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

If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg
A Conversation article celebrating Pi
http://bit.ly/1DXuXbV

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Operational or Operations Research?

Publication(s)

Introducing Individual and Social Learning Into Evolutionary Checkers
http://bit.ly/1a2YwLx
A Game Theoretic Approach for Taxi Scheduling Problem with Street Hailing
http://bit.ly/1hBsesZ
Evaluating the performance of a EuroDivisia index using artificial intelligence techniques
http://bit.ly/gaswDm
Evolving Tiles for Automated Self-Assembly Design
http://bit.ly/dInbHL

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K and Kendall, G Chapter 1: Introduction. In Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pages 5-18, Springer, 2005.

This is the introductory chapter that we wrote for this book


Abstract

The investigation of search and optimization technologies underpins the development of decision support systems in a wide variety of applications across industry, commerce, science and government. There is a significant level of diversity among optimization and computational search applications. This can be evidenced by noting that a very small selection of such applications includes transport scheduling, bioinformatics optimization, personnel rostering, medical decision support and timetabling. More examples of relevant applications can be seen in Pardalos and Resende (2002), Leung (2004) and Dell’Amico et al. (1997). The exploration of decision support methodologies is a crucially important research area. The potential impact of more effective and more efficient decision support methodologies is enormous and can be illustrated by considering just a few of the potential benefits: more efficient production scheduling can lead to significant financial savings; higher quality personnel rosters lead to a more contented workforce; more efficient healthcare scheduling will lead to faster treatment (which could save lives); more effective cutting/packing systems can reduce waste; better delivery schedules can reduce fuel emissions. This research area has received significant attention from the scientific community across many different academic disciplines. Indeed, a quick look at any selection of key papers which have impacted upon search, optimization and decision support will demonstrate that the authors have been based in a number of different departments including Computer Science, Mathematics, Engineering, Business, Management, and others. It is clearly the case that the investigation and development of decision support methodologies is inherently multi-disciplinary. It lies firmly at the interface of Operational Research and Artificial Intelligence (among other disciplines). However, not only is the underlying methodology inherently inter-disciplinary but the broad range of application areas also cuts across many disciplines and industries. We firmly believe that scientific progress in this crucially important area will be made far more effectively and far more quickly by adopting a broad and inclusive multi-disciplinary approach to the international scientific agenda in this field. The way forward is inter-disciplinary. This observation provides one of the key motivations for this book. The book is aimed primarily at first-year postgraduate students and final-year undergraduate students. However, we have also aimed it at practitioners and at the experienced researcher who wants a brief introduction to the broad range of decision support methodologies that is available in the scientific literature. In our experience, the key texts for these methodologies lie across a variety of volumes. This reflects the broad range of disciplines that are represented here. We wanted to bring together a series of entry-level tutorials, written by worldleading scientists from across the disciplinary range, in one single volume.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/0-387-28356-0_1 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://www.springerlink.com/content/978-0-387-23460-1

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{bk2005a, chapter = {Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques},
pages = {5--18},
title = {Chapter 1: Introduction},
publisher = {Springer},
year = {2005},
editor = {E.K. Burke and G. Kendall},
author = {E.K. Burke and G. Kendall},
note = {This is the introductory chapter that we wrote for this book},
abstract = {The investigation of search and optimization technologies underpins the development of decision support systems in a wide variety of applications across industry, commerce, science and government. There is a significant level of diversity among optimization and computational search applications. This can be evidenced by noting that a very small selection of such applications includes transport scheduling, bioinformatics optimization, personnel rostering, medical decision support and timetabling. More examples of relevant applications can be seen in Pardalos and Resende (2002), Leung (2004) and Dell’Amico et al. (1997). The exploration of decision support methodologies is a crucially important research area. The potential impact of more effective and more efficient decision support methodologies is enormous and can be illustrated by considering just a few of the potential benefits: more efficient production scheduling can lead to significant financial savings; higher quality personnel rosters lead to a more contented workforce; more efficient healthcare scheduling will lead to faster treatment (which could save lives); more effective cutting/packing systems can reduce waste; better delivery schedules can reduce fuel emissions. This research area has received significant attention from the scientific community across many different academic disciplines. Indeed, a quick look at any selection of key papers which have impacted upon search, optimization and decision support will demonstrate that the authors have been based in a number of different departments including Computer Science, Mathematics, Engineering, Business, Management, and others. It is clearly the case that the investigation and development of decision support methodologies is inherently multi-disciplinary. It lies firmly at the interface of Operational Research and Artificial Intelligence (among other disciplines). However, not only is the underlying methodology inherently inter-disciplinary but the broad range of application areas also cuts across many disciplines and industries. We firmly believe that scientific progress in this crucially important area will be made far more effectively and far more quickly by adopting a broad and inclusive multi-disciplinary approach to the international scientific agenda in this field. The way forward is inter-disciplinary. This observation provides one of the key motivations for this book. The book is aimed primarily at first-year postgraduate students and final-year undergraduate students. However, we have also aimed it at practitioners and at the experienced researcher who wants a brief introduction to the broad range of decision support methodologies that is available in the scientific literature. In our experience, the key texts for these methodologies lie across a variety of volumes. This reflects the broad range of disciplines that are represented here. We wanted to bring together a series of entry-level tutorials, written by worldleading scientists from across the disciplinary range, in one single volume.},
date-modified = {2007-01-16 16:10:05 +0000},
doi = {10.1007/0-387-28356-0_1},
entrytype = {book},
keywords = {optimization, optimisation, search methodologies},
url = {http://www.springerlink.com/content/978-0-387-23460-1},
webpdf = {http://www.graham-kendall.com/papers/bk2005a.pdf} }