• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.97-104
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• 2015

In this paper, we propose a data envelopment analysis (DEA) and analytic hierarchy process (AHP) integrated model to improve the selection process in the acquisition of a weapon system which is the key component to the success of the project. In particular, we applied DEA in the first stage to choose a frontier group among the candidates in the selection process of the next-generation fighter system (the 3rd FX) in Korea. Then, by using the Delphi technique, we surveyed military experts and applied AHP to determine the best choice among the candidates. The results of the study match the actual decision made by the Korean government in the weapon system acquisition. The results of the proposed DEA-AHP integrated method in the selection of the next-generation fighter systems in Korea demonstrate the usefulness of the method. In this paper, we also discuss the future implications of the proposed model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.4
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• pp.101-111
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• 2013

In this study, DEA (Data Envelopment Analysis) was carried out on the four bridges, which have the same extension (L=1,615m), in order to select the most superior, economical method of construction using the LCC concept of each bridge structure in the case of the Ulsan-Pohang double track railway which is scheduled to be constructed. DEA models were analyzed with the CCR model, which was designed for the evaluation of relative efficiency of each model. The initial construction costs, maintenance costs, indirect costs (user costs + indirect loss of social costs), and life cycle costs were used as input variables, and average duration was applied as the output variable. LCC was applied to calculate the input variables, and to get the costs of LCC, 100 years of period and 4.83% of real discount rate were applied, and the costs are classified into initial construction, maintenance, user, and indirect loss of social cost. The analysis results showed that the Method 2 and 3 were evaluated as the most efficient, and the other alternatives were evaluated as the following order; Method 1, the default, and Method 4.

• 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 Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1389-1398
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• 2009

This research suggests the calculation and analytic method of determining the proper numbers of manual workers with simulation and DEA methods. For this, first of all we designed and analysed the simulation model of real facility being built in Dea-Jun. Secondly, we find the solution with DEA among various alternatives built based on the results of the simulation analysis. In the simulation process, the waiting time, the number of painted trains, and utilization of main equipment are considered as main response variables. After simulation, we built DMUs(Decision Making Unit) consisting of combined results with the manpower assignment policies, the numbers of workers, and the rate of skilled operators, and calculated the efficiency of DMUs with the DEA method. Among 27 DMUs, 4 DMUs turned out efficient technically and on scale. In conclusion, through the suggested procedure, this research shows the way that decision makers can solve the problems with various factors they should concern, along the scientific process, when building a new facility.

• Journal of Korea Port Economic Association
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• v.21 no.1
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• pp.133-151
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• 2005

The purpose of this paper is to investigate the international competition power between Korean ports and Chinese ports according to the port efficiency scores of DEA(Data Envelopment Analysis) by newly introducing the priority vector of AHP(Analytic Hierarchy Process) to the DEA method. Empirical analysis shows the followings: First, there was not big changes of DEA rankings when we use the input-oriented CCR and BCC models after introducing the AHP priority vectors to the input variables. Yantian Port's competition power was declined, but that of Busan Port was up in the BCC model. Second, there was some changes of DEA rankings when we use the output-oriented CCR and BCC models after introducing the AHP priority vectors to the output variables. Rankings of Dalian, Qingdao, Shanghai Ports were up. But Shekou, Yantian Ports showed the declined ranking position in the CCR model. In the BBC model, rankings of Shanghai and Busan Ports were up. But those of Shekou and Yantian Ports were declined. The main policy implication based on the findings of this study is that The Ministry of Maritime Affairs & Fisheries in Korea and China should introduce AHP and DEA approaches when they measure the international competition power by using the porrt efficiency scores of DEA.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.279-289
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• 2011

The purpose of this study is to estimate efficiency of item-based agricultural cooperative by using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of item-based agricultural cooperative is 0.80, 0.87, 0.93 respectively. However the bias-corrected estimates are less than those of DEA. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.726 and 0.8747. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.239-244
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• 2014

The conventional clustering approaches are mostly based on minimizing total dissimilarity of input and output. However, the clustering approach may not be helpful in some cases of clustering decision making units (DMUs) with production feature converting multiple inputs into multiple outputs because it does not care converting functions. Data envelopment analysis (DEA) has been widely applied for efficiency estimation of such DMUs since it has non-parametric characteristics. We propose a new clustering method to identify groups of DMUs that are similar in terms of their input-output profiles. A real world example is given to explain the use and effectiveness of the proposed method. And we calculate similarity value between its result and the result of a conventional clustering method applied to the example. After the efficiency value was added to input of K-means algorithm, we calculate new similarity value and compare it with the previous one.

• 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.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.189-199
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• 2008

It is good to use DEA method as it can measure the efficiency without depending on a specific function like cost function. The method also finds out the most efficient group among the sample groups and gives us a specific number. For example, it shows what kind of factor of inefficient group gives how much input and produces how much output. Originally DEA, which was developed by Charnes, Cooper and Rhodes, allows us not only to measure the relative efficiency of Decision Making Units(DMUs) of non-profit organizations whose success cannot be measured by a single bottom-line figure such as profit but also to integrate several variables, which have different measuring scale, into a single model. Therefore we can use physical scales and financial scales simultaneously in the same model without any transformation process. In this study, price and measurable performance indexes of vehicles are used as input and outputs respectively. The purpose of this study is to propose an effective approach for evaluating the relative efficiency of vehicles and to determine the vehicles have high performance efficiency compared to product cost.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.899-904
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• 2013

This paper proposes an efficient portfolio selection methodology for the listed construction corporations in KOSPI and KOSDAQ. For the construction industrial sector classified by KRX(Korea Exchange), the proposed method carries out an efficiency analysis using DEA (Data envelopment analysis) approach and for the efficient corporations filtered by DEA, construct portfolio using Markowitz's Model. In order to show the effectiveness of the proposed method, we constructed annually portfolios for 5 years (2007-2011) out of 53 listed corporations in KOSPI and KOSDAQ, and proved that our portfolios are superior to benchmark portfolios in terms of rate of returns.