Classifying Inputs and Outputs with Fuzzy Data

  •  Mehrdad Nabahat    
  •  Fardin Sangari    


Data envelopment analysis is a nonparametric technique checking efficiency of DMUs using math programming. In conventional DEA, it has been assumed that the status of each measure is clearly known as either input or output. However in some situations, a performance measure can play input role for some DMUs and output role for others. There are different models for classifying inputs and outputs, but all of these models are with crisp data. In this paper we want to classify inputs and outputs when all of the DMUs have symmetrical triangular fuzzy inputs and outputs and flexible measures. The basic idea is to transform the fuzzy model into a crisp linear programming problem by applying an  $\alpha$-cut approach.Finally, a numerical example is proposed to display the application of this method.

This work is licensed under a Creative Commons Attribution 4.0 License.