• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.99-109
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• 2019

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. For example DEA is good at estimating "relative" efficiency of a DMU(Decision Making Unit), it only tells us how well we are doing compared with our peers but not compared with a "theoretical maximum." Thus, in order to measure efficiency of a new DMU, we have to develop entirely new DEA with the data of previously used DMUs. Also we cannot predict the efficiency level of the new DMU without another DEA analysis. We aim to show that DEA can be used to evaluate the efficiency of ports and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with C5.0. We can generate classification rules C5.0 in order to classify any new Port without perturbing previously existing evaluation structures by proposed methodology.

• Health Policy and Management
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• v.20 no.4
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• pp.74-96
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• 2010

The purpose of this empirical study is to evaluate efficiency of the regional public hospitals, using DEA(Data Envelopment Analysis). to do this, we design a DEA-AR/AHP Hybrid model to evaluate efficiency of 34 Regional Public Hospitals. the proposed model is developed by adding Acceptance Region(AR). using analytical hierarchy process(AHP). this model is compared with those of typical DEA models. Financial data used in this study were obtained from Database of the Korea Association Regional Public Hospital and analyzed using DEA model. As a result of analysis, This study found that the DEA-AR/AHP Hybrid model was superior to those typical DEA models in determining the priority among efficient hospitals. the result of this study can provide helpful information to evaluate the efficiency of public hospitals for efficient operational management, to develop more precise measurement for the priority of the efficient hospitals.

• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.165-178
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• 2016

Container port nowadays becomes one of the most vital link of the transportation chain, plays an important role in trading with other countries. Therefore, evaluating the operational efficiency of container ports to reflect their status and to reveal their position in this competitive environment is very important for port development. Although there have been lots of methods used to measure efficiency in the past, the DEA (Data Envelopment Analysis) model is still the most commonly applied approach. However, the data used in the model sometimes is complex and uncertain to handle using the basic DEA model. In this paper, we applied an uncertainty theory to create an uncertainty DEA model (UDEA), which can solve the limitation of the traditional one. This study mainly focuses on measuring efficiency of 41 container ports by applying proposed an UDEA model. The results show that among 41 container ports, only six container ports are regarded to have efficient operation through the clustering, meanwhile others have technical and scale inefficiencies. We found out that an UDEA model is better to analysis efficiency than existing DEA model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.364-378
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• 2008

Data Envelopment Analysis(DEA) models can be used for performance evaluation on governmental funding projects for IT small and medium-sized enterprises associated with multiple-outputs/multiple-inputs. In order to enhance the accuracy of DEA efficiency scores, DEA models with exogenously fixed variables are required where the corporate competency is taken into account. Additionally, it is necessary to use multiple DEA basic as well as extended models so as to relax the restriction on the performance evaluation to relying on a single DEA model. In this study; 1)a DEA data structure is designed including exogenously fixed variables representing corporate asset, revenue and the number of employees at the point in time that the governmental funding project concerned is initiated; 2)DEA basic as well as extended models are established according to the DEA data structure presented abovementioned; and 3)a case study is illustrated with an empirical testbed dataset. As for the DEA basic models, CCR, BCC, Super-efficiency model are adopted. The DEA extended models are developed based on the models associated with noncontrollable and nondiscretionary variables. In the case study, it is explained a comparison of DEA models and also major numerical outcomes such as efficiency scores, ranks derived from each DEA model are integrated using Analytic Hierarchy Process(AHP) weights. Performance significance with DEA efficiency scores between technical categories are tested based not only on parametric but also nonparametric single-factor analysis of variance method.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.201-212
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• 2017

This study presented the new evaluation index which can evaluate the discrimination of DEA models. To evaluate the discrimination of DEA models, data were analyzed using importance index as suggested in previous study and the coefficient of variation as suggested in this study for the discrimination evaluation. This study selected the CCR-DEA, BCC-DEA, entropy, bootstrap, super efficiency, and cross efficiency DEA model for the discrimination evaluation and accomplished empirical analysis. In order to grasp the rank correlation of the models, this study implemented the rank correlation analysis between the efficiency of CCR model and BCC model and entropy, bootstrap, super efficiency, and efficiency of the cross efficiency model. The obtained results of this study are as follows. First, the discrimination rank of models using the importance index and the coefficient of variation was shown to be identical. Therefore, the coefficient of variation can be used the discrimination evaluation index of DEA model. Second, the discrimination of the super efficiency model was found to be the highest rank among 4 models according to the analysis of this present study. Third, the highest rank correlation with CCR model was the super efficiency model. In addition, the super efficiency model was found to be the highest rank correlation with BCC model.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.449-458
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• 2004

Data envelopment analysis (DEA) estimators have been widely used in productivity analysis. The asymptotic distribution of DEA estimator derived by Kneip et al. (2003) is too complicated and abstract for analysts to use in practice, though it should be appreciated in its own right. This paper provides another way to express the limit distribution of the DEA estimator in a tractable way.

• Journal of Korea Port Economic Association
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• v.17 no.1
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• pp.27-51
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• 2001

The purpose of this paper is to investigate the productive efficiency of world container ports by using the DEA (Data Envelopment Analysis) method and raw data from previous research in measuring the international competitiveness of world container ports. Ports have to cope with rapid changes in shipping environments. In order for a port to compete in the global market, it must provide port services promptly and accurately. Basically, there are two approaches to measuring the international competitiveness of a container port. First, there is the traditional productivity analysis method, which analyzes productivity based on the container port's facilities (efficiency, selectivity, land availability), and by its general capacity (handling ability, storage capacity, terminal productivity). Second there is multi-attribute utility analysis, which considers several elements including the reasons for selecting particular container ports and factors determining international competitiveness. This paper follows the first method (traditional productivity analysis) and extends the limitation of previous studies by using the DEA method newly, and suggesting: the relative productive efficiency of container ports. The main results of this paper are as follows: First, the results of the DEA analysis in terms of world container ports matches that of a previous study (Jun et al., 1993) at a level of 35%. The low ratio is due to the constrained set of input-output elements, the result of only twenty container ports being analyzed in this paper. Second, the result of the DEA analysis in terms of North-East Asia's container ports matches with that of a previous study (Ha, 1996) at a level of 100 percent. Therefore we can conclude that the DEA analysis is the best measurement method for international competitiveness. Policy implications for this study are as follows: First, when port authorities want to measure the international competition power of container ports and enhance their productive efficiency, they should consider the traditional method and newly introduce the DEA method. Second, according to the analysis results of the DEA method, pen authorities should recommend benchmarking ports as reference ports in order to enhance the productive efficiency of container ports that show an efficiency score of below 1.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.517-521
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• 1998

DEA(data envelopment analysis) is a non-parametric technique for measuring and evaluating the relative efficiencies of a set of entities with common crisp inputs and outputs. In fact, in a real evaluation problem input and output data of entities often flucturate. These fluctuating data can be represented as linguistic variables characterized by fuzzy numbers. Based on a fundamental CCR model, a fuzzy DEA model is proposed to deal with fuzzy input and output data, Furthermore, a model that extends a fuzzy DEA to a more general case is also proposed with considering the relation between DEA and RA (regression analysis) . the crisp efficiency in CCR modelis extended to an L-R fuzzy number in fuzzy DEA problems to reflect some uncertainty in real evaluation problems.

• Korea Journal of Hospital Management
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• v.5 no.1
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• pp.84-104
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• 2000

This paper analyzes the technical efficiency of thirty two hospitals in Korea using DEA(Data Envelopment Analysis). DEA provides an efficiency measure for each hospital compared to the most efficient one. The amount and sources of inefficiency that are identified by the DEA are useful for benchmarking to improve efficiency. The results from multiple regression analysis and Wilcoxon Rank Sum test show that bed turnover, hospital size, and average length of stay are related to hospital efficiency.

• IE interfaces
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• v.24 no.4
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• pp.314-322
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• 2011

This study analyzes a relationship between Data Envelopment Analysis(DEA) efficiency scores and a normalization index in order to examine the validity of DEA models. A normalization index concerned in this study is 'sales per R&D project fund' which is regarded as a crucial R&D project performance evaluation index in practice. For this correlation analysis, three distinct DEA models are selected such as DEA basic model, DEA/AR-I revised model(i.e. DEA basic model with Acceptance Region Type I constraints) and Super-Efficiency(SE) model. Especially, SE model is adopted where efficient R&D projects(i.e. Decision Making Units, DMU's) with DEA efficiency score of unity from DEA basic model can be further differentiated in ranks. Considering the non-normality and outliers, two rank correlation coefficients such as Spearman's ${\rho}_s$ and Kendall's ${\tau}_B$ are investigated in addition to Pearson's ${\gamma}$. With an up-to-date empirical massive dataset of n = 482 R&D projects associated with R&D Loan Program of Korea Information Communication Promotion Fund in the year of 2011, statistically significant (+) correlations are verified between the normalization index and every model's DEA efficiency scores with all three correlation coefficients. Especially, the congruence verified in this empirical analysis can be a useful reference for enhancing the practitioner's acceptability onto DEA efficiency scores as a real-world R&D project performance evaluation index.