An Optimized Computational Framework for Multi-Criteria Decision Making Under Intuitionistic Fuzzy Environments: Integration, Validation, and Cross-Domain Applications
Abstract
Multi-Criteria Decision Making (MCDM) is an essential paradigm in the field of decision science, which allows the systematic analysis of competing options in the context of conflicting criteria. The major traditional approaches to MCDM, such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Elimination and Choice Translating Reality (ELECTRE), are largely based on accurate numerical data, and thus cannot be applied in a real-life setting, where vagueness, subjectivity, incomplete information, and cognitive reluctance usually prevail. Even though the traditional fuzzy set theory can in a way accommodate the uncertainty when memberships are graded, the combined membership-non-membership values can only sum to unity, which does not support the natural hesitancy of human judgments and expert assessment. Intuitionistic Fuzzy Sets (IFS), proposed as a variation of classical fuzzy logic, circumvents this restriction by introducing a hesitancy parameter defined as:
π = 1 − (μ + ν)
This formulation provides a more complete and realistic model of uncertainty in multi-faceted decision-making scenarios. This paper proposes the construction of an optimized computational model that logically incorporates IFS into well-known MCDM models to increase the accuracy, robustness, and practical adaptability of decisions under uncertain real-world conditions. The study adopts a mixed-methods approach involving rigorous theoretical modelling, computational algorithm development, and empirical verification through cross-domain case studies in healthcare diagnostics, financial risk assessment, and supply chain management. Simulations will be compared with conventional MCDM methods, and key performance indicators including decision accuracy, computational efficiency, scalability, and stakeholder usability will be evaluated. Sensitivity analyses will be conducted to test how changes in membership, non-membership, and hesitancy levels influence decision outcomes. The IFS-based MCDM model is expected to outperform traditional models under high uncertainty conditions while remaining computationally efficient for large-scale decision-support systems.
How to Cite
Shweta Saharan, Vikas Arya, "An Optimized Computational Framework for Multi-Criteria Decision Making Under Intuitionistic Fuzzy Environments: Integration, Validation, and Cross-Domain Applications", Vol. 4, Issue 2, 27-05-2026, pp. 114-128. DOI: 10.5281/zenodo.23055870
