Data envelopment analysis (DEA), based on mathematical programming techniques, is a popular method for assessing the relative efficiency of various decision-making units (DMUs). Efficient DEA computations need to circumvent intensive time-consuming calculations. This paper indicates that the “corner points” in single input and single output scatter plot has extreme efficiency with regard to the...

This article combines two techniques: data envelopment analysis (DEA) and Factor analysis (FA) to data reduction in decision making units (DMU). Data envelopment analysis (DEA), a popular linear programming technique is useful to rate comparatively operational efficiency of decision making units (DMU) based on their deterministic (not necessarily stochastic) input–output data and factor analysi...

Data Envelopment Analysis (DEA) is traditionally based on the axiom of convex production possibility sets. In many research situations this axiom is considered overly restrictive, and recent research has focused on finding suitable ways of relaxing it. Unfortunately, there currently is no consensus in the DEA field on why and how to account for non-convexities. This paper explores empirical evi...

Abstract: The radial measures of classical DEA models (CCR, BCC) are incomplete, they are only separate measures of input and output efficiency and their efficiency index omit the non-zero input and output slacks. Enhanced Russell graph measure (ERM) eliminates these deficiencies. All of the existing fuzzy DEA models are extension of CCR or BCC model, efficiencies of DMUs, ultimately, are solut...

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers ...

Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for performance evaluation and benchmarking. DEA has witnessed a widespread use in many application areas since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978. However, to the best of our knowledge, no published work formally addressed out-of-sample evaluation in DEA. In this p...

In humans, cytochrome P450 3A (CYP3A4) is a major enzyme involved in the metabolism of amiodarone (AM) to its major metabolite, desethylamiodarone (DEA). In rat, a commonly used animal model, metabolism of AM has not been well studied. To determine whether DEA is formed by CYP3A isoenzymes in the rat, microsomal protein was harvested from liver and intestine of male Sprague-Dawley rats. The met...

In this paper, Data Envelopment Analysis (DEA) and super efficient DEA (SE-DEA) are employed to measure the efficiency of Chinese commercial banks. Incorporating Tobit regression analysis, the determinants of banking efficiency are investigated based on Panel data. Overall, the DEA results show relatively low average efficiency levels and state-owned banks are more inefficient than that of join...

Traditional DEA models deal with measurements of relative efficiency of DMUs regarding multiple-inputs vs. multiple-outputs. One of the drawbacks of these models is the neglect of intermediate products or linking activities. After pointing out needs for inclusion of them in DEA models, we propose a slacks-based network DEA model that can deal with intermediate products. Using this model we can ...

It is usual to rank the participating countries in the Olympic Games in accordance with the number of medals they have won. An alternative ranking is suggested in this paper. This ranking is based on each country’s ability to win medals in relation to its available resources. This is an efficiency that can be measured with the help of Data Envelopment Analysis (DEA) for which two models exist: ...