Multiple attribute group decision making based on q‐rung orthopair fuzzy Heronian mean operators. Issue 12 (23rd July 2018)

• Record Type: Journal Article
• Authors: Liu, Zhengmin; Wang, Song; Liu, Peide
• Is Part Of: International journal of intelligent systems. Volume 33:Issue 12(2018)
• Page Start: 2341
• Page End: 2363
• Publication Date: 2018-07-23
• Subjects: Heronian mean -- multiple attribute group decision making -- partition structure -- q‐rung orthopair fuzzy set; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22032
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The q‐rung orthopair set (q‐ROFSs) can serve as a generalization of the existing orthopair fuzzy sets, including intuitionistic fuzzy sets and Pythagorean fuzzy sets. The most desirable characteristic of q‐ROFSs is that they support a greater space of allowable membership grades and provide decision makers more freedom in describing their true opinions. As a classical aggregation operator, Heronian mean (HM) can model the interrelationship between attributes. In this paper, we extend the traditional HM to aggregate q‐rung orthopair fuzzy information and propose the q‐rung orthopair fuzzy HM and its weighted form. Further, to overcome the shortcomings of the traditional HM, considering the possible partition structure in the actual decision situations, we propose the q‐rung orthopair fuzzy partitioned Heronian mean operator and the q‐rung orthopair fuzzy weighted partitioned Heronian mean operator. Then, some special cases and some desirable properties are investigated and discussed. A new multiple attribute group decision‐making(MAGDM) technique is developed based on the proposed q‐rung orthopair fuzzy operators. Finally, a representative example is provided to verify the effectiveness and superiority of the proposed method by comparing with other several existing representative MAGDM methods.