Data quality is critical to a successful data envelopment analysis (DEA) study. Outlier detection not only identifies suspicious data points and thus prevents the drawing of erroneous conclusions, but also can lead to the discovery of unexpected knowledge. This study develops a unified model to identify outliers in DEA studies by examining how they effect on the boundaries of a data set. The pr...

Sensitivity analysis in DEA is used for improving the efficiency scores of inefficient DMUs for which the efficient units remain unchanged. This paper introduces a generalized sensitivity analysis DEA model by perturbation a given input (or output) for all efficient DMUs. A numerical example illustrates the usefulness of the new model.

Data envelopment analysis (DEA) has been a wildly used powerful method to measure efficiencies of decision making units (DMUs). However, DEA efficiency scores are influenced by uncontrollable factors for respective DMUs. Previous studies attempted separating such factors from DEA scores. Fried et al. [4] proposed a multi-stage data adjustment approach using DEA and a regression model, and sever...

the basic models of data envelopment analysis (dea) are designed in such a way that the values of input and output indicators should be identified and known in them. in other words, these models are not used to consider inaccurate, interval, fuzzy, judgment data. in this paper, the aim is to not only review the past researches about the efficiency of the units by interval data and represent the...

intelligent wells provide the ability for monitoring and control of downhole environment of the wells. downhole monitoring is achieved through sensors while control is realized with downhole valves. recovery from intelligent wells can be improved by proper selection of candidate wells/fields and optimizing the number, location and performance of the installed interval control valves. design cri...

There are situations that Decision Making Units (DMU’s) have uncertain information and their inputs and outputs cannot alter redially. To this end, this paper combines the rough set theorem (RST) and Data Envelopment Analysis (DEA) and proposes a non-redial Rough-DEA (RDEA) model so called additive rough-DEA model and illustrates the proposed model by a numerical example.  

Abstract—Multi-component data envelopment analysis (MCDEA) is a popular technique for measuring aggregate performance of the decision making units (DMUs) along with their components. However, the conventional MC-DEA is limited to crisp input and output data which may not always be available in exact form. In real life problems, data may be imprecise or fuzzy. Therefore, in this paper, we propos...

Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different mult...

Return-To-Scale (RTS) is a most important topic in DEA. Many methods are not obtained for estimating RTS in DEA, yet. In this paper has developed the Banker-Trall approach to identify situation for RTS for the BCC model "multiplier form" with virtual weight restrictions that are imposed to model by DM judgments. Imposing weight restrictions to DEA models often has created problem of infeasibili...

Robust measurement of scale elasticity (SE) in data envelopment analysis (DEA) models remains elusive, primarily reflecting the computational challenges brought about by the piecewise linear nature of the DEA technology. SE is meaningfully defined only at frontier points or at the projection of interior points to the frontier but not for the inefficient unit itself. A long held issue of concern...