Rapid Prototype Process Selection using Multi Attribute Decision Making using Advanced Entropy Weight Based Fuzzy Single Valued Neutrosophic Set

Resumo

This paper presents a novel methodology for Multi-Attribute Decision Making (MADM) based on Single-Valued Neutrosophic Sets (SVNS), a specialized extension of Neutrosophic sets that incorporates three distinct membership functions: truth, indeterminacy, and falsehood. The objective is to develop an advanced decision-making framework that improves rapid prototype process selection, particularly when handling imprecise, uncertain, and inconsistent information commonly encountered in real-world applications. The proposed method integrates the conversion of crisp and fuzzy data into SVNS and employs an advanced entropy weight-based approach to enhance the selection process. The effectiveness of the approach is demonstrated through case studies drawn from existing literature on rapid prototyping, showing that the new methodology yields more accurate results with reduced computational effort when compared to traditional MADM methods. The results validate the superiority of the proposed approach in addressing indeterminacies and uncertainties, offering more reliable and precise decision-making outcomes in complex environments.