• Health Policy and Management
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• v.6 no.2
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• pp.1-13
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• 1996

To evalate the efficiency of public and private hospitals, the author used Data Envelopment Analysis(DEA), a mathematical linear programming method calculating the of ficiency of a unity(DMU: Decision Making Unit) in relation to the other units in analysis. DEA was applied to thirty three (10 public and 23 private) general hospitals wiwith 160 to 299 beds. In respect to productivity, public hospitals appeared to be a little more efficient than private ones, even though it's statisticansignificant. However, the efficiency score for profitability conversed that these contrary results were due to the caring of more medical protection patients in public hospitals, who brought less revenlue to te hospital than other patients. Public hospitals' superiority to private counterparts in productivity, which are aguged mainly based on cared patients, suggests that the former contributes so much positively to social utility. In particular, the fact that public hospitals are caring more medical protection patients, namely the poverty group whom the society should bear a burden of by all means, seems to be desirable in respect of role of publi hospitals.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.188-197
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• 2012

DEA 모델중 널리 사용되는 교차평가모델(cross efficiency model)은 가중치에 제한을 두지 않고 어떤 특정분야에 탁월한 성과를 내는 DMU(Decision Making Unit)보다는 보다 전반적인 분야에서 두각을 나타내는 DMU를 선발함으로써 많은 연구자들이 DEA문헌에서 적용하여 왔다. 본 연구에서는 이러한 교차평가모델이 실제에 있어서는 암묵적으로 고정 가중치를 사용한다는 것과 동일한 결과를 나타낸다는 것을 분석적으로 밝혔다(one input, multi output case). 또한 multi-input, multi-output case의 경우에도 overall performer의 cluster에 근접한 대다수 DMU의 경우에는 고정 가중치를 사용한 경우와 거의 차이가 없음을 보였다. 교차평가 모델에 적용된 변수의 가중치를 보다 명확히 함으로써 연구자들이 모델의 평가결과를 이해하는데 도움이 될 수 있을 것이다. 또한 교차 평가의 가중치 도식을 더 명확히 보여주기 위해 biplot을 제안한다.

• 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 applied mathematics & informatics
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• v.14 no.1_2
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• pp.173-183
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• 2004

Data envelopment analysis(DEA) is a mathematical programming approach to asses s relative efficiency of a group of decision-making units. In view of the defects of existing models in evaluating efficiency of the system with P independent subsystems, Yang et al. [10] introduced YMK model with the assumption that decision-making unit(DMU) is independent of each other. But in some production systems, decision-making units usually have some relationships in this way or that. In this paper, DEA model is given by assuming that DMUs can cooperate with others in its subgroups. Some property and the efficiency relationship of the whole system and its subsystems are given.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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• pp.117-126
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• 2008

Recently, the investment and importance have been increasing concerning the researches which are based on fundamental studies. In defense science and technology development, the defense research centers are involved in a large portion of developing the potential capability such as defense applied technology, enhanced human resource, etc. In this paper, we analyzed the relative efficiency of 9 research centers(9 DMU : Decision Making Unit) supported by the defense budget, using DEA(Date Envelopment Analysis) method especially with the CCR-I(Charnes, Cooper, Rhodes-Input) model. Some variables are selected such as budget(input data), patent, article and human resource(output data) to be analyzed. Conclusively, the needs to identify performance-indicators, increase incentives to promote the performance and induce enthusiastic participation in defense science and development projects, are suggested via a relative efficiency analysis.

• Health Policy and Management
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• v.11 no.2
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• pp.58-84
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• 2001

This study was conducted to explore the relationship between the efficiency and its organizational determinants of tile Community Mental Health Centers(CMHCs). Data are obtained from 81 personnel of 27 CMHCs from Sept. to Oct. in 1999(19 in Kyunggi province, 7 in Seoul City and 1 in Chunchon, Kangwon province). Major findings of this study are as follows. 1. DEA is a mathematical programming technique that optimizes the relative efficiency ratio of inputs over outputs for each decision-making unit(DMU). It produces a summary scalar efficiency ratio for each DMU of CMHCs. It assessed multiple inputs and multiple outputs simultaneously, and compared to specific peer group of CMHCs. 2. Organizational determinants of DEA efficiency of CMHCs we proved as advertisement(＋), location of CMHCs(in public facility)(＋), area of facility(＋), period of operation(＋), job satisfaction(＋), clarity of work-role(vague), cohesion(－), rate of certified personnel(＋), number of referral(＋), and voluntary service time(－).

• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.639-645
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• 2014

This study was conducted to evaluate the management efficiency of oak mushroom farms in Korea using the Data Envelopment Analysis (DEA), which is one of the non-parametric estimation methods. The data that was analyzed in this study was from the result of 2013 survey entitled "Standard Diagnostic Table for Oak Mushroom Management", which was conducted from March 2012 to October 2012. This survey was based on the inputs and outputs of 20 oak mushroom farms. Specifically, this study analyzed the technical efficiency, pure-technical efficiency and scale efficiency using CCR and BCC model of the DEA methods. Furthermore, this study compares the management efficiency between the full time oak mushroom production farms and part time oak mushroom production farms. Results showed that mean value for the technical efficiency was 0.655 which is considered as inefficient in general. For the pure-technical efficiency and scale efficiency, the mean values were 0.830 and 0.747, respectively which showed that inefficiency in the management was observed in the mushroom farms. Results also showed that there were seven farms with a total efficiency of 1, namely Decision Making Unit(DMU)2, DMU5, DMU6, DMU8, DMU10, DMU15 and DMU20. The management efficiency of DMU7 specifically the inputs for production was analyzed and compared to DMU5 and DMU6 and results showed that the DMU7 had an excessive inoculation and site development cost. Lastly, it was also observed that the full time mushroom production farms were more efficient as compared to the part time mushroom farms because of the lower scale efficiency value or smaller area for mushroom production allotted in the part time farms.