• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.335-350
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• 2008

This paper analyzes the effects of port size on port tariff using the data for world major sixteen container ports. Some previous studies show that demand for port services have significant effects on port tariff, but we cannot find studies analyzing the correlation between the supply variables and the port tariff. In this paper, we used the five supply variables, which are the number of gantry crane, the number of berth, the quay length, the terminal area and the storage capacity for containers. The panel regression results are as follows. Port tariff generally decreases as port size increases, which shows that port tariff is explained by the economic theory. However, increase of port size, in some cases, does not reduce port tariffs, which may be due to monopolistic characteristics of port. This paper also shows that both demand and supply factors affect port tariff, but that demand factors have more consistent effect on port tariff than supply factors.

• Journal of Advanced Research in Ocean Engineering
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• v.4 no.3
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• pp.115-123
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• 2018

This study was carried using the new approach method to design appropriately the Loading Arm length and the alarm setting according to ship movements on Loading and Unloading marine Berth. The quasi-static mooring analysis was performed to estimate 110,000DWT ship's movements based on environmental conditions such as wind, current and wave. The mooring motion of the ship is very important to determine the loading arm scope, and in this case, the operation condition is performed on the ship without considering the damaged condition of the mooring line because the ship movement in case of damage is larger than intact, and all operations are stopped, the loading arm being released due to control system. From the result of mooring analysis, motion displacements, velocities and accelerations were simulated. They were used to simulate the maximum drifting speeds and distances. The maximum drifting speeds were checked to be satisfied within drifting speed limits. The total maximum drifting distances were simulated with alarm steps of the new approach method. Finally, the loading arm envelopes using the total maximum drifting distances were completed. Therefore, it was confirmed that the new approach method for loading arm envelopes and alarm settings was appropriate from the above results. In the future, it will be necessary to perform the further advanced dynamic mooring analysis instead of the quasi-static mooring analysis and to use the precise computer program analysis for various environments and ship movement conditions.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.73-87
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• 2016

The purpose of this study is to investigate the empirical results of Analytic Hierarchy Process/Data Envelopment Analysis-Assurance Region(AHP/DEA-AR) by using multiple regression analysis during the period of 2009-2012 with 5 inputs (number of gantry cranes, number of berth, berth length, terminal yard, and mean depth) and 2 outputs (container TEU, and number of direct calling shipping companies). Assurance Region(AR) is the most important tool to measure the efficiency of seaports, because individual seaports are characterized in terms of inputs and outputs. Traditional AHP and multiple regression analysis techniques have been used for measuring the AR. However, few previous studies exist in the field of seaport efficiency measurement. The main empirical results of this study are as follows. First, the efficiency ranking comparison between the two models (AHP/DEA-AR and multiple regression) using the Wilcoxon signed-rank test and Mann-Whitney signed-rank sum test were matched with the average level of 84.5 % and 96.3% respectively. When data for four years are used, the ratios of the significant probability are decreased to 61.4% and 92.5%. The policy implication of this study is that the policy planners of Korean port should introduce AHP/DEA-AR and multiple regression analysis when they measure the seaport efficiency and consider the port investment for enhancing the efficiency of inputs and outputs. The next study will deal with the subjects introducing the Fuzzy method, non-radial DEA, and the mixed analysis between AHP/DEA-AR and multiple regression analysis.