Abstract: Data Envelopment Analysis (DEA) is a mathematical programming method for the assessment of relative efficiency of a set of Decision Making Units (DMUs) that use several inputs to produce several outputs. It measures the efficiency of each Decision Making Unit (DMU) by maximizing the ratio of virtual output to virtual input with the constraint that the ratio does not exceed one for eac...

The highest efficiency score 1 (100% efficiency) is regarded as a common benchmark for Decision Making Units (DMUs). This brings about the existence of more than one DMU with the highest score. Such a case normally occurs in all Data Envelopment Analysis (DEA) models and also in all the Common Set of Weights (CSWs) methods and it may lead to the lack of thorough ranking of DMUs. And ideal DMU b...

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...

Cost efficiency measures the cost of resource by output production. While conventional cost efficiency models set targets separately for each DMU, There are cases where the Central decision making is seeking the above targets, and at the same tries to obtain the target of Min cost efficiency for the total consumption. in this paper we consider that there is a centralized decision maker (DM). In...

   The performance of a decision making unit (DMU) can be evaluated in either across-sectional or a time-series manner, and data envelopment analysis (DEA) is a useful method for both types of evaluation. The Malmquist productivity index (MPI) evaluates the change in efficiency of a DMU between two time periods. It is defined as the product of the Catch-up and Frontier-shift terms. In this pape...

This study addresses the problem of finding the range of efficiency for each Decision Making Unit (DMU) considering uncertain data. Uncertainty in the DMU coefficients in each factor (input or output) is captured through interval coefficients (i.e., these are uncertain but bounded). A twophase additive Data Envelopment Analysis (DEA) model for performance evaluation is used, which is adapted to...

This paper clarifies the connection between multiple criteria decision-making and decision under uncertainty in a qualitative setting relying on a finite value scale. While their mathematical formulations are very similar, the underlying assumptions differ and the latter problem turns out to be a special case of the former. Sugeno integrals are very general aggregation operations that can repre...

This paper proposes two-stage model for prioritizating decision making units (DMU) where each unit has multiple inputs and outputs. The rst stage model is a noncooperative game where each optimal input-output weight set of DMU is de ned by the Data Envelopment Analysis (DEA) and each DMU evaluates other DMUs by its own weights. The equilibrium state obtained from the rst stage motivates none of...

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...

DEA (Deta Envelopment Analysis) is a non-parametric technique for measuring the efficiency of DMUs (Decision Making Units)with common inputs and outputs [2,5]. viewpoint for each DMU because of taking a maximum ratio. During recent years, the issue of sensitivity and stability of data envelopment analysis results has been extensively studied. The first DEA sensitivity analysis paper by Charnes ...