Abstract

Decision-making (DM) information is a crucial and prevalent way to assess the majority of difficulties in real-life challenges, allowing one to determine the optimal choice from the collection of preferences. To prioritize aggregation operators (PAOs), the bundled data is first aggregated into a singleton set, which is calculated using algebraic laws. Hamy mean (HM) information is also employed. Proposing Dombi operational laws for Interval value spherical fuzzy (IVSF) information and attempting to build the theory of HM information based on Dombi t-norm (TN) and t-conorm (T-CN) while taking into account SF information are the main contributions of this theory. These operators are referred to as IVSF prioritized Dombi HM (IVSFPDHM), IVSF weighted prioritized Dombi HM (IVSFWPDHM), IVSF prioritized Dombi dual HM (IVSFPDDHM), and IVSF weighted prioritized Dombi dual HM (IVSFWPDDHM) operators. We also assessed a few important characteristics and findings for the data that were provided in the examined analysis. Additionally, this book uses MADM "multi-attribute decision-making" information based on pioneering operators and provides some examples to support the importance and dominance of the assessed information. In order to improve the quality of the generated operators, we lastly contrasted the assessed information with some other outdated or current information.