• Proceedings of the KOR-KST Conference
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• 2007.02a
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• pp.97-105
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• 2007

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.47-53
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• 2009

We define product line (or mix) selection problem as selecting a subset of potential product variants that can simultaneously minimize product proliferation and maintain market coverage. Selecting the most efficient product mix is a complex problem, which requires analyses of multiple criteria. This paper proposes a method based on Data Envelopment Analysis (DEA) for product line selection. Data Envelopment Analysis (DEA) is a linear programming based technique commonly used for measuring the relative performance of a group of decision making units with multiple inputs and outputs. Although DEA has been proved to be an effective evaluation tool in many fields, it has not been applied to solve the product line selection problem. In this study, we construct a five-step method that systematically adopts DEA to solve a product line selection problem. We then apply the proposed method to an existing line of staplers to provide quantitative evidence for managers to generate desirable decisions to maximize the company profits while also fulfilling market demands.

• 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.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.57-74
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• 2023

It has been pointed out as a limitation that the rank of some efficient DMUs(decision making units) cannot be discriminated due to the relativity nature of efficiency measured by DEA(data envelopment analysis), comparing the production structure. Recently, to solve this problem, a DEA-SNA(social network analysis) model that combines SNA techniques with data envelopment analysis has been studied intensively. Several models have been proposed using techniques such as eigenvector centrality, pagerank centrality, and hypertext induced topic selection(HITS) algorithm, but DMUs that cannot be ranked still remain. Moreover, in the process of extracting latent information within the DMU group to build effective network, a problem that violates the basic assumptions of the DEA also arises. This study is meaningful in finding the cause of the limitations by comparing and analyzing the characteristics of the DEA-SNA model proposed so far, and based on this, suggesting the direction and possibility to develop more advanced model. Through the results of this study, it will be enable to further expand the field of research related to DEA.

• 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 Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.117-131
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• 2015

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.125-126
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• 2012

Domestic architect office from a period of high growth from the 1970s to the '80s has been established as some of the large corporations or publicly traded corporation. 1997 IMF has pointed out there is a lot of need for improvement activities in accordance with the construction recession since the 2008 global financial crisis. In order to address these causes, the company's continuous efficient operation for accurate efficiency and competitiveness analysis was required. Leverage financial ratio indicators Study Using Data Envelopment Analysis Data Envelopment Analysis (DEA) model, how to find a benchmark for the improvement of the efficiency of inefficient enterprises in various sectors being. In this study, a comparison of the conventional DEA model and the DEA-AR model is used to analyze the efficiency and domestic architect office is to improve the management efficiency.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.5
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• pp.727-737
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• 2007

The aims of this study was to suggest a new efficiency measurement indicator for evaluating the management efficiency of decision making units(DMUs) in the contract foodservice industry. The data envelopment analysis(DEA) model which considers multiple inputs and outputs and looking for benchmarks, was used to compare the productivity of DMUs. We considered sales, profits, and customer satisfaction as output variables and it adopted food cost, labor cost and administrative expense as input variables. The results of applying DEA revealed relatively efficient types of business and service types. The efficiency of school units was highest and the mired service type was the most efficient one. In this study the CCR model efficiency was analysed with profit and the customer satisfaction index by the matrix method. DEA efficiency was correlated with profit but there was no correlation between DEA efficiency and the customer satisfaction index.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.3
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• pp.59-68
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• 2010

Improving productive efficiency is important for strengthening a competitiveness of coastal fisheries. This paper examines the productive efficiency of a sample of coastal gillnet fishing business units by estimating a stochastic frontier analysis (SFA) and a data envelopment analysis (DEA) approaches and compares those estimates obtained from two approaches. The estimated mean productive efficiency by SFA is 77.6% and the mean productive efficiencies obtained for the VRS and CRS DEA are 75.9% and 45.7%, respectively. The joint use of SFA and DEA for estimating efficiency is also discussed.

• 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.