• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.143-149
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• 2021

This paper proposes length rate measurement for relative efficiency that is a core of data envelopment analysis(DEA). It has been said that the linear programming(LP) is a unique method to get the relative efficiency. This method has drawback that applies fractional LP focusing on each DMU in turn. This paper draws bi-dimensional input-output relational graph and distinguishes between efficient and inefficient DMU. The relative efficiency of inefficient DUM is solve using length rate measurement. As a result of various experimental data, the LP shows mistake of application, but this method gets the correct relative efficiency at all times. Also, this method only gets the relative efficiency for only inefficient DMUs without efficient DUMs that already achieved 100% efficiency. This method solves the relative efficiency of inefficient DUM draws the line to efficient frontier and decides the reference set easily.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.1-14
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• 2021

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

• Convergence Security Journal
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• v.23 no.2
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• pp.95-102
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• 2023

The purpose of this paper was to confirm the operational level of the military department of universities, which plays a key role in the officer training process, through empirical research and confirm meaningful results for improvement. There are 11 university military departments operated through the Army, agreements, and semi-agreement, and the Data Envelopment Analysis (DEA) was applied from the perspective of resource input and performance for each university's military department operation to analyze relative efficiency and confirm specific directions for improvement. As a result of operational efficiency analysis, 6 DMUs (Decsision Making Unit) were found to be efficient in the BCC model out of 11 DMUs, and the evaluation results could be confirmed through classification of efficient and inefficient groups through data capture analysis. This paper may be of practical value in that it checks the efficiency of the comparative university military departments and confirms specific information for development through the DEA-Additive model that reflects several evaluation factors at once. Through this, the operators of each university's military department are admitted.

• Asian Journal of Innovation and Policy
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• v.13 no.1
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• pp.1-28
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• 2024

Enhancing the efficiency of research and development (R&D) is crucial for organizations to remain competitive and generate innovative solutions. Data Envelopment Analysis (DEA) has emerged as a powerful tool for evaluating R&D efficiency. However, traditional DEA models heavily rely on the selection of input and output variables, which can limit their effectiveness. To overcome this dependency and improve the robustness of DEA, this study proposes a novel methodology that integrates machine learning techniques with DEA for determining the most suitable input and output variables. The proposed approach is particularly relevant for specialized R&D fields, such as Radiation Emergency Medicine (REM). REM is a critical domain that deals with the medical and public health consequences of nuclear emergencies. The selection of REM as the focus of this study is motivated by several factors, including the unique challenges posed by the field, the potential for significant societal impact, and the need for efficient resource allocation in emergency situations. By leveraging machine learning algorithms, such as Support Vector Machines (SVM), the proposed methodology aims to identify the most relevant input and output variables for DEA in the context of REM. The integration of machine learning enables the DEA model to capture complex relationships and non-linearities in the data, leading to more accurate and reliable efficiency assessments. The effectiveness of the proposed methodology is demonstrated through a comprehensive evaluation using real-world REM data. The results highlight the superior performance of the machine learning-integrated DEA approach compared to traditional DEA models. This study contributes to the advancement of R&D efficiency assessment in specialized fields and provides valuable insights for decision-makers in REM and other critical domains.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.255-260
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• 2007

The construction industry has been recognized as lagging behind other industries in the area of research and development (R&D) due primarily to the lack of R&D funds. To overcome this situation, the Korea Ministry of Construction and Transportation has a plan to expand its investments on the construction R&D up to 1.72 billion US dollars over the next 3 years. Despite this effort, it is still challenging to conduct the quantitative measurement on the efficiency of construction R&D projects, which can be utilized as the objective basis in the reasonable selection of a promising R&D team or in the consequential evaluation of a R&D performance. This study aims to conduct the efficiency analysis on the construction R&D projects to provide the basis for evaluating the performance of research and development. Toward this end, this study performs an efficiency analysis, which reflects all of the input and output data into/from the construction R&D by utilizing the Data Envelopment Analysis (DEA). The proposed methodology can be utilized to make a better decision on the priority of the R&D investments and present a sound basis to suggest the areas to be improved so as to reduce the inefficiency of R&D projects.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.151-151
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• 2000

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.87-96
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• 2007

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.465-466
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• 2010

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.254-254
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• 2017

• Safety and Health at Work
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• v.6 no.2
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• pp.77-84
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• 2015

Background: A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented. Methods: To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data. Results: The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs. Conclusion: The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.