Data Envelopment Analysis with Fuzzy Parameters: An Interactive Approach

Fuzzy Dea Literature Review

In the conventional DEA, all the data assume the form of specific numerical values. However, the observed values of the input and output data in real-life problems are sometimes imprecise or vague. Imprecise evaluations may be the result of unquantifiable, incomplete and non obtainable information. The “Stochastic approach” and the “fuzzy approach” are two existence approaches for modeling uncertainty in the DEA literature. The stochastic approach involves specifying a probability distribution function (e.g., normal) for the error process (Sengupta, 1992). However, as pointed out by Sengupta (1992), the stochastic approach has two drawbacks associated with modeling the uncertainty in DEA problems:

• 1.

  Small sample sizes in DEA make it difficult to use stochastic models, and

• 2.

  In stochastic approaches, the DM is required to assume a specific error distribution (e.g., normal or exponential) to derive specific results. However, this assumption may not be realistic because on an a priori basis there is very little empirical evidence to choose one type of distribution over another.