Enhanced Algorithms for Reliable Decision-Making under Imprecision in the Neutrosophic Soft Set Framework

This paper presents new decision-making techniques designed to manage complex problems more effectively under uncertainty. Working within the framework of neutrosophic soft sets, the study introduces two algorithms---the Column Difference Comparison Matrix (CDCM) algorithm and the Row Difference Comparison Matrix (RDCM) algorithm---developed to address the challenges posed by indeterminate, inconsistent, and conflicting information. These algorithms provide a systematic way to reduce uncertainty, enhance precision, and ensure greater stability when evaluating alternatives in multi-criteria decision-making (MCDM) environments. A detailed numerical example illustrates the implementation steps and demonstrates how the proposed methods deliver clearer and more reliable outcomes compared with traditional approaches. A comparative analysis is also conducted against techniques based on q-rung orthopair fuzzy soft sets, T-spherical fuzzy soft sets, and neutrosophic soft sets. The results show that the new algorithms offer improved robustness, scalability, and decision quality in situations involving ambiguous or imprecise data.