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Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm


Yang, Z and Garg, H and Li, J and Srivastava, G and Cao, Z, Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm, Neural Computing and Applications pp. 1-16. ISSN 0941-0643 (2020) [Refereed Article]

Copyright Statement

Copyright 2020 Springer-Verlag London Ltd, part of Springer Nature

DOI: doi:10.1007/s00521-020-05003-5


Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods under q-ROF environment have been developed in recent years. However, most existing studies merely concerned about the relationship between the criteria but they have not investigated the interactions between membership function and non-membership function. To explore the multiple heterogeneous relationships among membership functions and criteria, we propose a novel decision algorithm based on q-ROF set to deal with these using interactive operators and Maclaurin symmetric mean (MSM) operators. Specifically, the new interaction laws in the membership pairs of q-ROF sets are explained, and their properties are analyzed as the initial stage. Then, taking into account the influence of two or more factors on decision analysis, a q-ROF interaction Maclaurin symmetry mean (q-ROFIMSM) operator is formed based on the proposed interaction law to identify these factors' interrelationship. Thirdly, based on the proposed operator with q-ROF information, a MCDM algorithm is developed and illustrated by numerical examples. An analysis of the feasibility, sensitivity, and superiority of the proposed framework is provided to validate our proposed method.

Item Details

Item Type:Refereed Article
Keywords:q-rung orthopair fuzzy set, multiple heterogeneous relationships, interaction operators, Maclaurin symmetric, mean operators, decision
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:139031
Year Published:2020
Web of Science® Times Cited:27
Deposited By:Information and Communication Technology
Deposited On:2020-05-22
Last Modified:2020-08-17

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