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

The hunt for MH370
http://bit.ly/1DXRLbu
What do we spend so much in supermarkets?
http://bit.ly/1yW6If7

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

The 2009 IEEE Symposium on Computational Intelligence and Games: Report

Publication(s)

The Limitations of Frequency Analysis for Dendritic Cell Population Modelling
http://bit.ly/h5VYIQ
Effect of Look-Ahead Depth in Evolutionary Checkers
http://bit.ly/1PtXSaX
Alternative hyper-heuristic strategies for multi-method global optimization
http://bit.ly/g1xcMp
Fuzzy job shop scheduling with lot-sizing
http://bit.ly/gnd5ds

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, 2nd ed., pages 1-17, Springer, 2014.

This is the introductory chapter that we wrote for this book


Abstract

Search and optimization technologies underpin 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 small selection of applications includes transport scheduling, bioinformatics optimization, personnel rostering, medical decision support and timetabling. Later in this introductionwe present some recent survey papers for some of these areas and more examples of relevant applications are available in Pardalos and Resende (2002) and Leung (2004). The potential impact of more effective and 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; Efficient healthcare scheduling will lead to faster treatment (potentially saving 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. Looking at any selection of key papers which have impacted upon search, optimization and decision supportwill reveal that the authors are based in a number of different departments including Computer Science, Mathematics, Engineering, Business and Management (among 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, Computer Science 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. This observation provides one of the key motivations for this book, which 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 are in the literature. In our experience, the key texts for these methodologies lie across a variety of volumes. This reflects the wide range of disciplines that are represented here. We wanted to bring together a series of entry-level tutorials, written by world-leading 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 10.1007/978-1-4614-6940-7_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://link.springer.com/chapter/10.1007/978-1-4614-6940-7_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{bk2014, chapter = {Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2nd ed.},
pages = {1--17},
title = {Chapter 1: Introduction},
publisher = {Springer},
year = {2014},
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 = {Search and optimization technologies underpin 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 small selection of applications includes transport scheduling, bioinformatics optimization, personnel rostering, medical decision support and timetabling. Later in this introductionwe present some recent survey papers for some of these areas and more examples of relevant applications are available in Pardalos and Resende (2002) and Leung (2004). The potential impact of more effective and 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; Efficient healthcare scheduling will lead to faster treatment (potentially saving 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. Looking at any selection of key papers which have impacted upon search, optimization and decision supportwill reveal that the authors are based in a number of different departments including Computer Science, Mathematics, Engineering, Business and Management (among 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, Computer Science 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. This observation provides one of the key motivations for this book, which 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 are in the literature. In our experience, the key texts for these methodologies lie across a variety of volumes. This reflects the wide range of disciplines that are represented here. We wanted to bring together a series of entry-level tutorials, written by world-leading scientists from across the disciplinary range, in one single volume.},
date-modified = {2007-01-16 16:10:05 +0000},
doi = {10.1007/978-1-4614-6940-7_1},
entrytype = {book},
keywords = {optimization, optimisation, search methodologies},
owner = {Graham},
timestamp = {2015.01.03},
url = {http://link.springer.com/chapter/10.1007/978-1-4614-6940-7_1},
webpdf = {http://www.graham-kendall.com/papers/bk2014.pdf} }