Abstract
The purpose of this research is to provide an improved Evolutionary Algorithm (EA) in combination with Monte Carlo Simulation (MCS) to identify the robust number of a new type of intelligent vehicles in container terminals. This type of vehicles, named Intelligent Autonomous Vehicles (IAVs), has been developed in a European project. This research extends our previous study on combining MCS with EAs. This paper has three main contributions: first, it proposes a dynamic strategy to adjust the number of samples used by MCS to improve the performance of the EA; second, it incorporates different robustness measures into the EA to produce different robust solutions depending on user requirements; and third, it investigates the relation between different robust solutions using statistical analyses to provide insights into what would be the most appropriate robust solutions for port operators. These contributions have been verified using empirical experiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beyer, H.G., Sendhoff, B.: Robust optimization-a comprehensive survey. Computer methods in applied mechanics and engineering 196(33), 3190–3218 (2007)
Farling, B., Mosier, C., Mahmoodi, F.: Analysis of automated guided vehicle configurations in flexible manufacturing systems. International Journal of Production Research 39(18), 4239–4260 (2001)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Kavakeb, S., Nguyen, T.T.: Identifying the robust number of vehicles in container terminal. Submitted to IEEE Computational Intelligence Magazine (2014)
Nguyen, T.T.: Continuous Dynamic Optimisation Using Evolutionary Algorithms. Ph.D. thesis, School of Computer Science, University of Birmingham. http://etheses.bham.ac.uk/1296 (January 2011)
Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation 6, 1–24 (2012)
Pyung Hoi, K., Woon Seek, L., Dong Won, J.: Fleet sizing and vehicle routing for container transportation in a static environment. OR Spectrum 26(2), 193–209 (2004)
Stahlbock, R.: Vob, S.: Operations research at container terminals: a literature update. OR Spectrum 30(1), 1–52 (2008)
Steenken, D., Vob, S., Stahlbock, R.: Container terminal operation and operations research - a classification and literature review. OR Spectrum 26(1), 3–49 (2004)
Vis, I.F.A., Koster, R.D., Roodbergen, K.J., Peeters, L.W.P.: Determination of the number of automated guided vehicles required at a semi-automated container terminal. The JORS 52(4), 409–417 (2001)
Vis, I.F.A., De Koster, R.M.B.M., Savelsbergh, M.W.P.: Minimum vehicle fleet size under time-window constraints at a container terminal. Transportation Science 39(2), 249–260 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kavakeb, S., Nguyen, T.T., Yang, Z., Jenkinson, I. (2014). Identifying the Robust Number of Intelligent Autonomous Vehicles in Container Terminals. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_67
Download citation
DOI: https://doi.org/10.1007/978-3-662-45523-4_67
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45522-7
Online ISBN: 978-3-662-45523-4
eBook Packages: Computer ScienceComputer Science (R0)