data envelopment analysis (dea) is a method for measuring the relative efficiencies of a set of decision-making units (dmus) that use multiple inputs to produce multiple outputs. in this paper, we study the measurement of dmu performances in dea in situations where input and/or output values are given as imprecise data. by imprecise data we mean situations where we only know that the actual val...

traditional dea models deal with measurements of relative eciency ofdmus regarding multiple - inputs vs. multiple-outputs. one of the drawbacksof these model is the neglect of intermediate products or linkong activities. af-ter pointing out needs for inclusion of them to dea models. we propose hybridmodel that can deal with intermediate products formally using this model wecan evaluate divisio...

data envelopment analysis provides a theoretical framework for analyzing performance and measuring efficiency. using data envelopment analysis, we can evaluate units and organizations and discuss about their efficiency or inefficiency. in performance assessment of organizations, the most important goal is to rank the units according to their efficiency. since ranking decision making units in da...

Cross efficiency is one of the useful methods for ranking of decision making units (DMUs) in data envelopment analysis (DEA). Since the optimal solutions of inputs and outputs weights are not unique so the selection of them are not simple and the ranks of DMUs can be changed by the difference weights. Thus, in this paper, we introduce a method for ranking of DMUs which does not have a unique pr...

In this paper, by introducing two virtual decision-making units (DMUs) called ideal DMU (IDMU) and anti-ideal DMU (ADMU) with fuzzy inputs-outputs, the efficiency evaluation of DMUs are done by fuzzy data envelopment analysis (FDEA). Therefore, we evaluate DMUs from the perspective of the best and worst possible relative efficiency. For each DMU two efficiencies are calculated while inputs and ...

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using...

One of the drawbacks of Data Envelopment Analysis (DEA) is the problem of lack of discrimination among efficient Decision Making Units (DMUs). A method for removing this difficulty is called changing reference set proposed by Jahanshahloo and et.al (2007). The method has some drawbacks. In this paper a modified method and new method to overcome this problems are suggested. The main advantage of...

This study designs optimal systems for the centralized structures in data envelopmentanalysis (DEA). It assumes that a collection of decision making units (DMUs) with a masterdecision maker and a certain budget for them is available and introduces an optimal systemfor each DMU by maximizing their total revenue. A nmerical example is used to illustrate theproposed model.

  For efficiency evaluation of some of the Decision Making Units that have uncertain information, Rough Data Envelopment Analysis technique is used, which is derived from rough set theorem and Data Envelopment Analysis (DEA). In some situations rough data alter nonradially. To this end, this paper proposes additive rough–DEA model and illustrates the proposed model by a numerical example.