Evidence map›Paper›PMID 34673796›Full record

ArticlePloS one2021

Multiple attribute group decision-making based on interval-valued q-rung orthopair uncertain linguistic power Muirhead mean operators and linguistic scale functions.

Yuan Xu, Shifeng Liu, Jun Wang

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yuan XuSchool of Economics and Management, Beijing Jiaotong University, Beijing, China.
Shifeng LiuSchool of Economics and Management, Beijing Jiaotong University, Beijing, China.
Jun WangSchool of Economics and Management, Beijing University of Chemical Technology, Beijing, China.ORCID 0000-0001-7522-8593

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fuzzy set theory and its extended form have been widely used in multiple-attribute group decision-making (MAGDM) problems, among which the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) got a lot of attention for its ability of capturing information denoted by interval values. Based on the previous studies, to find a better solution for fusing qualitative quantization information with fuzzy numbers, we propose a novel definition of interval-valued q-rung orthopair uncertain linguistic sets (IVq-ROULSs) based on the linguistic scale functions, as well as its corresponding properties, such as operational rules and the comparison method. Furthermore, we utilize the power Muirhead mean operators to construct the information fusion method, and provide a variety of aggregation operators based on the proposed information description environment. A model framework is constructed for solving the MAGDM problem utilizing the proposed method. Finally, we illustrate the performance of the new method and investigate its advantages and superiorities through comparative analysis.

Indexed as

AlgorithmsDecision MakingGroup ProcessesFuzzy LogicHumansLinguisticsPatient TransferReferral and ConsultationResearch DesignUncertainty

Identifiers

PMID34673796
PMCPMC8530323

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.