Linguistic q-rung orthopair fuzzy multiple-attribute group decision making based on the grey similarity degree and PROMETHEE II method

Abstract

Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are an effective tool for representing fuzzy linguistic information, and they can obtain a wider expression scope than linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers by increasing the value of parameter q. In this paper, we propose a new similarity measure called the grey similarity degree between any two Lq-ROFNs based on the concept of the grey correlation degree. Considering the significance of determining unknown weights, we also propose a grey correlation method to determine each expert’s weight under different alternatives and attributes, and we construct an optimization model to determine incompletely known attribute weights. Furthermore, an approach to linguistic q-rung orthopair fuzzy multiple-attribute group decision making is proposed that combines the grey similarity degree with the PROMETHEE II method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method, and a sensitivity analysis and comparison analysis are also performed.

Incomplete linguistic q-rung orthopair fuzzy preference relations and their application to multi-criteria decision making

“…Considering that uncertainty and inaccuracy exist in most real decision-making problems, the Lq-ROFS with corresponding decision making algorithms can be employed in fault detection [38,39], robot selection problem [40,41], nonzero-sum game [42,43], global supplier searching [44,45] and impulsive decision-making [46,47]. Readers can also refer to [48][49][50]. In this paper, we will use the proposed Lq-ROFPRs to deal with three practical MCDM problems: the company manager selecting problem, the typhoon emergency plan choosing problem and the mobile phones evaluating problem.…”