Attribute reduction approaches under interval-valued q-rung orthopair fuzzy soft framework

The interval-valued q -rung orthopair fuzzy sets and soft sets are two different uncertainty theories to cope with incomplete and uncertain information in several real-world multi-attribute decision-making (MADM) situations. This study develops a novel hybrid model called interval-valued q -rung ort...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2022-06, Vol.52 (8), p.8975-9000
Main Authors: Ali, Ghous, Afzal, Muhammad, Asif, Muhammad, Shazad, Adeel
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Decision making
Fuzzy sets
Hierarchies
Machines
Manufacturing
Mechanical Engineering
Plant reliability
Power plants
Processes
Reduction
Site selection
Wind power
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Summary:The interval-valued q -rung orthopair fuzzy sets and soft sets are two different uncertainty theories to cope with incomplete and uncertain information in several real-world multi-attribute decision-making (MADM) situations. This study develops a novel hybrid model called interval-valued q -rung orthopair fuzzy soft sets (IV q ROFSSs, for brevity) to generalize the interval-valued q -rung orthopair fuzzy set model and to address the decision-makers preference information more effectively in complicated MADM processes. Afterward, some basic useful properties of the proposed model are explored, including subset relation, complement, union, intersection, the ‘AND’ operation, and the ‘OR’ operation. Further, four kinds of attribute reduction techniques for IV q ROFSSs are presented. An algorithm for each reduction approach is developed and explained through an illustrative numerical example which verifies that developed reduction methods remove the redundant attributes by preserving the ranking order of decision objects unchanged. Later on, an application is proposed, that is, site selection for a wind power plant, to explain the developed model’s reliability and its reduction approaches. Finally, the proposed hybrid model and its attribute reduction methods are compared with some existing models, and their attribute reduction approaches, respectively.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02853-x