• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.344-350
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• 2022

Due to the increase in container cargo volume, the congestion of container terminals is increasing and the waiting time of gate in-out trucks has significantly lengthened at container yards and gates, resulting in severe inefficiency in gate in-out truck operations as well as port operations. To resolve this problem, the Busan Port Authority and terminal operator provide services such VBS, terminal congestion information, and expected operation processing time information. However, the visible effect remains insufficient, as it may differ from actual waiting time.. Thus, as basic data to resolve this problem, this study presents deep learning based average gate in-out truck waiting time prediction models, using container gate in-out information at Busan New Port. As a result of verifying the predictive rate through comparison with the actual average waiting time, it was confirmed that the proposed predictive models showed high predictive rate.

• Journal of Navigation and Port Research
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• v.40 no.6
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• pp.421-430
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• 2016

In this study, a gate simulation model was developed to reduce the truck waiting time for trucking companies servicing container terminals. To verify the developed model, 4 weeks of truck gate-in/gate-out data was collected in December 2014 at the Port of Busan New Port. Also, the existing gate system was compared to the proposed gate system using the developed simulation model. The result showed that based on East gate-in, a maximum number of 50 waiting trucks with a maximum waiting time of 120 minutes. With the proposed system the maximum number of waiting trucks was 10 with a maximum waiting time of 5.3 minutes. Based on West gate-in, the maximum number of waiting trucks was 17 and the maximum waiting time was 34 minutes in the existing gate system. With the proposed system the maximum number of waiting trucks was 10 with a maximum waiting time of 5.3 minutes. Based on West gate-out, the maximum number of waiting trucks was 11 with a maximum waiting time of 5.5 minutes. With the proposed system the maximum number of waiting trucks was 9 with a maximum waiting time of 4.4 minutes. This developed model shows how many waiting trucks there are, depending on the gate-in/gate-out time of each truck. This system can be used to find optimal gate system operating standards by assuming and adjusting the gate-in/gate-out time of each truck in different situations.

• Journal of Navigation and Port Research
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• v.48 no.1
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• pp.27-33
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• 2024

Following the COVID-19 pandemic, congestion within container terminals has led to a significant increase in waiting time and turnaround time for external trucks, resulting in a severe inefficiency in gate-in and gate-out operations. In response, port authorities have implemented a Vehicle Booking System (VBS) for external trucks. It is currently in a pilot operation. However, due to issues such as information sharing among stakeholders and lukewarm participation from container transport entities, its improvement effects are not pronounced. Therefore, this study proposed a deep learning-based predictive model for external trucks turnaround time as a foundational dataset for addressing problems of waiting time for external trucks' turnaround time. We experimented with the presented predictive model using actual operational data from a container terminal, verifying its predictive accuracy by comparing it with real data. Results confirmed that the proposed predictive model exhibited a high level of accuracy in its predictions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.30-38
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• 2007

In this study, we propose an RFID based container terminal operation process. Field tests are conducted for 900 MHz (truck identification) tag and 433 MHz RFID (container identification/e-seal) tag at an Incheon container terminal to verify possibility of applying RFID in the container terminal gate. To evaluate effect of applying RFID in the container terminal operation, simulation models for current and RFID based container terminals are developed using ARENA. Simulations are carried out using industrial field data and the result indicates that performance of the container terminal can be prominently improved after RFID application.