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http://dx.doi.org/10.38121/kpea.2021.12.37.4.33

A Study on the Prediction of Yard Tractors Required by Vessels Arriving at Container Terminal  

Cho, Hyun-Jun (한국해양대학교 KMI-KMOU 학연협동과정)
Shin, Jae-Young (한국해양대학교 물류.환경.도시인프라공학부)
Publication Information
Journal of Korea Port Economic Association / v.37, no.4, 2021 , pp. 33-40 More about this Journal
Abstract
Currently, the shipping and port industries are implementing strategies to improve port processing capabilities through the expansion and efficient operation of port logistics resources to survive fierce competition with rapidly changing trends. The calculation of the port's processing capacity is determined by the loading and unloading equipment installed at the dock, and the port's processing capacity can be improved through various methods, such as additional deployment of logistics resources or efficient operation of resources in use. However, it is difficult to expect an improvement effect in a short period of time because the additional deployment of logistics resources is clearly limited in time is clear. Therefore, it is a feasible way to find an efficient operation method for resources being used to improve processing capacity. Domestic ports are also actively promoting informatization and digitalization with the development of the 4th industrial revolution technology. However, the calculation of the number of Y/T (Yard Tractor) assignments in the current unloading process depends on expert experience, and related previous studies also focus on the allocations of Y/T or Calculation of the total number of Y/T required. Therefore, this study analyzed the factors affecting the number of Y/T allocations using the loading and unloading information of incoming ships, and based on this, cluster analysis, regression analysis, and deep neural network(DNN) model were used.
Keywords
Container terminal; Yard tractor; Clustering; analysis; Deep neural network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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