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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

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A NEW IMPROVED SCORE FUNCTION OF AN INTERVAL-VALUED PYTHAGOREAN FUZZY SET BASED TOPSIS METHOD

Volume 7, Edição 5, 2017, pp. 463-474
DOI: 10.1615/Int.J.UncertaintyQuantification.2017020197
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RESUMO

In the present study, an improved score function for the ranking order of interval-valued Pythagorean fuzzy sets (IVPFSs) has been proposed. Based on it, a Pythagorean fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) method by taking the preferences of the experts in the form of interval-valued intuitionistic Pythagorean fuzzy decision matrices has been presented. A positive and negative ideal separation measures solution has been computed based on the proposed score function to determine the relative closeness coefficient and hence the most desirable one/s is/are selected. Finally, an illustrative example for a multicriteria decision-making (MCDM) problem has been taken to demonstrate the proposed approach.

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