In this article, Non radial method of dynamic DEA with the parallel network structure is presented and is used for calculation of relative efficiency measures when inputs and outputs do not change equally. In this model, DMU divisions under evaluation have been put together in parallel. But its dynamic structure is assumed in series. Since in real applications there are undesirable inputs an...

In models of Data Envelopment Analysis (DEA), an optimal set of input and output weights is generally as-sumed to represent the assessed Decision Making Unit (DMU) in the best light in comparison to all the other DMUs. These sets of weights are, typically, different for each of the participating DMUs. Thus, it is important to find a Common Set of Weights (CSW) across the set of DMUs. In this pa...

the problem of utilizing undesirable (bad) outputs in dea models often need replacing the assumption of free disposability of outputs by weak disposability of outputs. the kuosmanen technology is the only correct representation of the fully convex technology exhibiting weak disposability of bad and good outputs. also, there are some specific features of non-radial data envelopment analysis (dea...

Data Envelopment Analysis (DEA) is a technique that uses all collected observations to measure performance. This method presents no data about how to operate on DMU. The present research attempted to study a unit with all its subunits, if the unit is efficient, it means that all its subunits are efficient too and if it is an inefficient, it shows clearly that which one of the subunits makes ine...

Data Envelopment Analysis (DEA) is the use of non-parametric mathematical programming that useful for measuring efficiency Decision Making Unit (DMU) an organization. This study uses Cooper and Rhodes (CCR) method known as DEA-CCR multiplier which aims to determine weight value each input output variable DMU being evaluated, but it not sufficient measure optimization. To get efficient a referen...

the productivity change of a decision making unit (dmu) between two time periods can be evaluated by the malmquist productivity index. in this study, we propose a method to compute the malmquist productivity index in several time periods (from the first to the last periods) on interval data in data envelopment analysis (dea). then, the obtained malmquist productivity index is compared with malm...

Traditional data envelopment analysis (DEA) models evaluate two-stage decision making unit (DMU) as a black box and neglect the connectivity may exist among the stages. This paper looks inside the system by considering the intermediate activities between the stages where the first stage uses inputs to produce outputs which are the inputs to the second stage along with its own inputs. Additional...

The paper introduces stochastic DEA with a Perfect Object (SDEA PO). The Perfect Object (PO) is a virtual Decision Making Unit (DMU) that has the smallest inputs and greatest outputs. Including the PO in a collection of actual objects yields an explicit formula of the efficiency index. Given the distributions of DEA inputs and outputs, this formula allows us to derive the probability distributi...

This paper introduces two virtual decision making units (DMUs) called ideal DMU (IDMU) and anti-ideal DMU (ADMU) into the data envelopment analysis (DEA). The resultant DEA models are, respectively, referred to as the data envelopment analysis with ideal and anti-ideal decision making units. One evaluates DMUs from the viewpoint of the best possible relative efficiency, while the other evaluate...