Title: Forecasting of quay line activity with neural networks

Authors: Íñigo L. Ansorena

Addresses: Department of Civil Engineering: Transport, Technical University of Madrid, C/Profesor Aranguren s/n, Madrid 28040, Spain

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.

Keywords: neural network; berthing line; Callao Port.

DOI: 10.1504/IJOR.2019.100724

International Journal of Operational Research, 2019 Vol.35 No.2, pp.196 - 207

Received: 21 Oct 2015
Accepted: 25 Jun 2016
Published online: 17 Jul 2019 *

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