Adaptation of a novel intuitionistic fuzzy distance metric into TOPSIS, VIKOR, and CODAS methods: A case study on material selection for chest compression device body

Multi-criteria decision-making (MCDM) methods are widely employed to evaluate alternatives involving multiple and often conflicting criteria across various fields. Intuitionistic fuzzy (IF) extensions of these methods enhance decision quality by incorporating uncertainty and hesitation more effectively. In this study, a novel IF distance metric is adapted into three widely used IF-based MCDM methods—TOPSIS, VIKOR, and CODAS—to evaluate its comparative performance and applicability. While the proposed distance metric was previously applied in a limited context, this work represents the first comprehensive integration across multiple IF-MCDM methods. The methods are applied to a real-world engineering problem involving the selection of the most suitable thermoplastic material for the body of an automatic chest compression device. Six alternatives are evaluated based on twenty-two criteria, including structural performance, manufacturability, and cost. The results show that PC/ABS FR consistently ranks as the top material in all three methods. This study demonstrates the robustness and versatility of the proposed distance metric across different IF-MCDM methods and highlights its practical relevance in critical healthcare applications.