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine. In Proceedings of the International Conference on Intelligent Technologies (InTech'03), pages 132-141, Chiang Mai, Thailand, 2003.


Abstract

In this paper we introduce a Monte Carlo based hyper-heuristic. The Monte Carlo hyper-heuristic manages a set of low level heuristics (in this case just simple 2-opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that may be impossible to create by the other low level heuristics. On each iteration, the Monte Carlo hyper heuristic randomly calls a low level heuristic. The new solution returned by the low level heuristic will be accepted based on the Monte Carlo acceptance criteria. The Monte Carlo acceptance criteria always accept an improved solution. Worse solutions will be accepted with a certain probability, which decreases with worse solutions, in order to escape local minima. We develop three hyper-heuristics based on a Monte Carlo method, these being Linear Monte Carlo Exponential Monte Carlo and Exponential Monte Carlo with counter. We also investigate four other hyperheuristics to examine their performance and for comparative purposes. To demonstrate our approach we employ these hyper-heuristics to optimise component placement sequencing in order to improve the efficiency of the multi head placement machine. Experimental results show that the Exponential Monte Carlo hyperheuristic is superior to the other hyper-heuristics and is superior to a choice function hyper-heuristic reported in earlier work.


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{ak2003, author = {M. Ayob and G. Kendall},
title = {A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine},
booktitle = {Proceedings of the International Conference on Intelligent Technologies (InTech'03)},
year = {2003},
pages = {132--141},
address = {Chiang Mai, Thailand},
month = {December 17-19},
abstract = {In this paper we introduce a Monte Carlo based hyper-heuristic. The Monte Carlo hyper-heuristic manages a set of low level heuristics (in this case just simple 2-opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that may be impossible to create by the other low level heuristics. On each iteration, the Monte Carlo hyper heuristic randomly calls a low level heuristic. The new solution returned by the low level heuristic will be accepted based on the Monte Carlo acceptance criteria. The Monte Carlo acceptance criteria always accept an improved solution. Worse solutions will be accepted with a certain probability, which decreases with worse solutions, in order to escape local minima. We develop three hyper-heuristics based on a Monte Carlo method, these being Linear Monte Carlo Exponential Monte Carlo and Exponential Monte Carlo with counter. We also investigate four other hyperheuristics to examine their performance and for comparative purposes. To demonstrate our approach we employ these hyper-heuristics to optimise component placement sequencing in order to improve the efficiency of the multi head placement machine. Experimental results show that the Exponential Monte Carlo hyperheuristic is superior to the other hyper-heuristics and is superior to a choice function hyper-heuristic reported in earlier work.},
keywords = {Intelligent Search, Heuristic, Printed Circuit Board Assembly, Optimisation, hyper-heuristic, hyperheuristic, component placement},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/ak2003.pdf} }