Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain)
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| Format: | Artículo científico |
| Sprache: | en |
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Universidad Nacional de Colombia
2016
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| _version_ | 1876478921050947584 |
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| author | Juan Jesús Ruiz-Aguilar |
| author_facet | Juan Jesús Ruiz-Aguilar |
| contents | Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain) Juan Jesús Ruiz-Aguilar Ignacio Turias José Antonio Moscoso-López María Jesús Jiménez-Come Mar Cerbán Ingeniería congestion classification Freight forecasting multiple comparison tests artificial neural networks 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. 2016 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49644128021 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.195 Vol.83 |
| format | Artículo científico |
| id | redalyc_49644128021 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain) Juan Jesús Ruiz-Aguilar Ingeniería congestion classification Freight forecasting multiple comparison tests artificial neural networks Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain) Juan Jesús Ruiz-Aguilar Ignacio Turias José Antonio Moscoso-López María Jesús Jiménez-Come Mar Cerbán Ingeniería congestion classification Freight forecasting multiple comparison tests artificial neural networks 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. 2016 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49644128021 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.195 Vol.83 |
| title | Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain) |
| topic | Ingeniería congestion classification Freight forecasting multiple comparison tests artificial neural networks |
| url | https://www.redalyc.org/articulo.oa?id=49644128021 |