 Determining the membership degrees in the range (0, 1) for hypersoft sets independently of the decision-makerOrhan Dalkılıç International Journal of Systems Science, 2022, vol. 53, issue 8, 1733-1743 Abstract: Today, Molodtsov's soft set has been generalised to hypersoft sets, and the use of hypersoft set theory for uncertain data has become more preferable than soft sets. However, the membership degree of an object in hypersoft sets is 0 or 1. In order to express this situation in the range $ (0,1) $ (0,1), many mathematical models have been constructed by considering hypersoft sets together with fuzzy sets and their derivatives. However, these mathematical models require the decision-maker to express the membership degrees. It is a very difficult task for a decision-maker to determine a value in $ (0,1) $ (0,1) and the probability of an error is very high. For this reason, the concepts relational hypersoft membership degree and inverse relational hypersoft membership degree, which are given less dependent on decision-makers, are proposed in this paper. Moreover, two decision-making algorithms are given to use these concepts in an environment of uncertainty. Finally, the decision-making process for the given algorithms is analysed. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:53:y:2022:i:8:p:1733-1743 Ordering information: This journal article can be ordered from DOI: 10.1080/00207721.2021.2023686 Access Statistics for this article International Journal of Systems Science is currently edited by Visakan Kadirkamanathan More articles in International Journal of Systems Science from Taylor & Francis Journals |
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