• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.103-106
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• 2008

신재생에너지기술 개발인력 확보는 국가 에너지 안보의 확보 및 지속적인 성장을 가능하게 하는 주요 요인이다. 관련 사업추진에 있어 비효율성의 제거와 인적자원 개발 정책의 효과적인 추진을 위해서 신재생에너지 인력양성 사업에 대한 체계적인 성과평가가 요구되고 있다. 본 연구에서는 신재생에너지 인력양성의 평가를 위하여 적용가능한 방법론을 살펴 보았다. 기존 인력양성 방법론의 구분을 수정하여 인력 양성의 평가 방법을 성과점검, 요인통제분석, 사업의 파급효과 산출방법으로 분류하고 각 기준별 세부방법론을 제안하였다. 성과점검 방식에는 단순환산/일반질문법, 요인통제분석에는 AHP-DEA효율성 측정방법, 인적자본 축적모형방법이 포함될 수 있다. 마지막으로 사업의 파급효과 산출방법에는 산업연관분석 방법, 인력양성 산업연관도 작성 방법이 있다. 제안된 모형 중AHP-DEA효율성 측정방법은 신재생 에너지 인력양성 평가에 적용될 경우 DEA 모형의 객관성이라는 특징을 최대한 이용하는 동시에, 한계로 지적될 수 있는 변별력 분제를 체계적으로 보완하기위한 방편으로 AHP를 도입함으로써 적절한 인력양성 평가 방법론으로 적용될 수 있을 것이다.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.245-251
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• 2006

This is paper aims to evaluate allocation order of SRU using Analytic Network Process. For evaluation, in this paper, assess about person, ship and environment related risk by fuzzy logic and AHP(Analytic hierarchy Process). Also, quantity and quality operation efficiency assess by DEA (Data Envelopment Analysis) and Liquate scale. finally total weight calculate by ANP. At the result, Rescue Units of MP, YS RCC/RSC is order higher. Thus, it needs to have more rescue ships and rescue devices for relieving the risk in the future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.149-161
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• 2012

The current environment of technological and competitive changes influences not only the business R&D environment but also government driven national R&D strategies. Open innovation has now become an important paradigm that is replacing the outdated paradigm of closed innovation. Many companies and nations have been increasing R&D investment because R&D has been considered a driving force for national and corporate competitive advantage. The purpose of this paper is to evaluate and compare the performance of R&D focused on open innovation according to scientific and technological outputs which is based on paper publications, patents and etc. Comparisons should not be only based on the quantity but also on the quality of the output. This paper shows that it is possible to develop DEA models that utilize the Analytical Hierarchical Process in order to transform the qualitative index into a quantitative index. Hence, the relative efficiency for R&D organizations is obtained based on both quantity and quality outputs and subsequently provides comprehensive and realistic methods for decision makers to identify levels of project efficiency.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.29-37
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• 2009

National R&D programs play an important role in the development of a country in this age of the knowledge economy. Since many numbers of R&D programs compete for limited resources such as national R&D budget, the R&D program evaluation problem is a challenging decision-making problem faced by decision makers that deal with R&D management. In this sense, DEA(Data Envelopment Analysis) has been regarded as one of the most widely accepted methods to measure the relative efficiency of productivity of R&D programs. DEA is a methodology to measure and to evaluate the relative efficiency of a homogeneous set of decision-making units(DMUs) in a process which uses multiple inputs to produce multiple outputs. However, the sample of the R&D programs could consist of two or more naturally occurring subsets, thus exhibiting clear signs of heterogeneity such as different objectives. In such situations, the fairness of DEA is limited, for the nature of the relative efficiency of a DMU is likely to be influenced by its membership in a particular subset of the sample. In this study, we propose a methodology AI-DEA(attainment index DEA) allowing for reflecting decision maker's subjective judgement on difference among different subsets of R&D programs which have heterogeneous objectives. This methodology combines AHP and Delphi in order to decide the attainmnet index of each DMU for each outputs, and apply them to DEA model. We illustrate the proposed approach with a pilot evaluation of 13 programs involving 6 different subsets of Korean National R&D programs and compares the results of the original DEA model and AI-DEA model.

• Journal of Information Technology Services
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• v.8 no.2
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• pp.173-187
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• 2009

CASE tools are complex software products offering many different features. Systems professionals have evaluated various CASE products from a feature and attribute basis. Each product has a different mix of strengths and weaknesses as perceived by the end user. Specific CASE tools support different steps of the applications development process as well as varying methodologies. In this paper we develop a method for evaluating CASE tools. The model has an analytic hierarchy process for evaluating CASE tools in terms of functionality, management efficiency, and support ability of provider, and a data envelopment analysis for overall evaluation considering cost and AHP results. We applied the developed model to a real world case study.

• Journal of Information Technology Services
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• v.12 no.4
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• pp.255-265
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• 2013

This paper proposes a DEA-AR model for the efficiency evaluation of the iron ore brands in an integrated steel mill. The input factor is defined as unit cost of each brand based on CIF and two output factors are chosen as Fe and Al which are the important ingredients of iron ore. The relative importance between two output factors is determined by several experts using AHP model. The efficiency of each brand is determined using DEA and DEA-AR models. The negative correlation between the DEA-AR efficiency and the unit cost (CIF) is shown as significant whereas no significant correlation exist between the efficiency and the output factors. Also, the Kruskal Wallis rank sum test shows that there exist efficiency differences among the iron ore types whereas no difference is shown among the countries. The result could be utilized in selecting good brands of iron ores based on the DEA-AR efficiency in an integrated steel mill.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.6
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• pp.921-930
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• 2013

From an economic perspective, an enterprise's business activity depends on the efficient use of corporate resources for generating profits. However, on the enterprise side, it is difficult to measure and evaluate the effective use of each resource. This paper suggests an alternative for eliminating process inefficiencies in the consolidation of competitive power in auto parts manufacturing company A. Multitudinous process variables from company A's raw materials-to-shipment process are configured as input resources, and a Data Envelopment Analysis(DEA) is carried out to determine economical benefit of said resources' operation, as well as how products are manufactured. The DEA model offers a non-parametric approach to measuring relative efficiency using input and output factors. Furthermore, AHP is used for logically deciding the importance of each evaluation factor. In general, DEA models have been used for measuring efficiency of the service and public sectors. However, this study focused on measuring the efficiency of SMEs production lines.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.1-10
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• 2008

The enormous budget of government and manpower are invested to the government funded institutes every year. The R&D investment focused on input has to be turned toward the investment based on the effectiveness of R&D activities. Measuring the efficiency of research activities is required in order to evaluate the effectiveness of R&D investment in these institutes. The purpose of this paper is to evaluate the relative efficiency of research activities performed in 19 government funded research institutes. CCR/BCC model and DEA/AR model were applied to get the relative efficiency of 19 institutes. Assurance regions for the weight of output attributes were obtained by using the underlined concept of the analytic hierarchy process (AHP). We used input and output data items describing research activity of 19 government funded research institutes. The results of this study are expected to become a basis of the R&D investment decision of the government.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.478-478
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• 1999