This article compares two techniques: Data Envelopment Analysis (DEA) and Factor Analysis (FA) to aggregate multiple inputs and outputs in the evaluation of decision making units (DMU). Dِata envelopment analysis (DEA), a popular linear programming technique, is useful to rate comparatively operational efficiency of DMUs based on their deterministic or stochastic input–output data. Factor analys...

This paper suggests a method of finding super-efficiency scores and modification of input-oriented models for sensitivity analysis of decision making units. First, by using DEA-R (ratiobased DEA) models in the input orientation, the models of superefficiency and also models of super-efficiency modification are suggested. Second, the worst-case scenarios are considered where the efficiency of th...

Data envelopment analysis is a new field of interdisciplinary research in operations management science and mathematical economics. uses programming to evaluate the relationship between decision-making units with multiple inputs outputs (abbreviated as DMU). Effectiveness (DEA effective) judge whether DMU located on "frontier surface" production possibility set. This paper proposes method for e...

We develop comparative results for ratio-based efficiency analysis (REA), based on the decision making units’ (DMUs’) relative efficiencies over sets of feasible weights that characterize preferences for input and output variables. Specifically, we determine (i) ranking intervals which indicate the best and worst efficiency rankings that a DMU can attain relative to other DMUs, (ii) dominance r...

this research identifies returns to scale (rts) of efficient decision making units (dmus) with desirable (good) and undesirable (bad) inputs and outputs by presenting a new dea (data envelopment analysis) approach. in this study, we first introduce a new input-output oriented model to determine efficient dmus in the presence of undesirable factors and then, returns to scale of these dmus are es...

In data envelopment analysis (DEA), mul-tiplier and envelopment CCR models eval-uate the decision-making units (DMUs) under optimal conditions. Therefore, the best prices are allocated to the inputs and outputs. Thus, if a given DMU was not efficient under optimal conditions, it would not be considered efficient by any other models. In the current study, using common weights in DEA, a number of...

Data envelopment analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of decision making units (DMUs) in organizations. Crisp input and output data are fundamentally indispensable in traditional DEA evaluation process. However, the input and output data in real-world problems are often imprecise or ambiguous. In this study, we present a four-p...

Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency ...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...

In data envelopment analysis (DEA) efficient decision making units (DMUs) are of primary importance as they define the efficient frontier. By means of modified CCR model, in which the test DMU is excluded from the reference set, we are able to determine what perturbations of data can be tolerated before frontier DMUs become nonfrontier. In this paper we discuss simultaneous data perturbations i...