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http://dx.doi.org/10.36498/kbigdt.2021.6.2.85

Demand Forecast For Empty Containers Using MLP  

DongYun Kim (인천대학교 동북아물류대학원)
SunHo Bang (인천대학교 동북아물류대학원)
Jiyoung Jang (인천대학교 동북아물류대학원)
KwangSup Shin (인천대학교 동북아물류대학원)
Publication Information
The Journal of Bigdata / v.6, no.2, 2021 , pp. 85-98 More about this Journal
Abstract
The pandemic of COVID-19 further promoted the imbalance in the volume of imports and exports among countries using containers, which worsened the shortage of empty containers. Since it is important to secure as many empty containers as the appropriate demand for stable and efficient port operation, measures to predict demand for empty containers using various techniques have been studied so far. However, it was based on long-term forecasts on a monthly or annual basis rather than demand forecasts that could be used directly by ports and shipping companies. In this study, a daily and weekly prediction method using an actual artificial neural network is presented. In details, the demand forecasting model has been developed using multi-layer perceptron and multiple linear regression model. In order to overcome the limitation from the lack of data, it was manipulated considering the business process between the loaded container and empty container, which the fully-loaded container is converted to the empty container. From the result of numerical experiment, it has been developed the practically applicable forecasting model, even though it could not show the perfect accuracy.
Keywords
Trade imbalance; empty container; demand forecast; artificial intelligence;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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