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DYNA

versão impressa ISSN 0012-7353

Resumo

RUIZ-AGUILAR, Juan Jesús et al. Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain). Dyna rev.fac.nac.minas [online]. 2016, vol.83, n.195, pp.163-172. ISSN 0012-7353.  http://dx.doi.org/10.15446/dyna.v83n195.47027.

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.

Palavras-chave : freight forecasting; classification; congestion; artificial neural networks; multiple comparison tests.

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