4.7 Article

Multi-attribute group decision-making methods based on q-rung orthopair fuzzy linguistic sets

Journal

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Volume 34, Issue 6, Pages 1129-1157

Publisher

WILEY-HINDAWI
DOI: 10.1002/int.22089

Keywords

linguistic scale functions; multi-attribute group decision making; q-rung orthopair fuzzy set; q-rung orthopair fuzzy linguistic set; q-rung orthopair fuzzy linguistic weighted average operator; q-rung orthopair fuzzy linguistic weighted geometric operator

Funding

  1. Social Sciences Planning Project of Shandong Province [13BJJJ01]
  2. Natural Science Foundation of China [71401012, 91224007]
  3. Taishan Scholars Project of Shandong Province [ts201511045]

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With the continuous development of the economy and society, decision-making problems and decision-making scenarios have become more complex. The q-rung orthopair fuzzy set is getting more and more attention from researchers, which is more general and flexible than Pythagorean fuzzy set and intuitionistic fuzzy set under complex vague environment. In this study, the concept of q-rung orthopair fuzzy linguistic set (q-ROFLS) is proposed and a new q-rung orthopair fuzzy linguistic method is developed to handle MAGDM problem. Firstly, the conception, operation laws, comparison methods, and distance measure methods of the q-ROFLS are proposed. Secondly, the q-ROFL weighted average operator, q-ROFL ordered weighted average operator, q-ROFL hybrid weighted average operator, q-ROFL weighted geometric operator, q-ROFL ordered weighted geometric operator, and q-ROFL hybrid weighted geometric operator are proposed, and some interesting properties, special cases of these operators are investigated. Furthermore, a new method to cope with MAGDM problem based on q-ROFL weighted average operator (q-ROFL weighted geometric operator) is developed. Finally, a practical example for suppliers selection is provided to verify the practicality of the presented method, and the effectiveness and flexibility of the presented method are illustrated by sensitive analysis and comparative analysis.

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