• Journal of Navigation and Port Research
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• v.45 no.6
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• pp.314-324
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• 2021

Competition between ports around the world is intensifying to attract transshipment cargo. However, recently, there have been concerns about the departure of transshipment cargo volume at Busan Port, such as the lifting of the cabotage policy in China's shipping sector and the implementation of a safe fare system. In terms of operation, terminal congestion and vehicle waiting time are seriously occurring due to imbalance in the transshipment volume of each terminal and vehicles concentrated in a specific time period. In this paper, we propose a method of inter-terminal transportation (ITT) using buffer space to solve the problem caused by inefficient ITT systems and presented a mixed integer programming (MIP) for the problem. The effect of using the buffer space was analyzed for various work volumes and capacity fluctuation ranges by applying the terminal congestion pattern and ITT vehicle in/out pattern based on the Busan New Port data.

• Journal of Navigation and Port Research
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• v.44 no.4
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• pp.298-304
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• 2020

The purpose of this study was to identify evaluation factors and analyze the relative importance among factors to select a suitable transportation method for transferring the increasing amount of transshipment at multiple terminals at the Busan new port. To accomplish this, the evaluation factors were selected through a literature survey and brainstorming of a group of experts associated with the port operation, and were classified into five major factors and 15 middle factors. The evaluation factors classified hierarchically were surveyed relative to workers in organizations such as shipping companies, port corporations, container terminals, and related ministries. The importance of each factor was calculated using the hierarchical analysis process (AHP). As a result of the importance analysis, priority was assigned in order of safety, productivity, investment efficiency, operational efficiency, and policy conformity. Through this, it was necessary to select a suitable transportation method for the transshipment cargo in terminals while focusing on the cargo and terminal security and preventing accidents. As a result of calculating from six ITT transportation candidates, the priority was determined in order of monorail, Autocon, and so on as ITT transportation suitable for the Busan new port..

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.354-364
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• 2017

This study tries to evaluate the effect of ITT introduction in Busan New Port. The study used the estimation model of the number of vehicles required in accordance with the backhaul rate. The model used big data, COPINO e-document for one year in 2015. COPINO recorded the event such as truck ID, container ID, ATA, damage etc when truck arrived at gate. The study finds important information to estimate the required number of trucks for handling current ITT containers in Busan New Port: Daily throughput in Busan New Port is 1650 vans, especially night throughput recorded peak level in 1800 hours to 2400 hours, the throughput between adjacent terminals recorded high, i.e PNIT to HPNT. The transportation capability for 6 hours between terminals is from 4 vans to 7 vans. The required trucks are estimated 89 currently without considering peak level. If we change the back haul rate from current 20% to 40%, 60% and 80%, how much would the cost drop? It was discovered that, if it is raised to 40%, 60% and 80%, the number of vehicle required will be reduced from 89 (current) to 76, 65 and 59. It was also discovered that the total savings will reduce down to 12%, 25% and 34%.

• 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.