Radioactive waste reduction through transmutation techniques is significant for mitigating environmental risks and ensuring long-term safety. It offers a sustainable approach to minimizing the volume of radioactive waste, contributing to both environmental preservation and enhanced nuclear energy sustainability. This paper introduces an extension of aggregation operations to hybrid operators in Disc Spherical Fuzzy Sets (D-SFSs), including D-SFS algebraic hybrid weighted averaging and D-SFS hybrid geometric aggregation operators. These novel operators are tailored for D-SFSs and are supported by rigorous proofs, enhancing their theoretical robustness. Additionally, a Maximum Deviation Method (MDM) is developed in conjunction with a proposed distance measure to assess attribute weights in Multiple Attribute Decision-Making (MADM) problems with Disc Spherical Fuzzy (D-SF) information. The study proposes a new MADM method in the D-SF environment, leveraging the D-SF distance measure and the Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) method. Notably, the paper focuses on extending D-SFs to the CRADIS method, particularly when weight information is entirely unknown. A comprehensive case study on optimizing radioactive waste volume reduction through transmutation technologies is provided. A comparative analysis with existing decision-making methods validates the improved MADM method’s validity and practicality.
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