Data Envelopment Analysis (DEA) is a non-parametric technique which is based on mathematical programming for evaluating the efficiency of a set of Decision Making Units (DMUs). Throughout applications, managers encounter with stochastic data and the necessity of having a method that is able to evaluate efficiency and rank efficient units has been under consideration. In this paper considering t...

Data envelopment analysis (DEA) defines relative efficiency of decision making units (DMUs), using mathmatical programming. In evaluating relative efficiency, usually more than one unit may be efficient. The problem of ranking efficient DMUs is of interest from theoretical and practical point of view. In this paper different methods have been discussed and in some sense they are compared. Real ...

In the conventional data envelopment analysis (DEA) internal sub-processes of the production units are ignored. The current paper develops a network-DEA super-efficiency model to compare the performance of efficient network systems. A new ranking method is developed by aggregating the computed super-efficiency scores with a J-divergence measure. The proposed approach is then applied to evaluate...

Data Envelopment Analysis )DEA( is a technique for measuring the efficiency of decision making units. In all models of the DEA, for each unit under assessment, the numerical efficiency which may be less than or equal to one is obtained. Given the possible large number of efficiency units for evaluating units, we use various methods of ranking. span style="font-family: Cambria Math;font-size:8pt...

To proposing a voting model for groups with members of unequal power and proficiency, we present some models for rank ordering efficient candidates, by extending the ideas of some authors. Then, we propose a new methodology to rank the ranking models for the performance indices of only DEA efficient candidates based on a classical voting model. Also, an approach for combining the results obtain...

In the real world there are groups which composed of independent units. The conventional data envelopment analysis(DEA) model treats groups as units, ignoring the operation of individual units within each group.The current paper, investigates parallel system network approach proposed by Kao and modifies it. As modied Kao' model is more eligible to recognize ecient groups, a new ranking method i...

The failure mode and effects analysis (FMEA) is a widely used analytical technique that helps to identify and reduce the risks of failure in a system, component, or process. One important issue of FMEA is the determination of the risk priorities of failure modes. Risk ranking is produced in order to prioritize the focus on each of the failure modes that are identified. In this study, we applied...

For ranking alternatives based on pairwise comparisons, current analytic hierarchy process (AHP) methods are difficult to use to generate useful information to assist decision makers in specifying their preferences. This study proposes a novel method incorporating fuzzy preferences and range reduction techniques. Modified from the concept of data envelopment analysis (DEA), the proposed approac...