Dissociative electron attachment (DEA) is the major process where molecules are destroyed in low-energy plasmas. DEA cross sections are therefore important for a whole variety of applications but are both hard to measure or compute accurately. A method for estimating DEA cross sections based a simple resonance plus survival model is presented. Test results are presented for DEA of molecular oxy...

This paper discusses alternative methods for determining returns to scale in DEA. The methods for estimating returns to scale in DEA, as developed by Banker (1984), Banker, Charnes and Cooper (1984) and Banker and Thrall (1992), are proved to be conceptually equivalent to the two-stage methods of F~ire, Grosskopf and Lovell (1985) when their assumptions apply. Here the emphasis is on the CCR mo...

Selection of international market (IM) is a critical decision and needs to be made with considerable attention and deliberation. In some situations however, some criteria have the nature of both cost and profit. Likewise, in data envelopment analysis (DEA) some criteria play both input (cost) and output (profit) roles, simultaneously. In the DEA literature such criteria are called dual-role fac...

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 conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units (DMUs) with exact value of inputs and outputs. For imprecise data, i.e., mixtures of interval data and ordinal data, some methods have been developed to calculate the interval of the efficiency scores. This paper constructs a procedure to measure the efficiencies of DMUs with mi...

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...