Heronian mean operators of linguistic neutrosophic multisets and their multiple attribute decision making methods is popular PDF and ePub book, written by Changxing Fan in 2024-09-21, it is a fantastic choice for those who relish reading online the Mathematics genre. Let's immerse ourselves in this engaging Mathematics book by exploring the summary and details provided below. Remember, Heronian mean operators of linguistic neutrosophic multisets and their multiple attribute decision making methods can be Read Online from any device for your convenience.
Heronian mean operators of linguistic neutrosophic multisets and their multiple attribute decision making methods Book PDF Summary
A valid aggregation operator can reflect the decision result more clearly and make the decision effect more correctly. In this article, a linguistic neutrosophic multiset is first proposed to handle the multiplicity information, which is an expanding of neutrosophic multiset. Two Heronian mean operators are proposed to aggregate the linguistic neutrosophic multiset, one is a linguistic neutrosophic multiplicity number generalized-weighted Heronian mean operator, the other is a linguistic neutrosophic multiplicity number improved-generalized-weighted Heronian mean operator, and then their properties are discussed. Furthermore, two decision-making methods are introduced based on linguistic neutrosophic multiplicity number generalized-weighted Heronian mean or linguistic neutrosophic multiplicity number improved-generalized-weighted Heronian mean operators under linguistic neutrosophic multiplicity number environment. Finally, an illustrative example is used to indicate the practicality and validity of these two methods.
Detail Book of Heronian mean operators of linguistic neutrosophic multisets and their multiple attribute decision making methods PDF
- Author : Changxing Fan
- Release : 21 September 2024
- Publisher : Infinite Study
- ISBN : 978186723xxxx
- Genre : Mathematics
- Total Page : 13 pages
- Language : English
- PDF File Size : 20,5 Mb
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