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Gray Uncertain Linguistic Multiattribute Group Decision Making Method Based on GCC-HCD

Bibliographic Data

ID22107361
AuthorsCongjun Rao (0000-0002-1045-559X, Wuhan University of Technology), Cheng Wang (0000-0002-4693-6424, Hubei University of Education), Zhuo Hu (0000-0001-7735-7807, Wuhan University of Technology), Xinping Xiao (0009-0002-8695-8465, Wuhan University of Technology), Mark Goh (0000-0002-3620-7658, National University of Singapore)
Year2023
Volume10
Issue2
Pages523-537
Publication date2023-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2022.3166526
OpenAlexW4225749843
LanguageEN
Citations received6
References cited67

This article presents a multiattribute group decision-making (MAGDM) method using cloud models to handle decision problems with gray uncertain linguistic variables that contain both subjective and objective uncertainty. In this new method, the new concepts of gray uncertain linguistic variable and gray comprehensive cloud are defined, and a method for converting gray uncertain linguistic variables into gray comprehensive clouds is provided. A gray comprehensive clouds-weighted averaging (GCC-WA) operator is proposed to aggregate the information with multiple gray comprehensive clouds. A distance measure between two gray comprehensive clouds is defined to form the gray comprehensive cloud-hybrid closeness degree (GCC-HCD), which is then applied to rank the alternatives. An example of an offshore mining investment is applied to validate the method. Sensitivity analysis and comparison analysis are carried out with other congeneric methods

Closeness · Cloud computing · Data mining · Advanced Decision-Making Techniques · Computer Science · Grey System Theory Applications · Mathematics · Multi-Criteria Decision Making · Artificial Intelligence

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Unique citing works6
Citations per year2
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 6

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