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.

Evaluating the operational efficiency of electric vehicle charging stations (EVCSs) is important to understand charging network evolution and the charging behavior of electric vehicle users. However, aggregation of efficiency performance metrics poses a significant challenge to practitioners and researchers. In general, the operational efficiency of EVCSs can be measured as a complicated function of various factors with multiple criteria. Such a complex aspect of managing EVCSs becomes one of the challenging issues to measure their operational efficiency. Considering the difficulty in the efficiency measurement, this paper suggests a way to measure the operational efficiency of EVCSs based on data envelopment analysis (DEA). The DEA model is formulated as constant returns of output-oriented model with five types of inputs, four of them are the numbers of floating population and nearby charging stations, distance of nearby charging stations and traffic volume as desirable inputs and the other is the traffic speed in congestion as undesirable one. Meanwhile, the output is given by the charging frequency of EVCSs in a day. Using real-world data obtained from reliable sources, we suggest operational efficiencies of EVCSs in Seoul and discuss implications on the development of electric vehicle charging network. The result of efficiency measurement shows that most of EVCSs in Seoul are inefficient, while some districts (Nowon-gu, Dongdaemun-gu, Dongjak-gu, Songpa-gu, Guro-gu) have relatively more efficient EVCSs than the others.

The purpose of this study is to analyze the efficiencies of project management offices in large information system construction projects using the data envelopment analysis. In addition, we tried to estimate the confidence interval of those efficiencies using bootstrap DEA to give a statistical meaning. The efficiency by the CCR model is analyzed as eight project management offices are fully efficient and 22 project management offices are inefficient. On the other hand, there are 15 project management offices are fully efficient, but 15 project management offices are inefficient in the BCC model. As the result of the scale efficiencies, of the inefficient project management offices, 13 project management offices are inefficient in scale. It is possible to eliminate the inefficiency in the CCR model by improving their project performances. And, the nine project management offices showed that the inefficiency was due to pure technical efficiency, and these companies should look for various improvements such as improvement of project execution system and project management process. In order that the inefficient project management offices be efficient, it is analyzed that more efforts must be made for on-budget and on-time as a result of examining the potential improvement potentials of inefficient project management offices.

This paper was to evaluate social enterprises’ management efficiency with Data Envelope Analysis (DEA). The data was based on the 168 social enterprises’ of annual performance reports published in 2015. The research focused on to measure both financial efficiency and social impact of the companies simultaneously. To apply DEA, the paper classified the enterprises into seven types based on types of socal impacts which each company provides before the estimation of the efficiency. The research results showed that group D, which employes disadvantaged people, provides social services and shares resources was the most efficient group and had higest net worths in Pure Technical Efficiency. In contrast, Group B, which only employs social advantage people and provides social service, was the least efficient one. The research suggests a practical and efficient framework in measuring social enterprises’ management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. Because the Korea Social Enterprise Promotion Agency does not open business reports which social enterprises submit each year, there are basic limitations on researchers attempting to analyse with data from all social enterprises in Korea. Thus, this study dealt with only 10% of the social enterprises which self-published their performance report on the Korea Social Enterprise Promotion Agency’s web site. Regardless of these limitations, this study suggested substantial methods to estimate management efficiency with the self-published reports. Because self-publishing is increasing each year, it will be the main source of information for researchers in examining and evaluating social enterprises’ financial performance or social contribution. The research suggests a practical and efficient framework in measuring social enterprises’ management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. The research results suggest not only list of efficient enterprises but also methods of improvement for less efficient enterprises.

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.

정보화 사회의 도래에 따른 정보통신기술의 발전과 활용이 국가 경제구조 및 성장에 급격한 영향을 미치고 있는 추세에 맞춰 본 연구는 자료포락분석과 맘퀴스트지수를 활용하여 국가별 정보통신기술의 활용성과를 상대적 효율성 및 생산성의 관점에서 접근하고자 하였다. 투입요소로 ICT환경과 ICT이용준비도 그리고 산출물로 ICT활용도를 이용하여 총 28개 국가를 대상으로 2008년부터 2011년 동안 정보통신기술 활용성과를 진단한 결과, 자료포락분석에서는 전체적인 ICT 효율성이 감소한 것으로 나타나 외형적 성장에 비해 실질적인 ICT 활용 부문은 부진한 것으로 판단되었고, 맘퀴스트지수 분석결과에서도 전체적인 ICT 생산성은 지난 3개년 구간동안 개선되지 않은 것으로 분석되었다. 이러한 분석결과를 종합적으로 고려해 볼 때, ICT 활용성과를 제고하기 위해서는 지금까지 추진해온 물리적 요소의 양적 투입에 의존하는 외형적 개발정책보다는 투입요소와 산출물을 합목적적으로 연계시키고 ICT 활용 효율성을 증진시킬 수 있는 다각적인 운영 합리화 방안이 필요하다고 본다.

이 논문에서는 부트스트래핑 DEA 모형을 이용하여 품목농협의 효율성 분석 값의 통계적 유의성을 분석 하였다. 분석결과, 첫째, 일반적 DEA모형에 의한 기술효율성은 0.878로 추정된 반면, 부트스트래핑 기 법을 적용하면 0.804로 추정되었다. 그러나 두 값의 차이는 신뢰구간 범위 내에 있기 때문에 통계적으로는 유의하지 않다. 또한 95% 유의수준하에서 기술효율성의 통계적 신뢰수준은 0.726에서 0.874로 분석되었다. 둘째, 일반적 DEA모형에서 효율적인 품목농협으로 추정된 19개 농협 모두 부트스트래핑 기법을 적용한 경우 비효율적인 것으로 추정되었다. 이는 일반적 DEA모형의 경우 비효율적인 품목농협이 효율적인 것으로 추정될 수 있다는 것이다.

It is analysed the efficiency of the Korean IT industry using CCR and BCC models of the DEA(Data Envelopment Analysis) method in this study. In this study, the Korean IT industry is classified into 7 groups which are Displays, Digital Contents, Wireless Communication Devices, Telecommunication Services, Semi-Conducts, Broadcasting Services and Computer Services that the efficiency of each groups is analysed contrastively. And it is considered to find a way to improve the efficiency of inefficient companies using the slack variable analysis. Also the Super DEA is used to find the relative rank among companies that their efficiency is 100% and the change of the Korean IT efficiency is measured by the Time Series Analysis from 2005 to 2007 for 3years as well.

본 연구에서는 국적외항선사를 대상으로 DEA에 의한 Malmquist 생산성지수를 측정하여 주요 재무비율(수익성, 재무안정성, 유 동성, 효율성, 생산성 간에 영향 관계와 판별력을 규명하여 생산성을 개선할 방안을 제시하였다. 2017년에 비하여 2018년에 생산성(MPI)이 증대한 선사보다 감소한 선사가 11개 많다. 생산성 감소는 주로 내부환경의 영향을 받는 기술적 효율성 변화지수(TECI)의 감소가 주요인이며, 생산성이 증대된 선사는 외부환경의 영향을 받는 기술변화지수(TCI)의 증대로 나타나고 있다. 또한 생산성(MPI)과 경영효율성(CRS) 간의 강 한 유의적인 상관관계를 보인다. 선사 내부요인에 의한 기술적 효율성변화지수(TEC)는 효율적인 선사가 유의적으로 높은데 순수효율성 변화가 아니고 규모효율성변화의 차이에 기인한다. 용선비/매출 비율은 생산성이 높은 선사(0.17)가 낮은 선사(0.21)에 비하여 낮고, 매출액영업이 익률은 MPI>1인 선사는 7%인데 MPI<1인 선사는 1%에 불과하여 용선비 규모와 영업수익성은 생산성과 밀접한 관련이 있는 것으로 판명된다. 따라서 외항선사는 용선비중을 줄이고 내부적인 경영효율 개선을 통한 규모 효율과 생산성을 증대시켜야만 채산성을 높일 수 있는 것으로 확인하였다.

본 연구는 수자원정책의 효율성 제고를 위한 정책 방안 마련의 토대를 제공하고자 제조업체에서 투입요소로 사용하고 있는 공업용수의 기술적 효율성을 추정하였다. 이를 위하여 자료포락분석(DEA:Data Envelopment Analysis)기법을 이용하였으며 분석 결과에 의하면 공업용수의 기술적 효율성은 전 산업 평균이 0.407로 추정되어 모든 투입요소가 가변적인 경우의 연구 사례에 비하여 낮은 추정치를 보여준다. 이는 공업용수에 대한 비용이 다른 투입요소