A Fuzzy Stochastic DEA Model Considering an Input–Output Structure

Traditional DEA models can neither effectively handle fuzzy random variables nor achieve a complete ranking of decision-making units (DMUs). Based on the conventional fuzzy stochastic DEA model, this study introduces an exponential distribution extension. By incorporating fuzzy random variables, it significantly simplifies the deterministic transformation of chance-constrained models. Moreover, most existing DEA ranking methods only consider the relative efficiencies among DMUs while ignoring their internal structural characteristics. To address this issue, we develop a deterministic model for the exponentially extended fuzzy stochastic DEA and design a weight formula that reflects the internal input–output structure of each DMU. This approach makes the complete ranking of DMUs more reasonable and better aligned with practical situations. Finally, the rationality and effectiveness of the proposed model are verified through a comparative analysis of rankings obtained from different DEA models. The results indicate that the input–output structure within a decision-making unit plays a significant role in its efficiency ranking.