Data Envelopment Analysis (DEA) is a nonparametric approach for measuring the relative efficiency of a decision making units consists of multiple inputs and outputs. In all standard DEA models semi positive real valued measures are assumed, while in some real cases inputs and outputs may take complex valued. The question is related to measuring efficiency in such cases. As far as we are aware, ...

Data envelopment analysis (DEA), as a well-established nonparametric method, is used to meet efficiency evaluation purposes in many businesses, organizations, and decision units. This paper aims to present a novel integrated approach to fuzzy interpretive structural modeling (FISM) and dynamic network data envelopment analysis (DNDEA) for the selection and ranking of sustainable suppliers. Firs...

in some data envelopment analysis (dea) applications, some inputs of dmus have negative values with positive cost. this paper generalizes the global cost malmquist productivity index to compare the productivity of dierent dmus with negative inputs in any two periods of times under variable returns to scale (vrs) technology, and then the generalized index is decomposed to several components. th...

Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of homogenous Decision Making Units (DMUs) with multiple inputs and multiple outputs. These factors may be evaluated in fuzzy or stochastic environment. Hence, the classic structures of DEA model may be changed where in two fold fuzzy stochastic environment. For instances, linearity, feasibility a...

Among the various discriminant analysis (DA) methods, researchers have investigated several directions in this area: statistics, econometrics, computer data mining technologies and mathematical programming. Recently, as a nonparametric mathematical programming approach, Data envelopment analysis has been applied in DA area and received great attention. In this paper, we propose a new discrimina...