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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
An Improved Receding Horizon Genetic Algorithm For The Tug Fleet Optimisation Problem |
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Authors: |
Robin T. Bye, Hans G. Schaathun |
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Published in:
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(2014).ECMS 2014 Proceedings edited
by: Flaminio Squazzoni,
Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for
Modeling and Simulation. doi:10.7148/2014 ISBN:
978-0-9564944-8-1 28th
European Conference on Modelling and Simulation, Brescia,
Italy, May 27th – 30th,
2014 |
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Citation
format: |
Robin
T. Bye, Hans G. Schaathun (2014). An Improved Receding Horizon Genetic Algorithm For The Tug Fleet Optimisation Problem, ECMS 2014 Proceedings edited by: Flaminio Squazzoni, Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for Modeling and Simulation. doi:10.7148/2014-0682 |
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DOI: |
http://dx.doi.org/10.7148/2014-0682 |
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Abstract: |
A fleet of tugs along the
northern Norwegian coast must be dynamically positioned to minimise the risk of oil tanker drifting accidents. We
have previously presented a receding horizon genetic algorithm (RHGA) for
solving this tug fleet optimisation (TFO) problem.
In this paper, we begin by presenting an overview of the TFO problem and the
details of the RHGA. Next, we identify and correct a flaw in the original
cost function of the RHGA. In addition, we present several new cost functions
that can be used for dynamic resource allocation by an algorithm such as the
RHGA. In a preliminary simulation study, we correct and extend the simulation
scenarios used in our previous work and examine the merit of each of the
suggested cost functions. Finally, we discuss the potential for an objective evaluation
method for comparing various TFO algorithms and briefly present our TFO
simulator. |
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