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

I have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

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Publication(s)

Hyper-Heuristics: An Emerging Direction in Modern Search Technology
http://bit.ly/1goVsLe
Iterated Local Search Using an Add and Delete Hyper-heuristic for University Course Timetabling
http://bit.ly/1mlRZo4
A survey of surface mount device placement machine optimisation: Machine classification
http://bit.ly/hvQjOV
The Cross-domain Heuristic Search Challenge - An International Research Competition
http://bit.ly/1a2VfMs

Graham Kendall: Details of Requested Publication


Citation

Cowling, P; Kendall, G and Soubeiga, E A Parameter-Free Hyperheuristic for Scheduling a Sales Summit. In Proceedings of the 4th Metahuristics International Conference (MIC 2001), pages 127-131, 16-20 July 2001, Porto Portugal, 2001.

There is not an abstract for this paper, so we have used the introduction as the abstract.


Abstract

Personnel scheduling involves scheduling people to timeslots and possibly locations. This problem remains the subject of much research interest with a survey in every decade since the 1970ís. Several models including Constraint Satisfaction Problem (CSP), mathematical programming (linear programming, 0-1 integer programming etc.), and different solution methods (exact or heuristic based) have been proposed to tackle this problem. Also several applications have been reported in the literature including applications for hospital personnel, educational institution personnel and others. However the heuristic methods developed for a particular personnel scheduling problem may not perform well if applied to a different problem. Heuristic and metaheuristic approaches tend to be knowledge rich, requiring substantial expertise in both the problem domain and appropriate heuristic techniques. It is in this context that we proposed a hyperheuristic approach as a heuristic that operates at a higher level of abstraction than current metaheuristic approaches. The hyperheuristic has no problem-specific knowledge. It manages a set of simple, knowledge-poor, low-level heuristics. At any given time the hyperheuristic must choose which low-level heuristic to call. The hyperheuristic interacts with the low-level heuristics but is only allowed to communicate non problem-specific information such as CPU time and the change in the evaluation function. Problem-specific domain knowledge is prohibited from passing through the hyperheuristic/low-level heuristic interface. To allow the hyperheuristic to operate, a choice function is defined which adaptively ranks the low-level heuristics. The choice function provides guidance to the hyperheuristic by indicating which low-level heuristic should be applied next based upon the historical performance of the heuristics and the region of the search space currently under exploration. In a choice function was defined as a weighted sum of three factors expressing the recent improvement (change in the evaluation function) produced by each of the heuristics, the recent improvement produced by a consecutive pair of heuristics and the time since the heuristic had been called. Although the hyperheuristic produced good results, these results depended on the values of the parameters, and such values were empirically determined by simply choosing the best settings after many trials. Empirically setting the parameters increases the domain-specific knowledge of the hyperheuristic, which is not desirable. This paper is concerned with the development of a mechanism for automatically setting the parameters as well as refining the choice function. To do so we apply the hyperheuristic to a real-world personnel scheduling problem, that of scheduling a sales summit.


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Bibtex

@INPROCEEDINGS{cks2001a, author = {P. Cowling and G. Kendall and E. Soubeiga},
title = {A Parameter-Free Hyperheuristic for Scheduling a Sales Summit},
booktitle = {Proceedings of the 4th Metahuristics International Conference (MIC 2001)},
year = {2001},
pages = {127--131},
address = {16-20 July 2001, Porto Portugal},
note = {There is not an abstract for this paper, so we have used the introduction as the abstract.},
abstract = {Personnel scheduling involves scheduling people to timeslots and possibly locations. This problem remains the subject of much research interest with a survey in every decade since the 1970ís. Several models including Constraint Satisfaction Problem (CSP), mathematical programming (linear programming, 0-1 integer programming etc.), and different solution methods (exact or heuristic based) have been proposed to tackle this problem. Also several applications have been reported in the literature including applications for hospital personnel, educational institution personnel and others. However the heuristic methods developed for a particular personnel scheduling problem may not perform well if applied to a different problem. Heuristic and metaheuristic approaches tend to be knowledge rich, requiring substantial expertise in both the problem domain and appropriate heuristic techniques. It is in this context that we proposed a hyperheuristic approach as a heuristic that operates at a higher level of abstraction than current metaheuristic approaches. The hyperheuristic has no problem-specific knowledge. It manages a set of simple, knowledge-poor, low-level heuristics. At any given time the hyperheuristic must choose which low-level heuristic to call. The hyperheuristic interacts with the low-level heuristics but is only allowed to communicate non problem-specific information such as CPU time and the change in the evaluation function. Problem-specific domain knowledge is prohibited from passing through the hyperheuristic/low-level heuristic interface. To allow the hyperheuristic to operate, a choice function is defined which adaptively ranks the low-level heuristics. The choice function provides guidance to the hyperheuristic by indicating which low-level heuristic should be applied next based upon the historical performance of the heuristics and the region of the search space currently under exploration. In a choice function was defined as a weighted sum of three factors expressing the recent improvement (change in the evaluation function) produced by each of the heuristics, the recent improvement produced by a consecutive pair of heuristics and the time since the heuristic had been called. Although the hyperheuristic produced good results, these results depended on the values of the parameters, and such values were empirically determined by simply choosing the best settings after many trials. Empirically setting the parameters increases the domain-specific knowledge of the hyperheuristic, which is not desirable. This paper is concerned with the development of a mechanism for automatically setting the parameters as well as refining the choice function. To do so we apply the hyperheuristic to a real-world personnel scheduling problem, that of scheduling a sales summit.},
keywords = {hyper-heuristic, hyperheuristic, scheduling, personnel scheduling, rostering},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/cks2001a.pdf} }