• The Journal of Information Systems
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• v.33 no.2
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• pp.125-142
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• 2024

Purpose The purpose of this study is to identify and analyze the key factors influencing congestion in the in-out transportation at port container terminals, and to design of a predictive model for in-out congestion based on these analysis. This study focused on architecting a deep learning-based predictive model. Design/methodology/approach This study was conducted through the following methodology. First, hypotheses were established and data were analyzed to examine the impact of vessel schedules and external truck schedules on in-out transportation. Next, explored time series forecasting models to a design the architecture for deep learning-based predictive model. Findings According to the empirical analysis results, this study confirmed that vessel schedules significantly affect in-out transportation. Specifically, the volume of transportation increases as the vessel arrival/departure time and the cargo cutoff time approach. Additionally, significant congestion patterns in transportation volume depending on the day of the week and the time of day were observed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.59-60
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• 2018

Busan New Port has continued to expand its capacity to handle transshipment cargos exceeding 50 percent of its total volume, but it is considered inefficient because of the work scheduling based on the worker experience. In particular, depending on the transshipment task situation, which often requires an external truck, excessive congestion caused by the vehicle's delay can lead to increased logistics costs and social costs. One way to resolve this issue is to minimize the single transport of the truck and to maximize dual-cycle transport by putting the finished truck into another task. Therefore, we would like to study how to efficiently schedule transportation transshipment cargos between terminals considering dual-cycle.