Data Envelopment Analysis (DEA) requires that the data for all inputs and outputs are known exactly. When some outputs and inputs are known decision variables, such as interval data and ordinal data, the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). When data assume to be interval, decision making units (DMUs) can divide to three classes as follows: (I) ...

Article history: Received 17 April 2013 Received in revised form 30 August 2014 Accepted 26 October 2014 Available online 4 November 2014

   Recently, Farzipoor Saen [Journal of the Operational Research Society, 60(11), 1575–1582 (2009)] proposed a method based on data envelopment analysis to identify optimistic efficient suppliers in the presence of nondiscretionary factors-imprecise data. This short communication aims at showing a computational error in computing the value of preference intensity parameter in Farzipoor Saen’s [...

The selection of best Information System (IS) project from many competing proposals is a critical business activity which is very helpful to all organizations. While previous IS project selection methods are useful but have restricted application because they handle only cases with precise data. Indeed, these methods are based on precise data with less emphasis on imprecise data. This paper pro...

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...

Envelopment Analysis (DEA) is a very eective method to evaluate the relative eciency of decision-making units (DMUs). DEA models divided all DMUs in two categories: ecient and inecientDMUs, and don't able to discriminant between ecient DMUs. On the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ordinal da...

Data envelopment analysis technique which is developed based on the mathematical programming, evaluates the relative efficiency of a set of homogeneous decision making units. This paper shows the method of Discriminant Analysis (DA), on Imprecise Data by Data Envelopment 724 F. Hosseinzadeh Lotfi et al Analysis (DEA). DEA-Discriminant Analysis (DEA-DA) is designed to identify the existence or n...

data envelopment analysis (dea) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (dmus) where multiple inputs and outputs exist. in the conventional applications of dea, the data are considered as specific numerical values with explicit designation of being an input or output. however, the observed values of the data are sometimes imprec...

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...