Artificial Neural Network Based Empty Container Fleet Forecast

被引：0

作者：

Meseguer Llopis, Joan ^{[1
]}

Furio Prunonosa, Salvador ^{[1
]}

Llop Chabrera, Miguel ^{[1
]}

Cubas Rodriguez, Francisco J. ^{[2
]}

机构：

[1] Fdn Valenciaport, R&D Projects, Valencia, Spain

[2] Cosco Shipping Lines Spain SA, Valencia, Spain

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (ICAISC 2021), PT I | 2021年 / 12854卷

关键词：

Artificial Neural Networks; Empty Container Depots; Container Flow Prediction; Forecasting;

D O I：

10.1007/978-3-030-87986-0_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：111 / 123

页数：13

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