Fuzzy Soft Sets in Collaborative Decision-Making: Bridging Uncertainty and Consensus.
Published In: Cuestiones de Fisioterapia, 2025, v. 54, n. 3. P. 329 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Bhagat, Surendra Nath; Rath, Premansu Sekhar; Mitra, Anirban 3 of 3
Abstract
A useful technique for dealing with ambiguity and impreciseness in judgment situations is soft set theory. To assess how well soft sets, fuzzy soft sets, and fuzzy soft sets handle ambiguity and variability, this study investigates their use in a group decision-making framework. Fuzzy soft sets expand the adaptable framework of soft sets by adding degrees of membership, allowing for a more thorough examination of challenging decision-making issues. These techniques are especially useful when making decisions in groups since they make it easier to incorporate different personal preferences, which enhances the process of reaching consensus. Despite their benefits, soft sets and fuzzy soft sets have not yet reached their full potential in group decision-making, particularly when resolving opposing preferences and differing degrees of certainty. This study's main goal is to compare these methods' accuracy, dependability, and effectiveness. Through their application to a real-world example involving the selection of the most suitable clinic for physiotherapy from a pool based on a number of criteria and the preferences of a panel of decision-makers, the study shows how all three approaches can effectively manage uncertainty and improve decision-making outcomes. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Cuestiones de Fisioterapia. 2025/09, Vol. 54, Issue 3, p329
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:1135-8599
- Accession Number:186684053
- Copyright Statement:Copyright of Cuestiones de Fisioterapia is the property of Cuestiones de Fisioterapia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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