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

Help solve Santa's logistics problems
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How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

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General Algebraic Modeling System (GAMS)

Publication(s)

Automated code generation by local search
http://bit.ly/1dAz69a
An Integration of BP-Pool and Social Learning in the Opening of Go
http://bit.ly/1PtX9qq
An Ant Based Hyper-heuristic for the Travelling Tournament Problem
http://bit.ly/gPYAJl
An efficient and robust approach to generate high quality solutions for the Traveling Tournament Problem.
http://bit.ly/fCqNU6

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Hussin, N. M. A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology. In Practice and Theory of Automated Timetabling V, pages 270-293, Springer, Lecture Notes in Computer Science 3616, 2005.

A previous version of this paper was published in the 2004 PATAT proceedings


Abstract

In this paper we introduce an examination timetabling problem from the MARA University of Technology (UiTM). UiTM is the largest university in Malaysia. It has 13 branch campuses and offers 144 programmes, delivered by 18 faculties. This dataset differs from the others reported in the literature due to weekend constraints that have to be observed. We present their examination timetabling problem with respect to its size, complexity and constraints. We analyse their real-world data, and produce solutions utilising a tabu-search-based hyper-heuristic. Since this is a new dataset, and no solutions have been published in the literature, we can only compare our results with an existing manual solution. We find that our solution is at least 80% better with respect to proximity cost. We also compare our approach against a benchmark dataset and show that our method is able to produce good quality results.


pdf

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doi

The doi for this publication is 10.1007/11593577_16 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

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Bibtex

@INPROCEEDINGS{km2005, author = {G. Kendall and N. {Mohd Hussin}},
title = {A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology},
booktitle = {Practice and Theory of Automated Timetabling V},
year = {2005},
editor = {E. Burke and M. Trick},
volume = {3616},
series = {Lecture Notes in Computer Science},
pages = {270--293},
publisher = {Springer},
note = {A previous version of this paper was published in the 2004 PATAT proceedings},
abstract = {In this paper we introduce an examination timetabling problem from the MARA University of Technology (UiTM). UiTM is the largest university in Malaysia. It has 13 branch campuses and offers 144 programmes, delivered by 18 faculties. This dataset differs from the others reported in the literature due to weekend constraints that have to be observed. We present their examination timetabling problem with respect to its size, complexity and constraints. We analyse their real-world data, and produce solutions utilising a tabu-search-based hyper-heuristic. Since this is a new dataset, and no solutions have been published in the literature, we can only compare our results with an existing manual solution. We find that our solution is at least 80% better with respect to proximity cost. We also compare our approach against a benchmark dataset and show that our method is able to produce good quality results.},
doi = {10.1007/11593577_16},
keywords = {examination timetabling, timetabling, tabu search, meta-heuristics, metaheuristics, hyper-heuristics, hyperheuristics},
owner = {gxk},
timestamp = {2011.01.01},
webpdf = {http://www.graham-kendall.com/papers/km2005.pdf} }