The existing data envelopment analysis (DEA) models for measuring the relative efficiencies of a set of decision making units (DMUs) using various inputs to produce various outputs are limited to crisp data. The notion of fuzziness has been introduced to deal with imprecise data. Fuzzy DEA models are made more powerful for applications. This paper develops the measure of efficiencies in input o...

the present study is an attempt toward evaluating the performance of portfolios and asset selectionusing cross-efficiency evaluation. cross-efficiency evaluation is an effective way of ranking decisionmaking units (dmus) in data envelopment analysis (dea). conventional dea models assume nonnegativevalues for inputs and outputs. however, we know that unlike return and skewness, varianceis the on...

data envelopment analysis (dea) is a method for measuring the relative efficiencies of a set of decision-making units (dmus) that use multiple inputs to produce multiple outputs. in this paper, we study the measurement of dmu performances in dea in situations where input and/or output values are given as imprecise data. by imprecise data we mean situations where we only know that the actual val...

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...

This article developed an approached model of congestion, based on relaxed combination of inputs, in stochastic data envelopment analysis (SDEA) with chance constrained programming approaches. Classic data envelopment analysis models with deterministic data have been used by many authors to identify congestion and estimate its levels; however, data envelopment analysis with stochastic data were...