Data envelopment analysis (DEA) is a well-Known method in efficiency evaluation of a set of decision making units (DMUs) such as organizations and banks. An advantage of DEA technique is selection of weights at random. Weight selection is of crucial importance in efficiency evaluation. In this regard, it is important to employ models that have more freedom in selecting weights. One such model i...

<p style='text-indent:20px;'>The conventional data envelopment analysis (DEA) models presume that the values of input-output variables decision-making units (DMUs) are precisely known. However, some real-life situations can authoritatively mandate to vary in concrete fine-tuned ranges, which include negative and measures allowed take integer only. Our study proposes an integrated dynamic ...

  A data envelopment analysis (DEA) method can be regarded as a useful management tool to evaluate decision making units (DMUs) using multiple inputs and outputs. In some cases, we face with imprecise inputs and outputs, such as fuzzy or interval data, so the efficiency of DMUs will not be exact. Most researchers have been interested in getting efficiency and ranking DMUs recently. Models of th...

This study consists of three types of vendor selection models in supply chains and presents a decision making scheme for choosing appropriate method for supplier selection under certainty, uncertainty and probabilistic conditions. These models are, Data Envelopment Analysis (DEA), Fuzzy Data Envelopment Analysis (FDEA), and Chance Constraint Data Envelopment Analysis (CCDEA). In FDEA model we u...

This paper introduces a new robust data envelopment analysis (RDEA) approach for analyzing and ranking the organizations’ operations strategies. In the proposed RDEA method, pessimistic and optimistic efficiencies of decision making units (DMUs) obtained from the robust counterpart of the envelopment form and the optimistic counterpart of the multiplier form of DEA are introduced. The inputs an...

An original data envelopment analysis (DEA) model is to evaluate each decision-making unit (DMU) with a set of most favorable weights of performance indices. The efficient DMUs obtained from the original DEA construct an efficient (bestpractice) frontier. The original DEA can be considered to identify good (efficient) performers in the most favorable scenario. For the purpose of identifying bad...