Fuzzy stochastic congestion model for data envelopment analysis

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) on the basis of multiple inputs and outputs. Conventional DEA models assume that inputs and outputs are measured by exact values on a ratio scale. However, the observed values of the input and output data in real-world problems are often vague or random. Indeed, decision makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Several researchers have proposed various fuzzy methods for dealing with the ambiguous and random data in DEA. In this paper, we propose three fuzzy DEA models with respect to probability-possibility, probability-necessity and probability-credibility constraints. In addition to addressing the possibility, necessity and credibility constraints in the DEA model we also consider the probability constraints. . In thispaper we provide an extension to the DEA based congestion concept with input and output data are assumed to be characterized by fuzzy random variables (FRVs) fuzzy data.

کلیدواژه های Fuzzy stochastic congestion model for data envelopment analysis:

Data envelopment analysis ، fuzzy random variable ، probability-possibility