Efficiency distribution and expected efficiencies in DEA with imprecise data

Non convex technologies and economic efficiency measure with imprecise data

Non-discretionary imprecise data in efficiency Measurement

This paper introduces discretionary imprecise data in Data Envelopment Analysis (DEA) and discusses the efficiency evaluation of Decision Making Units (DMUs) with non-discretionary imprecise data. Then, suggests a method for evaluation the efficiency of DMUs with non-discretionary imprecise data. When some inputs and outputs are imprecise and non-discretionary, the DEA model becomes non-linear ...

The Efficiency of MSBM Model with Imprecise Data (Interval)

Data Envelopment Analysis (DEA) is a mathematical programming-based approach for evaluates the relative efficiency of a set of DMUs (Decision Making Units). The relative efficiency of a DMU is the result of comparing the inputs and outputs of the DMU and those of other DMUs in the PPS (Production Possibility Set). Also, in Data Envelopment Analysis various models have been developed in order to...

Randomizing DEA Efficiency Scores with Beta Distribution

Since the original publication on Data Envelopment Analysis (DEA) by Charnes et al. (1978), a considerable amount of research publications have appeared in decision science literature, a significant portion of which focusing on efficiency and productivity in the banking sector. A comprehensive survey of bank efficiency studies could be found in Fethi and Pasiouras (2010). They have examined ban...

Non-Discretionary Factors and Imprecise Data in DEA

Discretionary models of data envelopment analysis (DEA) assume that all inputs and outputs can be varied at the discretion of management or other users. In any realistic situation, however, there may exist ”exogenously fixed” or non-discretionary factors that are beyond the control of a DMU’s management, which also need to be considered. Also DEA requires that the data for all discretionary inp...

non-discretionary imprecise data in efficiency measurement

this paper introduces discretionary imprecise data in data envelopment analysis (dea) and discusses the efficiency evaluation of decision making units (dmus) with non-discretionary imprecise data. then, suggests a method for evaluation the efficiency of dmus with non-discretionary imprecise data. when some inputs and outputs are imprecise and non-discretionary, the dea model becomes non-linear ...