Development of a series-parallel data envelopment analysis model within an intuitionistic fuzzy environment

Data Envelopment Analysis (DEA) is a widely used tool for evaluating the efficiency of Decision-Making Units. However, traditional DEA models often encounter limitations when applied to complex, multi-stage systems, particularly in the presence of uncertainty and ambiguity in the data. To address these challenges, this study introduces a series–parallel DEA model that integrates both optimistic and pessimistic evaluations within an intuitionistic fuzzy environment. The model is specifically designed to provide a more accurate and nuanced assessment of multi-stage system efficiency, especially when handling diverse data types commonly encountered in real-world settings, including crisp, fuzzy, and intuitionistic fuzzy data. Two numerical examples are presented to validate the proposed model: one based on synthetic data and another adapted from real-world data reported by Kao. These case studies illustrate the model’s effectiveness and practical applicability in evaluating the performance of multi-stage systems with complex and uncertain data. By incorporating both optimistic and pessimistic perspectives, the proposed framework offers a comprehensive and balanced evaluation of DMU performance, making it a valuable tool for decision-makers operating under uncertain and dynamic conditions.