• Journal of Korea Port Economic Association
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• v.26 no.2
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• pp.82-98
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• 2010

The purpose of this paper is to suggest the efficiency measurement way of Korean seaport by using Average Index Transformation model of fuzzy DEA(Data Envelopment Analysis). Two inputs[cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, and the number of ship calls] are used in 1995 and 2004 for 26 Korean seaports. Empirical main results are as follows: First, Tongyung, Gohyun, Okpo, and Sogcho Ports are efficient, and Yeasu Port shows the high efficiency level over 95% under input oriented CCR model. Gohyun and Sogcho Ports showed the most efficient score under average index transformation model. Okpo and Yeasu Ports increased their efficiency scores as the lamda(λ) values are up. The empirical results of fuzzy DEA average index transformation model for Wando, Yeasu, and Seoguipo ports showed that if the lamda values are higher, the efficiency scores are also higher. The main policy implication based on the findings of this study is that the management manager of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the fuzzy DEA average index transformation model for deciding the size of inputs including the port investment amount and evaluating the port efficiency.

• 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.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.17-52
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• 2018

The purpose of this paper is to measure the clustering change and analyze empirical results. Additionally, by using k-means, hierarchical, and mixed models on Asian container ports over the period 2006-2015, the study aims to form a cluster comprising Busan, Incheon, and Gwangyang ports. The models consider the number of cranes, depth, birth length, and total area as inputs and container twenty-foot equivalent units(TEU) as output. Following are the main empirical results. First, ranking order according to the increasing ratio during the 10 years analysis shows that the value for average linkage(AL), mixed ward, rule of thumb(RT)& elbow, ward, and mixed AL are 42.04% up, 35.01% up, 30.47%up, and 23.65% up, respectively. Second, according to the RT and elbow models, the three Korean ports can be clustered with Asian ports in the following manner: Busan Port(Hong Kong, Guangzhou, Qingdao, and Singapore), Incheon Port(Tokyo, Nagoya, Osaka, Manila, and Bangkok), and Gwangyang Port(Gungzhou, Ningbo, Qingdao, and Kasiung). Third, optimal clustering numbers are as follows: AL(6), Mixed Ward(5), RT&elbow(4), Ward(5), and Mixed AL(6). Fourth, empirical clustering results match with those of questionnaire-Busan Port(80%), Incheon Port(17%), and Gwangyang Port(50%). The policy implication is that related parties of Korean seaports should introduce port improvement plans like the benchmarking of clustered seaports.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.139-164
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• 2009

The purpose of this paper is to investigate the value chain efficiency of Korean port investment by using the newly developed multi-year and multi-stage value chain efficiency model of DEA(Data Envelopment Analysis). Inputs[port investment amount, cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, number of ship calls, revenue, and score of customer service satisfaction] are used during 14 years(1994-2007) for 20 Korean seaports by using two kinds of DEA models. Empirical main results are as follows: First, Model 1 shows that the ranking order of multi-stage value chain efficiency is Stage 2, Stage 3-1, Stage 1, and Stage 3-2. And according to the value chain average efficiency scores, ranking order is stages 2, 1, 3-1, and 3-2. In Model 2, 3(Incheon, Mogpo, and Jeju) out of 9 ports show the ranking order of Stages 2, 3-2, 3-1, and 1. And value chain average efficiency scores rank in order of Stages 2, 3-2, 3-1, and 1. Second, the difference among the value chain efficiency scores of each stage comes from the efficiency deterioration of all ports except Stages 2 and 1 in Model 1. In Model 2, value chain efficiency scores among the Stages 3-1, 3-2 compared to Stage 1 were deteriorated. The main policy implication based on the findings of this study is that the manager of port investment and management of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the multi-year, multi-stage value chain efficiency method for deciding the port investment amount and evaluating the effect of port investment after considering the empirical results of this paper carefully.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.209-239
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• 2008

The purpose of this paper is to investigate the effectiveness of Korean port investment by using the newly developed slack-based multi-year panel congestion model of DEA(Data Envelopment Analysis). Inputs[port investment amount, cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, number of ship calls, revenue, and score of customer service satisfaction] are used during 1994-2004 for 20 Korean seaports. Empirical analysis identified congestion especially in port investment as input at the ports of Gunsan, and Busan in the all 3 models, and the ports of Pyungtag, Mogpo, Yeosu, and leju in over 2 models. Port investment induced the rapid increase of port efficiency from the ports of Masan, Incheon, Donghae, and Samcheok. Therefore other ports except these ports should examine the reason about the inefficiency of port investment by searching out the situation of each ports directly. The main policy implication based on the findings of this study is that The Ministry of Land, Transport and Maritime Affairs in Korea should introduce the new measurement way after reviewing the multi-year slack-based congestion approach when the amount of port investment for each port is decided.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.31-70
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• 2021

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Hierarchical clustering(single, complete, average, and centroid), Silhouette, and 2SCE[the Second Stage(Type II) cross-efficiency] matrix clustering models on Asian container ports over the period 2009-2018. The models have chosen number of cranes, depth, birth length, and total area as inputs and container TEU as output. The main empirical results are as follows. First, ranking order according to the efficiency increasing ratio during the 10 years analysis shows Silhouette(0.4052 up), Hierarchical clustering(0.3097 up), and 2SCE(0.1057 up). Second, according to empirical verification of the Silhouette and 2SCE models, 3 Korean ports should be clustered with ports like Busan Port[ Dubai, Hong Kong, and Tanjung Priok], and Incheon Port and Gwangyang Port are required to cluster with most ports. Third, in terms of the ASEAN, it would be good to cluster like Busan (Singapore), Incheon Port (Tanjung Priok, Tanjung Perak, Manila, Tanjung Pelpas, Leam Chanbang, and Bangkok), and Gwangyang Port(Tanjung Priok, Tanjung Perak, Port Kang, Tanjung Pelpas, Leam Chanbang, and Bangkok). Third, Wilcoxon's signed-ranks test of models shows that all P values are significant at an average level of 0.852. It means that the average efficiency figures and ranking orders of the models are matched each other. The policy implication is that port policy makers and port operation managers should select benchmarking ports by introducing the models used in this study into the clustering of ports, compare and analyze the port development and operation plans of their ports, and introduce and implement the parts which required benchmarking quickly.