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Forecasting of quay line activity with neural networks

Author

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  • Íñigo L. Ansorena

Abstract

This paper presents a generalised regression neural network (GRNN) to forecast the activity of the North Quay at the port of Callao (Peru). To the author's knowledge, this is the first application of artificial neural network theory to container terminals in South America. On the basis of service characteristics, operating profiles, and dimension of vessels, the model examines the berthing line. Five numerical variables are used to estimate one dependent variable. The results achieved are satisfactory and the model built up using neural network theory is able to estimate the staying time of vessels in port.

Suggested Citation

  • Íñigo L. Ansorena, 2019. "Forecasting of quay line activity with neural networks," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 35(2), pages 196-207.
    • Handle: RePEc:ids:ijores:v:35:y:2019:i:2:p:196-207
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