Complex Fermatean fuzzy partitioned Maclaurin symmetric mean operators and their application to hostel site selection

Complex Fermatean fuzzy (CFF) set is a powerful mathematical model that handles uncertain data effectively. However, existing multi-criteria decision-making (MCDM) methods based on CFF sets do not take into account the interconnections among multiple criteria and are unable to reduce the impact of extreme values. To address these issues, this paper proposes novel operators, named CFF partitioned Maclaurin symmetric mean (CFFPMSM) and CFF weighted partitioned Maclaurin symmetric mean (CFFWPMSM) to handle scenarios where criteria are divided into distinct parts and there are interconnections among multiple criteria within the same part. The favourable characteristics of the PMSM and PWMSM operators are extensively examined. To alleviate the negative impact of impractical evaluation values for criteria on the aggregated outcome, additional operators, known as CFF power partitioned Maclaurin symmetric mean (CFFPPMSM) and CFF weighted power partitioned Maclaurin symmetric mean (CFFWPPMSM) are introduced. These operators integrate the PWMSM operator with the power average (PA) operator within the CFF context. The weights of the criteria are assumed to be unknown and are determined using a statistical method. Subsequently, a novel MCDM approach is presented, employing the proposed AOs and the statistical variance method. Afterwards, a numerical instance is provided to exemplify the application of the proposed approach in determining the optimal location for a university hostel. Finally, a comparative analysis of the designed MCDM approach with different existing approaches is carried out. The findings indicate that incorporating criteria partitioning into the proposed method helps alleviate the negative effects of irrelevant criteria. Additionally, combining the PA operator with the proposed operator reduces the influence of extreme values, resulting in more feasible and reliable results.