recently the concept of facility efficiency, which defined by data envelopment analysis (dea), introduced as a location modeling objective, that provides facilities location’s effect on their performance in serving demands. by combining the dea models with the location problem, two types of “efficiencies” are optimized: spatial efficiency which measured by finding the least cost location and al...

data envelopment analysis (dea) as a non-parametric method for efficiency measurement allows decision making units (dmus) to select the most advantageous weight factors in order to maximize their efficiency scores.  in most practical applications of dea presented in the literature, the presented models assume that all inputs are fully desirable. however, in many real situations undesirable inpu...

background: during the last decades, the number of dentistry units increased significantly across the country. the aim of this study was to assess the efficiency of dental units of iran provinces regarding dental health inputs and outputs using data envelopment analysis approach. methods: in this applied descriptive-analytical study, the study population included all of iran 31 provinces. the o...

In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. DEA has been successfully applied to a host of different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the fundamental DEA models and some of their extensions.

this paper deals with the problem of optimizing two-stage structure decision making units (dmus) where the activity and the performance of two-stage dmu in one period effect on its efficiency in the next period. to evaluate such systems the effect of activities in one period on ones in the next term must be considered. to do so, we propose a dynamic dea approach to measure the performance of su...

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...

Sensitivity of the returns to scale (RTS) classifications in data envelopment analysis is studied by means of linear programming problems. The stability region for an observation preserving its current RTS classification (constant, increasing or decreasing returns to scale) can be easily investigated by the optimal values to a set of particular DEA-type formulations. Necessary and sufficient co...

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...

Here, we examine the capability of artificial neural networks (ANNs) in sensitivity analysis of the parameters of efficiency analysis model, namely data envelopment analysis (DEA). We are mainly interested to observe the required change of a group of parameters when another group goes under a managerial change, maintaining the score of the efficiency. In other words, this methodology provides a...

In this paper we apply data envelopment analysis (DEA) to evaluate the performance of hedge fund classifications. The purpose of alternative investment strategies such as hedge funds is to offer absolute returns, so using passive benchmarks to measure their performance could be ineffective. With the increasing number of hedge funds available, institutional investors, pension funds, and high net...