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- W2808030721 abstract "Data envelopment analysis (DEA) is a widely used mathematical programming technique for measuring the relative efficiency of decision-making units which consume multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally used in classical DEA models, real-life problems often involve uncertainties characterized by fuzzy and/or random input and output data. We present a new input-oriented dual DEA model with fuzzy and random input and output data and propose a deterministic equivalent model with linear constraints to solve the model. The main contributions of this paper are fourfold: (1) we extend the concept of a normal distribution for fuzzy stochastic variables and propose a DEA model for problems characterized by fuzzy stochastic variables; (2) we transform the proposed DEA model with fuzzy stochastic variables into a deterministic equivalent linear form; (3) the proposed model which is linear and always feasible can overcome the nonlinearity and infeasibility in the existing fuzzy stochastic DEA models; (4) we present a case study in the banking industry to exhibit the applicability of the proposed method and feasibility of the obtained solutions." @default.
- W2808030721 created "2018-06-21" @default.
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- W2808030721 date "2019-01-01" @default.
- W2808030721 modified "2023-10-02" @default.
- W2808030721 title "A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data" @default.
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- W2808030721 doi "https://doi.org/10.1142/s0219622018500396" @default.
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