Data Envelopment Analysis (DEA) is a powerful performance measurement in economic sector and operations research to assess the relative efficiency for each decision making unit (DMU). In general, there are two assumptions in DEA. Firstly, the DEA assumes that all DMUs are homogenous in their environments and secondly, the DEA is a deterministic approach which refers to not allow to noise or err...

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...

in the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results. in these ‘‘deterministic’’ frontier models, statistical theory is now mostly available. this paper deals with the statistical pared sample method and its capability of detecting outliers in data envelopment analysis. in the prese...

one of the major problems in data envelopment analysis (dea) is to determine the projection of inefficient decision making units (dmus) into the efficient frontier. in conventional dea models, inputs and outputs of inefficient dmus alter arbitrarily for reaching to the efficient frontier. nevertheless, sometimes the ability of dmus is defined and restricted. moreover, there are situations in th...

Within the data envelopment analysis context, problems of discrimination between efficient and inefficient decision-making units often arise, particularly if there are a relatively large number of variables with respect to observations. This paper applies Monte-Carlo simulation to generalize and compare two discrimination-improving methods; principal component analysis applied to data envelopme...

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a twostage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between ...

over the years we have witnessed the growing importance of supply chain operations and supply chain management (scm), as supply chains play a major role in every step of the product life cycle. therefore, this paper will dea-based performance evaluation approaches for overall supply chain can be categorized. future perspectives and challenges are discussed

This paper adopts two mathematical approaches, data envelopment analysis (DEA) and grey relation analysis (GRA) to measure DEA efficiency using the sample of 69 listed US internet companies. A total of 40 indicators were initially selected for the efficiency evaluation, with 21 related to DEA-input indicators and 19 to DEAoutput indicators. Eight representative indicators selected using GRA are...

In the banking industry, the production process can be described as a two-stage process. There are a number of published data envelopment analysis (DEA) papers that study the bank performance with two-stage model. However, none of them is applied to identify bank failure. In fact, only one of them deals with negative profit data. In the real world, failed banks or firms often produced negative ...

Data envelopment analysis (DEA) is an effective method to evaluate the relative efficiency of decision-making units (DMUs). In one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. Thi...