The recently proposed q-rung dual hesitant fuzzy sets ( q-RDHFSs) not only deal with decision makers’ (DMs’) hesitancy and uncertainty when evaluating the performance of alternatives, but also give them great liberty to express their assessment information comprehensively. This paper aims to propose a new multiple attribute decision-making (MADM) method where DMs’ evaluative values are in form of q-rung dual hesitant fuzzy elements ( q-RDHFEs). Firstly, we extend the powerful Schweizer-Sklar q-norm and t-conorm (SSTT) to q-RDHFSs and propose novel operational rules of q-RDHFEs. The prominent advantage of the proposed operations is that they have important parameters q and r, making the information fusion procedure more flexible. Secondly, to effectively cope with the interrelationship among attributes, we extend the Hamy mean (HM) to q-RDHFSs and based on the newly developed operations, we propose the q-rung dual hesitant fuzzy Schweizer-Sklar Hamy mean ( q-RDHFSSHM) operator, and the q-rung dual hesitant fuzzy Schweizer-Sklar weighted Hamy mean ( q-RDHFSSWHM) operator. The properties of the proposed operators, such as idempotency, boundedness and monotonicity are discussed in detail. Third, we propose a new MADM method based on the q-RDHFSSWHM operator and give the main steps of the algorithm. Finally, the effectiveness, flexibility and advantages of the proposed method are discussed through numerical examples.

The ability of q-rung dual hesitant fuzzy sets (q-RDHFSs) in dealing with decision makers’ fuzzy evaluation information has received much attention. This main aim of this paper is to propose new aggregation operators of q-rung dual hesitant fuzzy elements and employ them in multi-attribute decision making (MADM). In order to do this, we first propose the power dual Maclaurin symmetric mean (PDMSM) operator by integrating the power geometric (PG) operator and the dual Maclaurin symmetric mean (DMSM). The PG operator can reduce or eliminate the negative influence of decision makers’ extreme evaluation values, making the final decision results more reasonable. The DMSM captures the interrelationship among multiple attributes. The PDMSM takes the advantages of both PG and DMSM and hence it is suitable and powerful to fuse decision information. Further, we extend the PDMSM operator to q-RDHFSs and propose q-rung dual hesitant fuzzy PDMSM operator and its weighted form. Properties of these operators are investigated. Afterwards, a new MADM method under q-RDHFSs is proposed on the basis on the new operators. Finally, the effectiveness of the new method is testified through numerical examples.