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Erschienen in: Neural Computing and Applications 17/2021

21.05.2020 | S. I : Hybridization of Neural Computing with Nature Inspired Algorithms

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

verfasst von: Zaoli Yang, Harish Garg, Jinqiu Li, Gautam Srivastava, Zehong Cao

Erschienen in: Neural Computing and Applications | Ausgabe 17/2021

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Abstract

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.

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Metadaten
Titel
Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm
verfasst von
Zaoli Yang
Harish Garg
Jinqiu Li
Gautam Srivastava
Zehong Cao
Publikationsdatum
21.05.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 17/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05003-5

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