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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 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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ALGORITHM FOR PICTURE FUZZY MULTIPLE ATTRIBUTE DECISION-MAKING BASED ON NEW DISTANCE MEASURE

Volume 7, Issue 2, 2017, pp. 177-187
DOI: 10.1615/Int.J.UncertaintyQuantification.2017020096
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ABSTRACT

Picture fuzzy sets are an extension of intuitionistic fuzzy set which can model the situations involving more answers of these types: yes, abstain, no, refusal. It is a powerful tool to deal with multiple attribute decision-making (MADM) problems. In this paper, we initiate a new axiomatic definition of picture fuzzy distance measure, which is presented by the picture fuzzy number (PFN) that will reduce the information loss and keep more original information. Meanwhile, the comparison method is proposed. Then, the objective weights of various attributes are determined via gray systems theory. Combing objective weights with subjective weights, we introduce the combined weights. Later, we give an algorithm to solve the MADM problem based on distance measure. Finally, the effectiveness and feasibility of the algorithm is demonstrated by a numerical example.

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