Optimizing Hard Disk Selection via a Fuzzy Parameterized Single-Valued Neutrosophic Soft Set Approach
Journal Title: Journal of Operational and Strategic Analytics - Year 2023, Vol 1, Issue 2
Abstract
This study introduces a novel approach to decision-making problems, especially in the context of hard disk selection, using the concept of the fuzzy parameterized single-valued neutrosophic soft set (FP-SVNSS). Primarily, the focus is on assigning different levels of importance to each parameter within the set, which enables a more nuanced and flexible evaluation process. This is underpinned by the development of several related concepts and the definition of basic operations such as complement, subset, union, and intersection. In the quest for clarity, the nuances of these operations and the overall framework of the FP-SVNSS method are illustrated via numerous examples. The superiority of the FP-SVNSS method over other decision-making methods is affirmed through a comprehensive comparison. The unique strength of the proposed approach lies in its ability to handle imperfect, ambiguous, and inconsistent data. Consequently, it offers greater accuracy and practicality than existing models. In the latter part of the study, the theory is put to the test by tackling a real-world decision-making problem. The selected case involves the optimal selection of hard disks, a common issue in information technology procurement. The successful application of the FP-SVNSS method to this issue provides a compelling demonstration of its potential value in practical settings. Through the exploration of this innovative decision-making methodology, this research contributes to the broader field of soft computing and decision-making theory. The findings suggest a myriad of future applications of the FP-SVNSS method in dealing with various complex and fuzzy problems in both academic and industrial contexts.
Authors and Affiliations
Muhammad Ihsan, Muhammad Saeed, Atiqe Ur Rahman
Keywords
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- EP ID EP731851
- DOI https://doi.org/10.56578/josa010203
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