• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.249-258
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• 2013

Data Envelopment Analysis (DEA) is a useful tool to analyze the relative efficiency of decision making units (DMU) characterized by multiple inputs and multiple outputs. This method has been popularly used as an analytical tool to suggest some strategic improvement. To do this, the results of DEA provide decision makers with a single efficiency score, efficient frontier, return to scale, benchmarking decision making units, etc. The purpose of this paper is to evaluate research performance of 38 universities and provide an inefficient university with the way of organizational changes to be an efficient university by using DEA. Various input and output variables are used to identify technical and scale inefficiency. Additionally, we analyze how an inefficient DMU could be changed an efficient DMU based on a case university. This result will give an insight of constructive directions for increasing of research performance to university decision makers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.1-6
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• 2006

This study proposes a new approach which combines Data Envelopment Analysis(DEA) and the Analytic Hierarchy Process(AHP) techniques to effectively evaluate Decision Making Units(DMUs). While DEA evaluates a quantitative data set, employs linear programming to obtain input and output weights and ranks the performance of DMUs, AHP evaluates the qualitative data retrieved from expert opinions and other managerial information in specifying weights. The objective of this research is to design a decision support process for managers to incorporate positive aspects of DEA's absolute numerical evaluations and AHP's human preference structure values. It is believed that a pragmatic manager will be more receptive to the results that include subjective opinions incorporated into the evaluation of the efficiency of each DMU efficiency. The WPDEA method provides better discrimination than the DEA method by reducing the number of efficient units.

• Journal of Navigation and Port Research
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• v.46 no.2
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• pp.110-120
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• 2022

Spain is Europe's second-largest country with total throughput reaching 16.7 million twenty-foot equivalent units (TEU) by 2020. The purpose of this study was to measure and compare the efficiency of 17 container terminals. As a study method, the DEA-CCR model, undesirable variable, and Malmquist Index (MI) were used for data envelopment analysis (DEA). The study results are as follow: (1) DEA-CCR is used to evaluate basic efficiency. The most efficient terminals are decision-making units DMU 1 (APM Terminals (Algeciras Port)), DMU 2 (Total Terminal International Algeciras (Algeciras Port)) and DMU 5 (Barcelona Europe South Terminal (Barcelona Port)). (2) Undesirable DEA was conducted to suggest inefficiency from the undesirable output. Overall, the efficiency scores were reduced. However, DMU 1, DMU 2, and DMU 5 maintained efficiency scores regardless of the finish factor. (3) Malmquist Index was used to observe technology and efficiency changes dynamically. The changes in TCI affected Spanish container terminals more than the Technical Efficiency Change Index (TECI) in 2018-2019. However, in 2019-2020, the TECI was 2.706, higher than the TCI value, indicating that the change in TECI had more influence on the increase in productivity. This study offers a broader understanding of Spanish container terminals.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.173-183
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• 2004

Data envelopment analysis(DEA) is a mathematical programming approach to asses s relative efficiency of a group of decision-making units. In view of the defects of existing models in evaluating efficiency of the system with P independent subsystems, Yang et al. [10] introduced YMK model with the assumption that decision-making unit(DMU) is independent of each other. But in some production systems, decision-making units usually have some relationships in this way or that. In this paper, DEA model is given by assuming that DMUs can cooperate with others in its subgroups. Some property and the efficiency relationship of the whole system and its subsystems are given.

• Health Policy and Management
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• v.6 no.2
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• pp.1-13
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• 1996

To evalate the efficiency of public and private hospitals, the author used Data Envelopment Analysis(DEA), a mathematical linear programming method calculating the of ficiency of a unity(DMU: Decision Making Unit) in relation to the other units in analysis. DEA was applied to thirty three (10 public and 23 private) general hospitals wiwith 160 to 299 beds. In respect to productivity, public hospitals appeared to be a little more efficient than private ones, even though it's statisticansignificant. However, the efficiency score for profitability conversed that these contrary results were due to the caring of more medical protection patients in public hospitals, who brought less revenlue to te hospital than other patients. Public hospitals' superiority to private counterparts in productivity, which are aguged mainly based on cared patients, suggests that the former contributes so much positively to social utility. In particular, the fact that public hospitals are caring more medical protection patients, namely the poverty group whom the society should bear a burden of by all means, seems to be desirable in respect of role of publi hospitals.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.209-220
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• 2010

Evaluating performance in public sector aims to enhance the efficiency of organizations. Evaluating the efficiency which is the ratio between input and output, organizations set directions of improvement. This research applied Data Envelopment Analysis(DEA) useful to evaluating the efficiency of organizations in public sector. Decision Making Units(DMU) of this research are 21 Information Security Organizations of departments/agencies. As the results, the mean of efficiency score of 21 DMUs is a little more than 50%. Means of departments(8 DMUs) and agencies/committees(11 DMUs) are similar to the total efficiency score. For these results, the decision makers of the information security organizations in public sector have to strive to improve the inefficiency.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.1
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• pp.32-41
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• 2010

DEA(Data Envelopment Analysis) is the relative efficiency measure among homogeneous DMU(Decision- Making Units) which can be used to useful tool to improve performance through efficiency evaluation and benchmarking. However, the general case of DEA was considered as unrealistic since it consists a benchmarking regardless of DMU characteristic by input and output elements and the high efficiency gap in benchmarking for inefficient DMU. To solve this problem, stratification method for benchmarking was suggested, but simply presented benchmarking path in repeatedly applying level. In this paper, we suggest a new method that inefficient DMU can choice the optimal path to benchmark the most efficient DMU base on the similarity among the input elements. For this, we propose a route choice method that combined a stratification benchmarking algorithm and SOM (Self-Organizing Map). An implementation on real environment is also presented.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.61-73
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• 2007

We examined technological forecasting of extended TFDEA(Technological Forecasting with Data Envelopment Analysis) and thereby apply the extended method to the technological forecasting problem of main battle tank. The TFDEA has the possibility of using comparatively inefficient DMUs(Decision Making Units) because it is based on DEA(Data Envelopment Analysis), which usually leads to multiple efficient DMUs. Therefore, TFDEA may result in incorrect technological forecasting. Instead of using the simple DEA, we incorporated the concept of Super-efficiency, Cross-efficiency, and CCCA(Constrained Canonical Correlation Analysis) into the TFDEA respectively, and applied each method to the case study of main battle tank using verifiable practical data sets. The comparative analysis shows that the use of CCCA with TFDEA results in very comparable prediction accuracies with respect to MAE(Mean Absolute Error), MSE(Mean Squared Error), and RMSE(Root Mean Squared Error) than using the concept of Super-efficiency and Cross-efficiency.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.255-262
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• 2013

Data envelopment analysis(DEA) is a linear programming procedure designed to evaluate the relative efficiency of a set of peer entities called decision making units which use the same inputs to produce the same outputs. It has been widely employed in a variety of disciplines as an efficiency or performance measurement tool for comparing a set of entities such as firms, banks, hospitals, nations and organizations. The method, however, cant's make the priority of their performance when many units have efficiency score of unity or 100 percent. In this paper, we propose a new approach which combine qualitative method(graphical approach using network analysis) and quantitative method(super-efficient analysis using DEA), and present the results of an empirical analysis using the data of the Korean professional baseball players. As a result, there were 12 DMU that priority is hardly realized through DEA. However, this problem could be solved with super-efficiency analyzing. Also, more in-depth interpretation was able through integrating results of dendrogram and super-efficiency analyzing and prospecting it in qualitative, quantitative ways.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.1
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• pp.83-90
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• 2000

DEA is a newly developed analyzing tool to measure efficiency evaluation of decision making units (DMU). It compares DMU by radial Projection on the efficient frontier. The purpose of this study is to show reference point approach used for searching solution in multiple objective linear Programming can be usefully used to determine flexible efficient frontier of each DMU In reference point approach, the minimization of ASF Produces an efficient points in frontier and enhances the usefulness of DEA by Providing flexibility in DEA and optimally allocating resources to DMU. Various DEA models can be supported by reference point method by changing the projection direction in order to choose the targets units, standards costs and management benching-marking.