Q-rung orthopair fuzzy weighted induced logarithmic distance measures and their application in multiple attribute decision making. (April 2021)

• Record Type: Journal Article
• Authors: Zeng, Shouzhen; Hu, Yingjie; Xie, Xiaoying
• Is Part Of: Engineering applications of artificial intelligence. Volume 100(2021)
• Publication Date: 2021-04
• Subjects: Q-rung orthopair fuzzy set -- Logarithmic distance measure -- Induced aggregation -- MADM; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104167
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: As a more flexible and practical approach than the Pythagorean fuzzy set and intuitionistic fuzzy set, the q-rung orthopair fuzzy set (q-ROFS) has been widely used to express vagueness and uncertainty. In this paper, a method based on a weighted induced logarithmic distance is presented to help address multiple attribute decision making (MADM) with q-ROFS information. A new induced weighted logarithmic distance measure is first proposed to remedy the shortcomings of existing methods. Some outstanding properties have also been examined in detail. Considering the superiority of q-ROFS in modeling uncertainties, the improved induced weighted logarithmic distance measure is then extended to q-ROFS, thereby obtaining two new q-ROFS distance measures. Moreover, based on the developed q-ROFS distance measures, a new method for handling MADM problems under q-ROFS environment is presented, wherein information concerning the attribute weights is completely unknown. Finally, a numerical example concerning smart phone selection is presented to demonstrate the validity and superiority of the proposed method. Highlights: The WIOWLAD measure is proposed for removing the existing shortcoming. The q-RWIOWLAD measure is proposed to handle q-ROFS information. The q-RWICWLAD is presented by unifying the q-RWIOWLAD and weighted average method. The influences of the parameters on the ranking results are analyzed in detail.