삼각분포를 기반으로 한 가중치 제약 DEA-AR 모형에 대한 연구
A DEA-AR Model with Weight Bounds Based on Triangular Distributions
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초록
본 연구는 기존의 DEA-AR 모형에 가중치에 통계적인 제약을 가한 새로운 DEA-AR 모형을 제안한다. 기존의 DEA-AR 연구는 가중치를 확정적(deterministic)으로 제약을 가하기 때문에 불확실성이 내포된 현실에서의 적용에 한계가 있었다. 이를 보완하고자 불확실성을 고려한 DEA-AR 모형들이 개발되어 왔으나, 이는 입, 출력 변수의 불확실성만을 고려하는데 그쳤다. 그러므로 본 연구에서는 삼각 분포를 이용하여 가중치에 통계적 변동성을 고려하여 제약을 가한 새로운 DEA-AR 모형을 개발하여 실제 주유소 데이터를 이용하여 모형의 모수를 변경해가면서 모형을 실험하였다. 실험 결과 제약식의 하한값이 일정 수준 유의할 때 본 연구에서 제안하는 모형의 결과값과 기존의 DEA-AR 모형의 결과값 간 통계적으로 유의한 차이가 있음을 확인하였다.
Abstract
This paper propose a DEA-AR model using stochastic weight bounds. The existing DEA-AR study usually constraint the weight in deterministic way, so it limits the evaluate performance with uncertainties inherent in real world. In order to compensate for this, various DEA-AR models have been developed considering uncertainties, but it has limit that existing DEA-AR models only consider uncertainties of input and output variables. Therefore, in this study, we developed a new DEA-AR model with statistical constraints on the weights by using triangular distribution and experimented the model by changing the parameters of the model using gas station data. The results show that there is a statistically difference between the efficiency score using proposed DEA-AR model with efficiency score using existing DEA-AR model when the lower value of the constraint exists at a certain level.
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