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

Can ants play chess? Yes they can!
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Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX

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Publication(s)

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques
http://bit.ly/eJvwpG
Scheduling in sports: An annotated bibliography
http://bit.ly/eCfi42
Collective Behavior and Kin Selection in Evolutionary IPD
http://bit.ly/if34nF
Constructing Initial Neighbourhoods to Identify Critical Constraints
http://bit.ly/h3xfnd

Graham Kendall: Details of Requested Publication


Citation

Chen, J; Bai, R; Dong, H; Qu., R and Kendall, G A dynamic truck dispatching problem in marine container terminal. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016.


Abstract

In this paper, a dynamic truck dispatching problem of a marine container terminal is described and discussed. In this problem, a few containers, encoded as work instructions, need to be transferred between yard blocks and vessels by a fleet of trucks. Both the yard blocks and the quay are equipped with cranes to support loading/unloading operations. In order to service more vessels, any unnecessary idle time between quay crane (QC) operations need to be minimised to speed up the container transfer process. Due to the unpredictable port situations that can affect routing plans and the short calculation time allowed to generate one, static solution methods are not suitable for this problem. In this paper, we introduce a new mathematical model that minimises both the QC makespan and the truck travelling time. Three dynamic heuristics are proposed and a genetic algorithm hyperheuristic (GAHH) under development is also described. Experiment results show promising capabilities the GAHH may offer.


pdf

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doi

The doi for this publication is 10.1109/SSCI.2016.7850081 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



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Bibtex

@INPROCEEDINGS{cbdqk2016, author = {J. Chen and R. Bai and H. Dong and R. Qu. and G. Kendall},
title = {A dynamic truck dispatching problem in marine container terminal},
booktitle = {Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
year = {2016},
abstract = {In this paper, a dynamic truck dispatching problem of a marine container terminal is described and discussed. In this problem, a few containers, encoded as work instructions, need to be transferred between yard blocks and vessels by a fleet of trucks. Both the yard blocks and the quay are equipped with cranes to support loading/unloading operations. In order to service more vessels, any unnecessary idle time between quay crane (QC) operations need to be minimised to speed up the container transfer process. Due to the unpredictable port situations that can affect routing plans and the short calculation time allowed to generate one, static solution methods are not suitable for this problem. In this paper, we introduce a new mathematical model that minimises both the QC makespan and the truck travelling time. Three dynamic heuristics are proposed and a genetic algorithm hyperheuristic (GAHH) under development is also described. Experiment results show promising capabilities the GAHH may offer.},
doi = {10.1109/SSCI.2016.7850081},
owner = {gxk},
timestamp = {2011.01.03},
webpdf = {http://www.graham-kendall.com/papers/cbdqk2016.pdf} }