Fuzzy stochastic input-oriented primal data envelopment analysis models with application to insurance industry Online publication date: Tue, 20-Dec-2016
by Seyed Hadi Nasseri; Ali Ebrahimnejad; Omid Gholami
International Journal of Applied Decision Sciences (IJADS), Vol. 9, No. 3, 2016
Abstract: Data envelopment analysis (DEA) is a widely used technique for measuring the relative efficiencies of decision making units (DMUs) with multiple inputs and multiple outputs. However, in real-world problems, the observed values of the input and output data are often vague or random. Indeed, decision makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. This paper proposes a normal distribution with fuzzy components to deal with fuzziness and randomness of input and output data. The proposed normal distribution introduces a class of fuzzy random variables. We propose a DEA model problems characterised by fuzzy stochastic variables. Unlike the existing approaches, the proposed fuzzy stochastic DEA model is transformed into a deterministic model with linear constraints. A case study in the insurance industry is presented to exhibit the efficacy and the applicability of the proposed model.
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