Ranking DMUs by Calculating the Interval Efficiency with a Common Set of Weights in DEA

Ranking Efficient DMUs in Two-stage Network DEA with Common Weights method

Two stages DEA models are used in many fields of management and industry. One of the concepts that has attracted the attention of researchers in the theory of production is the concept of ranking the units with a two-stage network. A unit ranking can provide useful information to decision makers (DMUs) about optimal decision making activities. This concept defines the superiority of a unit in t...

Ranking Two-Stage Process Using a Common Set of Weights in DEA

Supply chain management is the combination of art and science that goes into improving the supply chain. In some cases of supply chain especially two-stage process, differing weights for the same factors, may not acceptable. The purpose of this paper is a performance evaluation of two-stage using DEA and based on a CSW model and this method for ranking two-stage and could be used to measure the...

Determining a common set of weights in DEA by solving a linear programming

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...

Ranking of DMUs on interval data by imposing a minimum weight restriction in DEA

Deriving Common Set of Weights in the Presence of the Undesirable Inputs: A DEA based Approach

Data Envelopment Analysis (DEA) as a non-parametric method for efficiency measurement allows decision making units (DMUs) to select the most advantageous weight factors in order to maximize their efficiency scores.  In most practical applications of DEA presented in the literature, the presented models assume that all inputs are fully desirable. However, in many real situations undesirable inpu...