Multiple-attribute decision making for green sustainable recycling partner selection based on linguistic r, s, t-spherical fuzzy aggregation information

In present-day decision science, multiple-attribute decision making is an important area of research for choosing the right alternative among several possible alternatives. To address this issue, this paper proposes a novel multiple-attribute decision making problem based on linguistic r, s, t-spherical fuzzy information. The main purpose of the current study is to explore a novel linguistic r, s, t-spherical fuzzy score function and extend the measurement of alternatives and ranking according to the multiple-attribute decision making method with weight information to linguistic r, s, t-spherical fuzzy variables. Firstly, we propose the concept of linguistic r, s, t-spherical fuzzy variable, where the linguistic positive-membership, the linguistic neutral-membership and the linguistic negative-membership are represented by linguistic variables, and its operation rules are also discussed. The linguistic r, s, t-spherical fuzzy weighted averaging and linguistic r, s, t-spherical fuzzy weighted geometric operator are developed based on the proposed operation rules. Following that, the linguistic r, s, t-spherical fuzzy multiple-attribute decision making approach is established to cope with multiple-attribute decision making problems. Later, a case study of a selecting the suitable green supplier is addressed to show the practicality of the presented method. The comparison analysis with other existing operators shows the reliability of our work.