An improvement on the efficiency bounds and efficiency classifications in DEA with imprecise data - Bohlool Ebrahimi; Duško Tešić (Big Data and Computing Visions)
Recently, Park [1] proposed a mathematical Data Envelopment Analysis (DEA) model to estimate the lower bound of efficiency scores in the presence of imprecise data. The current paper shows that its model uses infeasible precise data instead of ordinal data. In addition, in some cases, we may be unable to calculate the relative efficiencies with his model. To overcome the problems, we propose a simple, practical algorithm to estimate the expected value of efficiencies, which is inspired by considering the DEA axioms to the imprecise data.