Data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. This methodology is applied widely in different contexts. Regarding to its logic, DEA allows each DMU to take its optimal weight in comparison with other DMUs while a similar condition is considered for other units. This feature is a bilabial characteri...

Traditional Data Envelopment Analysis (DEA) models evaluate the efficiency of decision making units (DMUs) with common crisp input and output data. However, the data in real applications are often imprecise or ambiguous. This paper transforms fuzzy fractional DEA model constructed using fuzzy arithmetic, into the conventional crisp model. This transformation is performed considering the goal pr...

Data Envelopment Analysis (DEA) is one of the best-known efficiency evaluation methods due to its advantages in selection of weights. Many research papers have extensively discussed the issue of weight restrictions, rather than those implied in the model itself. However, this often leads to a failure to represent the relations of certain weights, as well as underestimation of the efficiency of ...

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of a set of decision making units (DMUs), such as companies or public sector agencies. The main DEA models are only used for positive data. In recent years, some models have been presented to deal with negative data in DEA models. However, these models do not discriminate between efficient DMUs and only eval...

In this paper, we extend the standard data envelopment analysis (DEA) model to include longer term top management goals. This extension is in recognition of the fact that benchmarking for decision making units (DMUs) is more than a purely monitoring process, and includes a component of future planning. The new model uses a goal programming structure to find points on the efficient frontier whic...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs) which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of...

Data envelopment analysis (DEA) is a technique used to evaluate the relative efficiency of comparable decision making units (DMUs) with multiple input-output. It computes a scalar measure of efficiency and discriminates between efficient and inefficient DMUs. It can also provide reference units for inefficient DMUs without consideration of the decision makers’ (DMs) preferences. In this paper, ...