• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.365-373
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• 2000

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications and merits, some features of DEA remain bothersome. DEA offers no guideline about to which direction relatively inefficient DMUs improve since a reference set of an inefficient DMU, several efficient DMUs, hardly provides a stepwise path for improving the efficiency of the inefficient DMU.In this paper, we aim to show that DEA can be used to evaluate the efficiency of life insurance companies while overcoming its limitation with the aids of machine learning methods.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.359-369
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• 2014

Due to the severe market conditions, pre-entrepreneur seeks to start their business through franchise company. This paper, using the data envelopment analysis(DEA) method, examines efficiency of a group of franchise company in order to provide efficiency information with pre-entrepreneur. Output-oriented DEA model is applied in the investigation of efficiency, and the overall efficiency score is decomposed into pure technical efficiency and scale efficiency. The input variables selected to evaluate the efficiency are franchise deposit, franchise contribution cost and the output variables are sales and number of franchises, and length of business. The results of this paper show franchise industry have the low level of overall efficiency and the main sources of inefficiency is found technical rather than scale. As a result, this paper provides not only the current status of efficiency information of a franchise with pre-entrepreneur but also give warning when they sign-up with franchise business.

• Health Policy and Management
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• v.6 no.2
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• pp.91-114
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• 1996

In this research, the Data Envelopment Analysis(DEA) was applied to measure production efficiency of Public Corporation Medical Centers(PCMCs) operating in Korea. The focus of this research is triple. First, identifing convenience and usefulness of DEA to measure the relative efficiency among PCMCs. Second, assessing magnitudes of the relative efficiency for each PCMC. Third, adding insights into some factors resulting inefficiency in PCMCs. Then, in this paper technical efficiency and scale efficiency measured by DEA[introduced by Charnes, Cooper, and Rhoides(1978) and Banker, Charnes, and Cooper(1984)] were analyzed and a new separate variable was introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decresing returns to scale(in multiple input and output situations). And a multi-factor Tobit analysis was conducted to see which variables are associated with PCMC's efficiency.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.2
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• pp.189-196
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• 2011

Recently, research and development of a hybrid system for passenger cars as well as for heavy-duty vehicles has become more intensive. An electric powertrain system using an electric motor can replace conventional gasoline and diesel engines. The electric motor has a higher efficiency, better acceleration performance, and is more comfortable than conventional powertrain systems; however, new methods for assessing power performance and energy convergence efficiency have to be investigated because the characteristics of an electric motor are entirely different from those of an internal combustion engine (ICE). In this study, an experiment was carried out on a motor (PMSM: Permanent Magnet Synchronous Motor) test bench. One simple driving mode and four other driving modes identified from real-world driving data of a city bus were selected to perform the experiment on the motor test bench. Then, methods for assessing the acceleration performance, energy convergence efficiency, regenerative effect, etc., were investigated. It was found that the energy efficiency of PMSM was about 90% and that 40% of demand energy was regenerated.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.150-150
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• 2019

• Asia pacific journal of information systems
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• v.10 no.1
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• pp.19-35
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• 2000

Data Envelopment Analysis(DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.77-88
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• 2006

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.327-332
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• 2010

In this paper we presents an approach to assessing the efficiency of freight railroad stations reflecting freight item importance weights with multiple performance indicators and multiple operational condition indicators. We evaluate 187 freight railroad stations using data envelopment analysis with assurance region. Each freight item's loading/unloading volume is used as a performance indicator. Freight labor and yard capacity are used as operational condition indicators. Freight item importance weights are reflected to the data envelopment analysis as assurance region. The evaluation results facilitates the organization's decision making by providing valuable information.

• Journal of Ocean Engineering and Technology
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• v.31 no.3
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• pp.183-188
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• 2017

This paper provides numerical results for the estimation of the efficiency of KRISO energy saving devices in the design stage. A finite volume method is used to solve Reynolds averaged Navier-Stokes (RANS) equations, where the SST k-$\omega$ model is selected for turbulence closure. The propeller rotating motion is determined using a rigid body motion (RBM) scheme, which is called a sliding mesh technique. The numerical analysis focuses on predicting the power reduction by the designed KRISO devices (K-DUCT) under a self-propulsion condition. The present numerical results show good agreement with the available experimental data. Finally, it is concluded that CFD can be a useful method, along with model tests, for assessing the performance of energy saving devices for propulsion efficiency improvement.

• IE interfaces
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• v.11 no.1
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• pp.23-32
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• 1998

This paper presents a model for assessing the efficiency of logistics activities in distribution centers. The DEA approach is adopted to compare the relative efficiency of distribution centers, where considered as input and output factors are warehouse floor area, field storage area, average inventory level, overhead costs, number of workers, number of orders, and total value of goods handled. The artificial neural network approach is also adopted to overcome the limitation of DEA. The 12 distribution centers of Korea Telecom are studied for the validation of the model, which results in 84.9% of learning accuracy. This model can be used to identify the inefficient factors in a distribution center and to reveal changes in the degree of efficiency over time.