Branch Mathematics and Statistics Faculty and Staff Publications
Document Type
Article
Publication Date
2025
Abstract
This paper proposes a modified structure for the neutrosophic set called the Quantified Neutrosophic Set (QtNS) with a parameterized setting. Unlike conventional approaches, the QtNS provides a quantified environment for the indeterminacy by its dependence on truthness and falsity components. This innovative approach quantifies the uncertainty and improves the assessment process via expert-guided opinions, customising it according to the specific situations in real-world decision-making scenarios. Some QtNS operations along with useful characteristics are addressed. Furthermore, two algorithms, QtNSUI and QtNSAO, are developed for the proposed operations of union, intersection, AND, and OR based on QtNS. In the world of sustainable materials, biofabricated textiles are making progress. The MCDM methods based on QtNS are developed for material preferences in the industry of biofabricated textiles, specifically with anti-microbial properties. The study’s main purpose is to develop a novel technique to quantify and reduce the predicted uncertainties in the material preference problem for antimicrobial biofabricated textile manufacturing. For eco-conscious decision-making, our work would provide an optimised environment at the industrial level, especially for ecologically conscious textile industries, for enhanced and sustainable selection with greater accuracy.
Publication Title
Journal of Mathematics and Computer Science
Volume
38
First Page
214
Last Page
235
Language (ISO)
English
Keywords
Neutrosophic set, quantified neutrosophic set (QtNS), QtNSUI-algorithm, QtNSAO-algorithm, optimization, decision making, bio-fabricated textile.
Recommended Citation
Saeed, Muhammad; Neha Andaleeb Khalid; and Florentin Smarandache.
"Quantified neutrosophic set (QtNS)-based MCDM algorithms for sustainable material selection for anti-microbial bio-fabricated textile manufacturing."
Journal of Mathematics and Computer Science
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
