Management of uncertainty in DEA using intuitive fuzzy and fuzzy rough parameters

This article examines the impact of uncertainty in the form of intuitionistic fuzzy data and rough fuzzy parameters on performance evaluation and management using Data Envelopment Analysis (DEA). The primary objective of this research is to enhance the accuracy and management of uncertainty in DEA models. In the present study, we utilize intuitionistic fuzzy imprecise data, each parameter of which forms a rough fuzzy set. The expected values of intuitionistic fuzzy and rough fuzzy parameters play a significant role in this research. Given that rough fuzzy parameters help the model to work more effectively with imprecise and uncertain data, and intuitionistic fuzzy data provide more information and details about variations and uncertainties, the combination of these two concepts allows DEA models to assess and analyze the performance of various units with greater accuracy and confidence, leading to improved management of uncertainties. However, the increased model size and computational complexity are considered limitations of this approach. We illustrate the proposed approach with a numerical example. The use of the results from the proposed models can be an effective tool in performance evaluation across various organizations and industries, leading to significant improvements in decision-making processes.