Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain)

The prediction of freight congestion (cargo peaks) is an important tool for decision making and it is this paper’s main object of study. Forecasting freight flows can be a useful tool for the whole logistics chain. In this work, a complete methodology is presented in order to obtain the best model t...

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Bibliographic Details
Published in:Dyna (Medellín, Colombia) Colombia), 2016-01, Vol.83 (195), p.163-172
Main Authors: Ruiz Aguilar, Juan Jesús, Turias, Ignacio J., Moscoso López, José A., Jiménez Come, María J., Cerbán, María M.
Format: Article
Language:English
Subjects:
artificial neural networks
clasificación
classification
congestion
congestión
ENGINEERING, MULTIDISCIPLINARY
freight forecasting
multiple comparison tests
predicción de mercancías
redes neuronales artificiales
test de comparación múltiple
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Summary:The prediction of freight congestion (cargo peaks) is an important tool for decision making and it is this paper’s main object of study. Forecasting freight flows can be a useful tool for the whole logistics chain. In this work, a complete methodology is presented in order to obtain the best model to predict freight congestion situations at ports. The prediction is modeled as a classification problem and different approaches are tested (k-Nearest Neighbors, Bayes classifier and Artificial Neural Networks). A panel of different experts (post–hoc methods of Friedman test) has been developed in order to select the best model. The proposed methodology is applied in the Strait of Gibraltar’s logistics hub with a study case being undertaken in Port of Algeciras Bay. The results obtained reveal the efficiency of the presented models that can be applied to improve daily operations planning.
ISSN:0012-7353
2346-2183
DOI:10.15446/dyna.v83n195.47027