In complex and uncertain environment, Multi-attribute decision-making (MADM) remains a significant challenge, particularly when existing MADM techniques struggle to differentiate between the preference orders (POs) of the alternatives. To address this problem, this paper presents a novel technique for MADM within the context of \(p, q\) -quasirung orthopair fuzzy numbers ( \(p, q\) -QOFNs). To achieve this, we first develop a novel possibility degree measure (PDM) to compare different \(p, q\) -QOFNs and provide some notable characteristics. In the process of aggregating information, Schweizer-Sklar norms offer greater flexibility. To fully utilize the advantages of these norms, we propose new operational laws for \(p, q\) -QOFNs based on these norms. Then, by using the proposed operations, we propose the \(p, q\) -quasirung orthopair fuzzy Schweizer-Sklar weighted arithmetic ( \(p, q\) -QOFSSWA) and geometric ( \(p, q\) -QOFSSWG) aggregation operator (AO). Also, we discuss some properties of the proposed \(p, q\) -QOFSSWA AO and \(p, q\) -QOFSSWG AO. Finally, we develop a novel MADM technique for \(p, q\) -QOFNs incorporating the proposed PDM and the \(p, q\) -QOFSSWA AO and \(p, q\) -QOFSSWG AO. In order to showcase the efficacy and superiority of the proposed MADM technique, we provide numerical examples. These examples illustrate that the new MADM technique effectively addresses the limitations of existing MADM techniques, particularly in situations where they are unable to differentiate between the POs of the alternatives.
