Multiple attribute decision making based on q-rung orthopair fuzzy generalized Maclaurin symmetic mean operators

Abstract

In the article, we establish two multiple attribute decision making (MADM) approaches using the developed weighted generalized Maclaurin symmetric mean (q-ROFWGMSM) and weighted generalized geometric Maclaurin symmetric mean (q-ROFWGGMSM) operator concerning q-rung orthopair fuzzy numbers (q-ROFNs). Firstly, inspired by the generalized Maclaurin symmetric mean (G-MSM) and geometric Maclaurin symmetric mean (Geo-MSM) operators, we establish the q-rung orthopair fuzzy G-MSM (q-ROFGMSM) and q-rung orthopair fuzzy Geo-MSM (q-ROFGGMSM) operators, which assumes the grades of membership and non-membership to evaluate information can take any values in interval [0,1] respectively and the attributes are relevant to other multiple attributes. Then, we present its characteristics and some special cases. Moreover, we propose the weighted forms of the q-ROFGMSM and q-ROFGGMSM operator, which is called the q-ROFWGMSM and q-ROFWGGMSM operators, respectively. Then, we present their some characteristics and special examples. Finally, we put forward two new MADM approaches founded on the developed q-ROFWGMSM and q-ROFWGGMSM operators. The developed approaches are more general and more practicable than Liu and Wang's MADM approach (2018), Wei and Lu's MADM method (2017), Qin and Liu's MADM method (2014) and Shen et al.’s MADM approach (2018).