Development of a Vessel-Performance Forecasting System: Methodological Framework and Case Study
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 142, Issue 2
Abstract
Terminals rely on optimization tools to determine merchandise location, quay occupation, or vehicle trajectories to minimize the movements and time dedicated to every task. However, operations are developed in an environment that induces variability to the theoretical model used to schedule and control the operations. Given the complexity of the port operations, artificial intelligence systems can act as a valuable tool to analyze such processes. Neural networks in particular are characterized by their capacity to establish nonlinear relationships (and consequently, nonintuitive ones) among the variables; this interaction generates a specific operational response. In the near future, the monitoring of operational variables has great potential to make a qualitative improvement in the operations management and planning models of terminals that use increasing levels of automation. This paper proposes a method to obtain operational parameter forecasts in container terminals. To this end, a case study is presented, in which forecasts of vessel performance are obtained. By doing so, the management strategies are supported by an expert system, grounded in the historical data series of quay operation and the climatic conditions observed, as well as the ordinary and extraordinary events that have happened in the past, from which the system is able to learn. This research was based entirely on data gathered from a semiautomated container terminal from Spain.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors are indebted to the Spanish Agency of State Ports (Puertos del Estado) for financial support.
References
Adya, M., and Collopy, F. (1998). “How effective are neural networks at forecasting and prediction? A review and evaluation.” J. Forecasting, 17(5-6), 481–495.
ASCE. (2006). “Minimum design loads for buildings and other structures.” ASCE/SEI 7-10, Reston, VA.
Beale, M. H., Hagan, M. T., and Demuth, H. B. (2012). Neural network toolbox: Reference. MathWorks, Natick, MA.
Bilegan, I., Crainic, T. G., and Gendreau, M. (2006). “Fleet management for advanced intermodal services.” Final report, Centre de Recherche sur les Transports (CRT), Montréal.
Carbonneau, R., Laframboise, K., and Vahidov, R. (2007). “Application of machine learning techniques for supply chain demand forecasting.” Eur. J. Oper. Res., 184(3), 1140–1154.
Celik, H. M. (2004). “Modeling freight distribution using artificial neural networks.” J. Transp. Geogr., 12(2), 141–148.
Chao, X., and Erihe (2013). “Artificial neural network and fuzzy logic in forecasting short-term temperature.” M.S. thesis, Telemark University College, Porsgrunn, Norway.
Collopy, F., Adya, M., and Armstrong, J. S. (1994). “Principles for examining predictive validity: The case of information systems spending forecasts.” Inf. Syst. Res., 5(2), 170–179.
Dasu, T., and Johnson, T. (2003). Exploratory data mining and data cleaning, John Wiley & Sons, Florham Park, NJ.
Doukim, C. A., Dargham, J. A., and Chekima, A. (2010). “Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique.” Proc., 10th Int. Conf. on Information Sciences Signal Processing and Their Applications (ISSPA), IEEE, Universiti Malaysia Sabah, Kuala Lumpur, Malaysia, 606–609.
Durst, C. S. (1960). “Wind speeds over short periods of time.” Meteorol. Mag., 89(1960), 181–186.
Fancello, G., Pani, C., Pisano, M., Serra, P., and Fadda, P. (2010). “Development of prediction models for container traffic.” 12th World Congress on Transportation Research, Lisbon, Portugal.
Gosasang, V., Vatcharavee, C., and Supaporn, K. (2010). “An application of neural networks for forecasting container throughput at Bangkok port.” 12th World Congress on Transportation Research, Lisbon, Portugal.
Hagan, M. T., Demuth, H. B., and Beale, M. H. (1996). Neural network design, PWS Publishing, Boston.
Hagan, M. T., and Menhaj, M. B. (1999). “Training feedforward networks with the Marquardt algorithm.” IEEE Trans. Neural Networks, 5(6), 989–993.
Hettiarachchi, P., Hall, M. J., and Minns, A. W. (2005). “The extrapolation of artificial neural networks for the modelling of rainfall–runoff relationships.” J. Hydroinf., 7, 291–296.
Huang, G.-B., and Babri, H. A. (1998). “Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions.” IEEE Trans. Neural Networks, 9(1), 224–229.
Hyndman, R. J., and Koehler, A. B. (2006). “Another look at forecast accuracy.” Int. J. Forecasting, 22(4), 679–688.
Jagadish, H., et al. (1997). “Special issue on data reduction techniques.” Bull. Tech. Committee Data Eng., 20(4).
Jang, J.-S. R. (1996). “Input selection for ANFIS learning.” Proc., 5th IEEE Int. Conf. on Fuzzy Systems, Vol. 2, IEEE, New York, 1493–1499.
Khalifa, M. A. (2009). “Calmness study for container handling ports with open basin systems using numerical modeling.” JKAU Mar. Sci., 20, 69–88.
Ligteringen, H., and Velsink, H. (2000). Ports and terminals, VSSD, TU Delft, Delft, Netherlands.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications.” Environ. Modell. Software, 15(1), 101–124.
Marquardt, D. W. (1963). “An algorithm for least-squares estimation of nonlinear parameters.” SIAM J. Appl. Math., 11(2), 431–441.
MATLAB 2014a [Computer software]. MathWorks, Natick, MA.
Minns, A. W., and Hall, M. J. (2005). “Artificial neural networks as rainfall-runoff models.” Hydrolog. Sci. J., 41(3), 399–417.
Moscoso, J. (2013). “Predicción de tráfico ro-ro en el nodo logístico del Estrecho de Gibraltar.” Ph.D. thesis, Universidad de Cádiz, Algeciras, Spain.
Navarro, M. N., Molina, R., Martin, M. M., Hernandez Torres, J. M., and Hernandez, A. H. (2010). “Development of a neuro fuzzy modelling tool for a decision support system in desalination in coastal zones.” Desalin. Water Treat., 22(1–3), 386–391.
PIANC. (2012). “Use of hydro/meteo information for port access and operations.” Rep. No. 117, World Association for Waterborne Transportation Infrastructure, Brussels, Belgium.
Powell, M. D., Vickery, P. J., and Reinhold, T. A. (2003). “Reduced drag coefficient for high wind speeds in tropical cyclones.” Nature, 422(6929), 279–283.
Puertos del Estado. (2001). ROM 0.0: General procedure and requirements in the design of harbor and maritime structures, Public Works Ministry, Madrid, Spain.
Pyle, D. (1999). Data preparation for data mining, Vol. 1, Morgan Kaufmann, Burlington, MA.
Vogl, T. P., Mangis, J. K., Rigler, A. K., Zink, W. T., and Alkon, D. L. (1988). “Accelerating the convergence of the backpropagation method.” Biol. Cybern., 59(4–5), 257–263.
Wang,W.-C., Chau, K.-W., Cheng, C.-T., and Qiu, L. (2009). “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series.” J. Hydrol., 374(3-4), 294–306.
Yan, B., Zhou, Q., and Gao, T. (2012). “Analysis on throughput capacity of coal terminal by bp neural network.” ICLEM 2012: Logistics for sustained economic development--technology and management for efficiency, J. Zhang, X. Zhang, Z. Qiu, and P. Yi, eds., ASCE, Reston, VA, 302–307.
Zhang, G., Patuwo, B. E., and Hu, M. Y. (1998). “Forecasting with artificial neural networks: The state of the art.” Int. J. Forecasting, 14(1), 35–62.
Information & Authors
Information
Published In
Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 142 • Issue 2 • March 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 8, 2014
Accepted: Apr 28, 2015
Published online: Oct 2, 2015
Published in print: Mar 1, 2016
Discussion open until: Mar 2, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.