• Journal of the Korea Society for Simulation
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• v.13 no.4
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• pp.17-29
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• 2004

The focus of this study is to represent a methodology of analysis for integrated logistic system by means of the Evolution Strategies and Data Envelopment Analysis(DEA). The integrated logistic system is composed of AS/RS (Automated Storages and Retrieval System), AGVs(Automated Guided Vehicle System) and Conveyor System. We design the simulation alternatives with choosing the qualitative critical factors for the each subsystem. Evolution Strategies is used to optimize the quantitative critical factors and responses of each alternative. DEA is applied to measure the efficiency of the alternatives in order to select the optimal operation efficiency scheme. The method of analysis which combines Evolution Strategies with DEA can be used to analyze the qualitative and quantitative critical factors in the integrated logistic systems.

• The Journal of Fisheries Business Administration
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• v.41 no.3
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• pp.45-58
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• 2010

This study estimated the technical efficiency of coastal composite fishery in Korea by using the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA) methods, and the results on the respective method were compared. In the DEA method, the constant returns to scale (CRS) and the variable returns to scale (VRS) output-oriented DEA models were separated and technical efficiencies were estimated, respectively. The average estimated value of technical efficiency by the SFA method (0.633) was found to be lower than that by the VRS-DEA method (0.738), while it was higher than that by the CRS-DEA method (0.479). It was found that strong correlation exists between the SFA method and the VRS-DEA method. The method which can utilize both methods in mutually complementing way for the estimation of technical efficiency was also considered.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.210-220
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• 2014

This article describes a 2-in-1 methodology utilizing simulation optimization technique and Data Envelopment Analysis in measuring an accurate efficiency score. Given the high level of stochastic data in real environment, a novel methodology known as Data Collection Budget Allocation-Data Envelopment Analysis (DCBA-DEA) is developed. An example of the method application is shown in banking institutions. In addition to the novel approach presented, this article provides a new insight to the application domain of efficiency measurement as well as the way one conducts efficiency study.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.854-855
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• 2007

Analyzing the productivity has been widely applied in economic analysis and of the key concept of utility regulation. In this paper we use benchmarking technique known as Data Envelopment Analysis (DEA) model to analyze the productivity. And we show that from the result it is possible to evaluate the performance of inefficient firms by reducing their input levels.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.679-684
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• 2007

This paper proposes a scheme to estimate the technical efficiency of trucks in logistics as performance measure by Data Envelopment Analysis (DEA). The result of technical efficiency estimation shows that there exists a substantial opportunity for improvement in technical efficiency of trucks and also the heterogeneity in the technical efficiency among trucks.

• Korean Management Science Review
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• v.17 no.2
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• pp.161-174
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• 2000

This paper provides an implementation of data envelopment analysis (DEA) developed by Charnes et al. using SAS. Since a flexible interactive matrix language SAS/IML included in the SAS has a syntax similar with the matrix algebra, one can easily create and understand SAS/IML code for DEA. In this paper, a simple SAS/IML code for DEA and its illustrative implementation with an input-output data set of 25 American private university research libraries are provided.

• Korean Journal of Organic Agriculture
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• v.24 no.3
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• pp.385-412
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• 2016

This paper analyzes economic efficiency of strawberries and tomatoes farms using the data envelopment analysis (DEA). We examine how production characteristics and farm size affect overall efficiency (OE), allocative efficiency (AE), and technological efficiency (TE). We also survey sample farm households from the Farm Income Investigation database of Rural Development Administration to investigate management and cultivation techniques and to analyze economic efficiency by technique. Implications for improving efficiency are suggested in the conclusion.

• International Journal of Quality Innovation
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• v.2 no.1
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• pp.1-9
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• 2001

As all the other managerial activities, total quality management (TQM) has also inputs into and outputs from the process. Therefore, the principal managerial efficiency criteria of maximum outputs with minimum inputs should be applied to TQM. In this paper, the methodology for the performance evaluation of TQM by Data Envelopment Analysis (DEA) was proposed. DEA is used to measure the efficiency of TQM for each firm with the input and output data obtained by questionnaire. It is found that there are not significant differences between the firms with and without IS09000 certification and between the large and small-sized firms with respect to the TQM efficiency.

• 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.

• Korean Journal of Organic Agriculture
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• v.22 no.4
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• pp.581-591
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• 2014

This study analyze the policy efficiency of the environment-friendly farming zone program using data envelopment analysis. On analyzed results, the average policy efficiency is 0.185 and 98.3% of zones are inefficient. 97.8% of zones are in increasing returns to scales. The empirical results indicate that more work need to be done to increase the efficiency of the program.