Network Data Envelopment Analysis and Uncertainty in Decision-Making‎: ‎A Three-Stage Model Based on Liu's Uncertainty Theory

Data Envelopment Analysis (DEA) is a well-established methodology for assessing the efficiency of decision-making units‎. ‎In complex systems comprising multiple interconnected subsections‎, ‎Network DEA provides a structured framework for efficiency evaluation‎. ‎However‎, ‎traditional DEA models rely on the assumption of deterministic data‎, ‎which inadequately reflects the inherent uncertainty present in real-world scenarios‎. ‎Traditional uncertainty-handling methods‎, ‎such as fuzzy logic‎, ‎stochastic models‎, ‎and interval-based techniques‎, ‎often fail when there is limited historical data and when expert opinions significantly influence the dataset‎. ‎To address these limitations‎, ‎this study introduces an uncertain network DEA model based on Liu’s uncertainty theory‎, ‎facilitating a more accurate assessment of efficiency under conditions of data imprecision‎. ‎The proposed model is designed for three interconnected subsections and is further extended into a generalized multi-stage framework‎, ‎allowing it to adapt to increasingly complex systems‎. ‎Its effectiveness and practical applicability are demonstrated through two numerical case studies in the banking industry‎, ‎highlighting its capacity to support decision-making under uncertainty‎. ‎The findings emphasize the model's potential to enhance efficiency evaluation methods‎, ‎particularly in environments characterized by limited and uncertain data‎.

کلیدواژه ها:

Uncertainty theory‎ ، ‎Network DEA‎ ، ‎Efficiency‎ ، ‎Banking system