Partitioned Hamy mean aggregation for multi-criteria group decision-making in the MAIRCA framework with -rung orthopair fuzzy 2-tuple linguistic information

In the context of multi-criteria group decision-making (MCGDM), the process involves categorizing criteria into distinct groups based on their inherent characteristics through a partitioning method. This research aims to create the partitioned Hamy mean (PHM) and the partitioned dual Hamy mean (PDHM) operators in the -rung orthopair fuzzy 2-tuple linguistic (q-ROF2L) environment, namely the q-ROF2L PHM (q-ROF2LPHM), the q-ROF2L PDHM (q-ROF2LDHM), and their corresponding weighted versions. Meanwhile, the fundamental properties of the presented aggregation operators (AOs) are verified to ensure their validity. Furthermore, the research also develops an integrated weighting approach by combining the objective weight determination model based on distances between criteria and the subjective weight assessment model called pivot pairwise relative criteria importance assessment approach for criteria weight determination. Subsequently, two distinct MCGDM algorithms, multi-attribute ideal-real comparative assessment and aggregation-based approaches are devised for addressing MCGDM challenges within the q-ROF2L environment. To demonstrate the practicality and efficiency of these approaches, a case study of assessing the research capabilities of lecturers while considering multiple criteria into consideration. Finally, the research concludes with a thorough discussion of sensitivity analysis and comparative assessments, providing insights into the feasibility and stability of the introduced approach for assessing the research capabilities of lecturers.