ranking efficient dmus using the ideal point and norms

Ranking Efficient DMUs Using the Ideal point and Norms

  In this paper, presenting two simple methods for ranking of efficient DMUs in DEA models that included to add one virtual DMU as ideal DMU and is using the additive model. Note that, we use an ideal point just for comparing efficient DMUs with. Although these methods are simple, they have ability for ranking all efficient DMUs, extreme points and the others, also they are capable of ranking t...

Ranking efficient DMUs using minimizing distance in DEA

In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The main purpose of this paper ...

Ranking efficient DMUs using the infinity norm and virtual inefficient DMU in DEA

In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The purpose of this paper is ra...

Ranking stochastic efficient DMUs based on reliability

Ranking efficient DMUs using the variation coefficient of weights in DEA

One of the difficulties of Data Envelopment Analysis(DEA) is the problem of deciency 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 pape...

Ranking DMUs by ideal points in the presence of fuzzy and ordinal data

Envelopment Analysis (DEA) is a very eective method to evaluate the relative eciency of decision-making units (DMUs). DEA models divided all DMUs in two categories: ecient and inecientDMUs, and don't able to discriminant between ecient DMUs. On the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ordinal da...