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      Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain) Translated title: Predicción a corto plazo de la congestión del flujo de mercancías: El caso de estudio del Puerto Bahía de Algeciras (España)

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          Abstract

          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.

          Translated abstract

          La predicción de la congestión en el tráfico de mercancías (picos de carga) es una importante herramienta para la toma de decisiones y es el principal objetivo de este trabajo. Predecir los flujos futuros de mercancías proporciona una potente herramienta en la cadena de suministro. En este trabajo, se presenta una metodología para conseguir el mejor modelo para predecir situaciones de congestión en flujos de mercancías. La predicción es modelada como un problema de clasificación, evaluando diferentes métodos (K-vecinos, clasificador Bayesiano y Redes Neuronales Artificiales). Para seleccionar el mejor modelo se desarrolla un panel de expertos (mediante métodos post-hoc del test de Friedman). La metodología propuesta se aplica a la cadena logística del Puerto Bahía de Algeciras. Los resultados obtenidos revelan la eficiencia de los modelos presentados, que pueden ser aplicados para mejorar la planificación diaria de operaciones.

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          A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings

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            • Article: not found

            Neural Network for Pattern Recognition

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              • Record: found
              • Abstract: not found
              • Article: not found

              Neural networks for classification: a survey

              G.P. Zhang (2000)
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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                dyna
                DYNA
                Dyna rev.fac.nac.minas
                Universidad Nacional de Colombia
                0012-7353
                February 2016
                : 83
                : 195
                : 163-172
                Affiliations
                [1 ] University of Cádiz Spain
                [2 ] University of Cádiz Spain
                [3 ] University of Cádiz Spain
                [4 ] University of Cádiz Spain
                [5 ] University of Cádiz Spain
                Article
                S0012-73532016000100021
                10.15446/dyna.v83n195.47027
                0cf5daa2-368c-4eaf-a5cc-0b5a68b9a26b

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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                SciELO Colombia

                Self URI (journal page): http://www.scielo.org.co/scielo.php?script=sci_serial&pid=0012-7353&lng=en
                Categories
                ENGINEERING, MULTIDISCIPLINARY

                General engineering
                predicción de mercancías,clasificación,congestión,redes neuronales artificiales,test de comparación múltiple,freight forecasting,classification,congestion,artificial neural networks,multiple comparison tests

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