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- W3203827425 abstract "Global trade imbalances and poor, partial and unreliable information about available equipment make the coordination of empty containers a very challenging issue for shipping lines. The cancellation of transport operations once started or the extraordinary repositioning of containers are some of the problems faced by the local shipping agencies. In this paper, we selected the Artificial Neural Networks technique to predict the reception and withdrawal of empty containers in depots to forecast their future stock. To train the predictive models we used the different messages generated along the containers’ shipment journey together with the temporal data related to these events. The evaluation of the models with the test dataset confirmed the possibility of using ANN to predict the number of empty containers in depots." @default.
- W3203827425 created "2021-10-11" @default.
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- W3203827425 date "2021-01-01" @default.
- W3203827425 modified "2023-09-23" @default.
- W3203827425 title "Artificial Neural Network Based Empty Container Fleet Forecast" @default.
- W3203827425 cites W2056540816 @default.
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- W3203827425 doi "https://doi.org/10.1007/978-3-030-87986-0_10" @default.
- W3203827425 hasPublicationYear "2021" @default.
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