Inscrição na biblioteca: Guest
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

Indexed in

A PARTIAL LEAST-SQUARES PATH MODEL FOR MULTIATTRIBUTE DECISION-MAKING UNDER FUZZY ENVIRONMENT

Volume 8, Edição 2, 2018, pp. 123-141
DOI: 10.1615/Int.J.UncertaintyQuantification.2018020755
Get accessGet access

RESUMO

In practical multiattribute decision-making problems, attributes are often correlated and some attributes (latent attributes) that play significant parts in evaluating alternatives cannot be directly observed, leading to an incorrect result. This paper proposes a partial least-squares path model for multiattribute decision-making under a triangular fuzzy environment, which not only addresses interaction between attributes but also fully reveals the effects of latent attributes on the evaluation of alternatives, and their weights are objectively assigned. First, utilizing a least-squares method, a triangular fuzzy regression model is built with the defuzzification of the residual sum of squares. On the basis of a triangular fuzzy regression model, an iterative algorithm is proposed for a triangular fuzzy partial least-squares path model. Four indexes are given to investigate the goodness of the proposed model. Then the procedure of the triangular fuzzy partial least-squares path model-based multiattribute decision-making is introduced. Finally, an illustrated example is provided to demonstrate the feasibility and validity of the proposed method.

Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain