Maximizing deviation method for multiple attribute decision making under q-rung orthopair fuzzy environment

Defence Technology

Cun Wei¹  Gui-wu Wei²  Jie Wang²  Jiang Wu³ 

[1] Corresponding author.;School of Business, Sichuan Normal University, Chengdu, 610101, PR China;School of Statistics, Southwestern University of Finance and Economics, Chengdu, 611130, PR China;

关键词: Multiple attribute decision making (MADM);    q-rung orthopair fuzzy sets (q-ROFSs);    q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs);    Maximizing deviation method;    Building materials;   

DOI  :  

来源: DOAJ

【 摘 要 】

Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment, the evaluation information of alternatives given by decision makers is often fuzzy and uncertain. As a generalization of intuitionistic fuzzy set (IFSs) and Pythagoras fuzzy set (PFSs), q-rung orthopair fuzzy set (q-ROFS) is more suitable for expressing fuzzy and uncertain information. But, in actual multiple attribute decision making (MADM) problems, the weights of DMs and attributes are always completely unknown or partly known, to date, the maximizing deviation method is a good tool to deal with such issues. Thus, combine the q-ROFS and conventional maximizing deviation method, we will study the maximizing deviation method under q-ROFSs and q-RIVOFSs in this paper. Firstly, we briefly introduce the basic concept of q-rung orthopair fuzzy sets (q-ROFSs) and q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs). Then, combine the maximizing deviation method with q-rung orthopair fuzzy information, we establish two new decision making models. On this basis, the proposed models are applied to MADM problems with q-rung orthopair fuzzy information. Compared with existing methods, the effectiveness and superiority of the new model are analyzed. This method can effectively solve the MADM problem whose decision information is represented by q-rung orthopair fuzzy numbers (q-ROFNs) and whose attributes are incomplete.

【 授权许可】

Unknown