Risk preference evaluation approach based on the Choquet integral in q-rung orthopair fuzzy environment and its automotive performance assessment

A q -rung orthopair fuzzy number ( q -ROFN) is a highly regarded and effective tool in intelligent computing and decision science, its unique representation ability and theoretical methods are widely used in many engineering fields, especially, they can handle some group decision-making problems with multi-attribute fuzzy information on a wider region. The main objective of this study is to overcome the shortcomings of existing ranking methods related to q -ROFNs and seek a more reasonable ranking criterion, and then establish a novel risk evaluation approach through the aggregation operator of discrete Choquet integrals in a q -ROFN environment. In this article, some related definitions for q -rung orthopair fuzzy numbers are introduced, a few defects of its traditional score functions are pointed out, and then a new risk preference score function with its ranking rule are given by introducing a q -level compression mapping and risk factor. Secondly, it is demonstrated that the novel risk score function satisfies monotonicity and the rationality of ranking criterion, two types of Choquet integral average (or geometric) aggregation operators are proposed in a q -rung orthopair fuzzy environment, and several calculation examples are provided. Finally, the novel aggregation operator and risk preference evaluation approach are established by the Choquet integral average operator and risk preference score function, and apply them to the assessment of automotive performances, and then their validity and superiority are verified by an example.