A Unified Framework for Interval-Valued Fuzzy Multi-Criteria Decisions: Synergizing Dynamic Outranking and Aggregation-Based Methods

Contemporary decision-making is increasingly confronted with complex, uncertain environments where traditional multi-criteria decision-making (MCDM) models, reliant on precise data, prove inadequate. While interval-valued fuzzy sets (IVFS) provide a sophisticated mechanism for capturing linguistic vagueness and expert doubt, existing IVFS-based MCDM methods often suffer from rank instability, sensitivity to subjective parameters, and insufficient adaptability to dynamic uncertainty. To bridge these critical gaps, this article introduces a novel unified framework for Interval-Valued Fuzzy MCDM (IVF-MCDM) that synergistically integrates the strengths of dynamic outranking relations and robust aggregation-based techniques. The framework is operationalized through two innovative hybrid methodologies: first, an enhanced ELECTRE I model employing Type-2 interval-valued fuzzy sets with adaptive, data-driven concordance and discordance indices that dynamically adjust to uncertainty levels; and second, a symmetrical Interval-Valued Fuzzy Weighted Aggregated Sum Product Assessment (IVF-WASPAS) approach that balances additive and multiplicative aggregation logics through a symmetry principle () to enhance ranking stability. The practical efficacy, robustness, and superior performance of the proposed unified framework are rigorously validated through comprehensive case studies in strategic healthcare system evaluation and sustainable urban landfill site selection. Comparative analyses with standalone methods such as IVF-TOPSIS and IVF-COPRAS demonstrate significant improvements in reliability, reduced rank reversal, and enhanced handling of pervasive uncertainty. This research contributes a methodologically sound, versatile decision-support tool that advances the theoretical frontier of fuzzy MCDM and offers practitioners in engineering, management, and policy a powerful instrument for navigating complex, ambiguous strategic landscapes.