Data envelopment analysis (DEA) identifies an empirical efficient frontier of a set of peer decision making units (DMUs) with multiple inputs and outputs. The efficient frontier is characterized by the DMUs with an unity efficiency score. The performance of inefficient DMUs is characterized with respect to the identified efficient frontier. If the performance of inefficient DMUs deteriorates or...

In original Data Envelopment Analysis (DEA) models for measuring the relative efficiencies of a set of Decision Making Units (DMUs) using various inputs to produce various outputs are limited to crisp data. To deal with imprecise data, the notion of fuzziness has been introduced. this paper develops a procedure to measure the efficiencies of DMUs with fuzzy observations. The basic idea is to tr...

Data Envelopment Analysis (DEA) deals with evaluating a set of decision-making units (DMUs) based on nonparametric mathematical approaches. In classical DEA models, the given set of factors of DMUs is divided into two categories, inputs and outputs, while in some practical problems there are some measures whose membership to these categories is unclear. It means these factors treat different in...

Data envelopment analysis (DEA) is a mathematical programming - based technique to evaluate the performance of homogeneous group decision-making units (DMUs) with multiple inputs and outputs. One DEA applications involves aggregating input resources reallocating them create efficient DMUs.The present study employs centralized resource allocation (CRA) approach develop model for creating new DMU...

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decisionmaking units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. This research proposes a methodology to determine one common set of weights for the performance indices of only DEA efficient DMUs. Then, these DMUs ar...

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) is a powerful mathematical method that utilises linear programming (LP) to determine the relative efficiencies of a set of functionally similar decision-making units (DMUs). Evaluating the efficiency of DMUs continues to be a difficult problem to solve, especially when the multiplicity of inputs and outputs associated with these units is considered. Problems rela...

This paper will examine the relationship between "Data Envelopment Analysis" and a statistical concept ``Outlier". Data envelopment analysis (DEA) is a method for estimating the relative efficiency of decision making units (DMUs) having similar tasks in a production system by multiple inputs to produce multiple ‎outputs.‎ An important issue in statistics is to identify the outliers. In this pap...

this paper introduces discretionary imprecise data in data envelopment analysis (dea) and discusses the efficiency evaluation of decision making units (dmus) with non-discretionary imprecise data. then, suggests a method for evaluation the efficiency of dmus with non-discretionary imprecise data. when some inputs and outputs are imprecise and non-discretionary, the dea model becomes non-linear ...

‎The purpose of this paper is to offer the equitable method for ranking Decision Making Units(DMUs) based on the Dynamic Data Envelopment Analysis (DDEA) concept, where quasi-fixed inputs or intermediate products are the source of inter-temporal dependence between consecutive periods. In fact, this paper originally makes the use of an approach extending the ranking of DMUs in DEA by Khodabakhsh...