In many applications, discrimination among decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA). The DEA models unable to discriminate between extremely efficient DMUs. Hence, there is a growing interest in improving discrimination power in DEA yet. The aim of this paper is ranking extreme efficient DMUs in DEA based on exp...

one of the difficulties of data envelopment analysis(dea) is the problem of deciency discriminationamong efficient decision making units(dmus) and hence, yielding large number of dmus as efficientones. the main purpose of this paper is to overcome this inability. one of the methods for rankingefficient dmus is minimizing the coefficient of variation (cv) for inputs-outputs weights. in this pap...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

data envelopment analysis (dea) with considering the best condition for each decision making unit (dmu) assesses the relative efficiency for it and divides a homogenous group of dmus in to two categories: efficient and inefficient, but traditional dea models can not rank efficient dmus. although some models were introduced for ranking efficient dmus, franklin lio & hsuan peng (2008), proposed a...

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...

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decision making units (DMUs) in a homogeneous group. However, DEA does not provide more information about efficient units. Super-efficiency DEA models can be used in ranking the performance of efficient DMUs. This research proposes a methodology to determine an Euclidean dist...

Although investment is regarded as a key force of China’s economic growth, little study has been done to measure China’s investment efficiency. The present paper applies the data envelopment analysis (DEA) to Chinese provincial panel data from the year 2003 to 2008 for measuring the investment efficiencies and identifying their trends of Chinese 30 provinces and autonomous regions. A cross-effi...

This paper proposes the macro model and micro model for the efficiency evaluation of data warehouses (DWH), and introduces Fuzzy Data Envelopment Analysis (FDEA) and Data Envelopment Analysis (DEA) to comprehensively assess DWH efficiency. FDEA can determine the efficiencies of data warehouses with multiple input / output variables, and also resolve the problem that standard DEA cannot handle w...

This paper concentrates on methods for comparing activity units found relatively efficient by data envelopment analysis (DEA). The use of the basic DEA models does not provide direct information regarding the performance of such units. The paper provides a systematic framework of alternative ways for ranking DEA-efficient units. The framework contains criteria derived as by-products of the basi...

In this paper, we conduct simulation experiments to evaluate the performance of two alternative uses of the superefficiency procedure in Data Envelopment Analysis (DEA). The first is for outlier identification and the second is for ranking efficient units. We find that the ranking procedure does not perform satisfactorily. In fact, the correlations between the true efficiency and the estimated ...