Decision-making algorithm with complex hesitant fuzzy partitioned maclaurin symmetric mean aggregation operators and SWARA method

Abstract

Complex hesitant fuzzy sets (CHFSs) have emerged as a powerful tool for addressing uncertainty, especially in representing two-dimensional information through multiple possible values. This study addresses the limitations in existing methodologies by introducing two novel operators: the complex hesitant fuzzy partitioned Maclaurin symmetric mean (CHFPMSM) and the complex hesitant fuzzy weighted partitioned Maclaurin symmetric mean (CHFWPMSM). These operators enable effective aggregation of criteria by organizing them into independent partitions based on inherent properties, enhancing their applicability to real-world multi-criteria decision-making (MCDM) problems. To validate the reliability of these operators, essential properties such as idempotency, monotonicity, and boundedness are verified. The study further extends the stepwise weight assessment ratio analysis (SWARA) method to the CHFS framework, facilitating the derivation of attribute weights under uncertain and complex conditions. A robust MCDM methodology is then proposed, integrating the newly developed operators and the extended SWARA approach to address two-dimensional decision-making challenges effectively. The proposed methodology is applied to a practical case study of selecting the best supplier for electronic goods among five alternatives. Comprehensive sensitivity analysis is conducted to examine the stability of the decision-making process against variations in criteria weights. Additionally, a comparative analysis underscores the novelty and efficiency of the proposed methodology by benchmarking its results against existing approaches.