Intuitionistic Hesitant Fuzzy Partitioned Maclaurin Symmetric Mean Aggregation Operators-Based Algorithm and Its Application in Decision Making

Intuitionistic hesitant fuzzy sets, which enable the representation of an element's membership and non-membership as a set of multiple possible values, offer significant utility in describing uncertainty in various aspects of people's daily lives. Fundamental mathematical techniques known as intuitionistic hesitant fuzzy aggregation operators are employed to merge multiple inputs into a single result based on predetermined criteria. However, conventional approaches that rely on classic intuitionistic hesitant fuzzy aggregation operators have faced criticism due to their limited understanding of criteria characterization. We introduce two novel operators, namely the intuitionistic hesitant fuzzy partitioned Maclaurin symmetric mean (IHFPMSM) and intuitionistic hesitant fuzzy weighted partitioned Maclaurin symmetric mean (IHFWPMSM), which draw inspiration from the partitioned Maclaurin symmetric mean concept. Subsequently, we thoroughly examine various characteristics and special cases of these operators. Building upon the IHFWPMSM operator, we propose a novel multiple-criteria decision-making (MCDM) method that effectively selects the most suitable alternative from a set of options. To demonstrate the effectiveness of our proposed approach, we discuss a systematic methodology for selecting the optimal location for shoe company construction. Lastly, we demonstrate the superior prevalence and effectiveness of the developed approach through comprehensive comparative and sensitivity analyses, surpassing the capabilities of existing approaches.