Forecasting of quay line activity with neural networks
by Íñigo L. Ansorena
International Journal of Operational Research (IJOR), Vol. 35, No. 2, 2019

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.

Online publication date: Fri, 05-Jul-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Operational Research (IJOR):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com