Abstract
The q-rung picture fuzzy sets act as a proficient and extensive extension of q-rung orthopair fuzzy sets and picture fuzzy sets within fuzzy set theory. The parameter q and the three real-valued membership functions enable us to perform better than existing approaches in describing mysterious data. Here, we built aggregation operators for the q-RPLFS framework using the Schweizer and Sklar (SS) operations. We introduced and analyzed several kinds of aggregation operators in detail, including the q-rung picture linguistic SS weighted averaging operator (q-RPLSSAO) and the q-rung picture linguistic SS geometric operator (q-RPLSSGO). The q-RPLFS framework for solving MADM problems contains SS t-norms and t-conorms, allowing the generated operators to make the information aggregation technique more flexible than existing ones. Additionally, we described a numerical example to support the applicability and advantages of the suggested approach. To confirm the accuracy and feasibility of the suggested approaches, comparison results with the current methods are also provided.