• Journal of the Korea Society for Simulation
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• v.8 no.4
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• pp.61-72
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• 1999

This paper presents an estimation method of container handling capacity and selection of resource allocation strategies of container terminals using the computer simulation models. Simulation models are developed to model container terminal consisting of 4 berths considering the berth allocation strategies, crane allocation strategies and the total number of container cranes using Arena simulation package. The proposed models do not consider the yard operations and gate operations. All the input parameters for the models are estimated on the basis of the existing container terminal operation data and the planning data for the automated container terminal planned by Korean government. Four berth allocation strategies and three crane allocation strategies are considered. The total number of container cranes considered ranges from 12 to 15. Non-terminating simulation techniques are utilized for the performance comparison among alternatives. The performance measures such as average ship turnaround time, average ship waiting time, average ship service time, the number of containers handled per year, and the number of ships processed per year are used. The result shows that the berth allocation strategy minimizing the sum of the number of ships waiting, the number of busy container cranes and number of ships handled performs better than any other berth allocation strategies. In addition, the crane allocation strategy allocating up to 5 container cranes per berth performs better than any other crane allocation strategies. Finally there are no significant performance differences among the alternatives consisting of different total number of container cranes allocated.

• Journal of Korea Port Economic Association
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• v.26 no.3
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• pp.198-220
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• 2010

This paper investigate the problems of standard container port handling capacity in establishing national port development plan in Korea. Considering container port developing, it's not easy to adopt container port service quality parameters such as lay time constraint of very large container ships by using the standard guideline of container port handling capacity. A simple methodology that connects vessel waiting to service time(w/s) and berth occupancy to costs has been used to evaluate the performance of a container terminal. But the total handling capacity have to be calculated by the performance of the handling system and number of equipments and layout of terminal by using computer simulation that represents of reality events needs to be performed by probabilistic techniques. A simulation model of estimation of container terminal capacity is introduced in order to establish a hub terminal for very large container ships that focus the port's quality of service and also suggest as tool for policy maker to justify a required port investment.

• Journal of Korea Port Economic Association
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• v.18 no.1
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• pp.43-60
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• 2002

Apart from the DEA method, the traditional measuring systems of container terminal productivities generally bring to light the partial productive efficiencies, because they evaluate only the productivity of each individual facility. However, these methods do not play roles of the reasonable productivity measures to compare and evaluate the efficiencies of terminals to meet the requirement of terminal customers like as shipping lines, shippers, etc. with the cutthroat competition in container handling market. As the suppliers of service, the terminals struggle to cope with the needs of buyers' markets due to the lack of suitable criteria to measure the efficiencies. Therefore, this paper uses the simulation approach to reveal which strategies enhance the productivities through the container terminal analysis. An additional intent of this paper is to build the active strategies to present what the experimental indexes like as norm-time exactness ratio, norm-time excess ratio, etc. compare between terminals to measure the total productivities for the age of global supply chain management. However, the experimental productivity indexes presented in this paper require a validation of the exact usefulness, by consideration from all angles in the future, and a development to more reasonable measuring method/indexes for the container terminal productivities.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.57-63
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• 2019

It is time to study safety improvement on container terminals to maintain a high-quality service to customers' productivity and safety. The data analyzed was of the past 3 years accidents data from a container terminal in the Busan port. I especially tried to found out the equipment that had the highest number of accidents and what the main causes were. This study suggests the top hazards identified in the container terminal using risk assessment, Moreover, it seeks control measurements to prevent hazards by bowtie methodology.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1209-1214
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• 2010

In these days, rail container service is being more important because it has relatively simple network. In addition, small lot sizes of shipment, frequent shipment, and demand for flexible service are important characteristics in rail container transportation. Efficient container operation at terminals is important in reducing transportation costs and load/unload time. This paper describes the assigning track of freight trains in the railway freight terminal. The paper addresses the operational requirements and facility restrictions to construct a mathematical model for track assignment. In addition, it suggests several types of objectives for the model.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.149-163
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• 2012

Logistics systems provide their service to customers by coordinating the resources with limited capacity throughout the underlying processes involved to each other. To maintain the high level of service under such complicated condition, it is essential to carry out the real-time monitoring and continuous management of logistics processes. In this study, we propose a method of estimating the service completion time of key processes based on process-state information collected in real time. We first identify the factors that influence the process completion time by modeling and analyzing an influence diagram, and then suggest algorithms for quantifying the factors. We suppose the container terminal logistics and the process of discharging and loading containers to a vessel. The remaining service time of a vessel is estimated using a decision tree which is the result of machine-learning using historical data. We validated the estimation model using container terminal simulation. The proposed model is expected to improve competitiveness of logistics systems by forecasting service completion in real time, as well as to prevent the waste of resources.

• Journal of Korea Port Economic Association
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• v.22 no.1
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• pp.125-149
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• 2006

The main purpose of this study is to develop new measurement device of the quality of terminal service, applying the existing research output of service marketing to the specific industrial aspect of container terminal. Especially we tried to help marketing managers present the points of effective marketing strategy with each dimension of service quality. To achieve this aim, we defined the service quality demensions of container port as the following three ones; the internal, the external, and the interactive qualities. The applied measuring instruments could be divided into three dimensions by carrying out factor analysis. Additionally, by analyzing the relative effects of the three dimensions of service quality on customer satisfaction, we found that the relative importance are as follows; interaction quality>internal quality> external quality. We confirmed that the three dimensions of service quality could affect customer loyalty and information, through customer satisfaction.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.165-166
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• 2015

Due to the international economic downturn and the increasing competition, container terminals have been trying to achieve cost effectiveness and high service levels. Shipping companies want to get better services from container terminals but container terminals only have limited space and equipment. Frequently, too many external trucks come to the terminal at the same time, and create bottle necks at the terminals. Ouside the container terminals traffic jams occur as well. As a result, stakeholders need a vehicle booking system. By VBS, container terminals can plan efficiently in advance to avoid work delay and the truck drivers can finish their jobs quickly. This paper aims to search efficient models for the container terminals by using VBS.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.153-154
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• 2020

It can be replaced with a smart work service centering on tasks that do not require on-site work among container terminal tasks. Here, the cloud service is helpful for small and medium-sized stevedoring companies as it reduces the cost and time required for initial system construction, and allows you to pay usage fees as needed. This study introduces a case study on the development of web services that comply with industry standards so that advanced ICT can be used in connection with the terminal operation system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.345-352
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• 2006

The purpose of this paper is to evaluate the efficiency of two yard layout of HSS at container terminal, one is that the container yard blocks are placed horizontally in parallel with berth, the other is that the yard blocks are arranged vertically in perpendicular to the berth. In stevedoring system of container terminal, stacking and transport performance are influenced according to block arrangement type of yard. Therefore, efficient design that can improve stacking and transport performance is required. In this paper, we compared their efficiency of two block arrangement concepts in terms of storage capacity, productivity, facility investments, truck service level.