Partitioned Maclaurin symmetric mean operators in bipolar complex fuzzy sets for multiattribute decision making

Mathematical tools are crucial for dealing with uncertainty because they provide a rigorous and logical framework for evaluating, measuring, and making decisions in the presence of ambiguous information. The bipolar complex fuzzy is one of the mathematical methods for simultaneously handling dual aspect and second-dimensional information. Thus, in this script, we propound aggregation operators "partitioned Maclaurin symmetric mean and partitioned dual Maclaurin symmetric mean" within bipolar complex fuzzy set that is bipolar complex fuzzy partitioned Maclaurin symmetric mean and bipolar complex fuzzy partitioned dual Maclaurin symmetric mean, bipolar complex fuzzy weighted partitioned Maclaurin symmetric mean and bipolar complex fuzzy weighted partitioned dual Maclaurin symmetric mean operators. We also propound the related axioms of the invented operators. By employing the deduced aggregation operators, we produce a technique of multiattribute decision making within bipolar complex fuzzy sets to overcome awkward uncertainties. After that, we demonstrate an explanatory example for revealing the significance and practicability of the deduced theory and then we analyze the reliability and legitimacy of the propounded operators by comparing them with some prevailing work.