Optimizing Decision Processes with Dual Neutrosophic Weighted Hamy Operators in Supply Chain Management Environment

Abstract

The central objective of this research is to improve the quality of decision-making in supply chain management by effectively addressing the uncertainties present in Multiple Attribute Decision-Making (MADM) problems through the innovative use of Dual Neutrosophic Fuzzy Sets (DNFS). To achieve this, four distinct new aggregation operators are introduced: Dual Neutrosophic Fuzzy Hamy Mean (DNFHM), Dual Neutrosophic Fuzzy Weighted Hamy Mean (DNFWHM), Dual Neutrosophic Fuzzy Hamy Dual Mean (DNFHDM), and Dual Neutrosophic Fuzzy Weighted Hamy Dual Mean (DNFWHDM). These operators are formally defined using various computational methods and scoring functions. In addition, the authors present an algorithm that enables the ranking of alternatives based on their aggregated values. The methods introduced in the article are tested on a supplier selection problem. The results of this study reveal that the use of the DNFWHDM operator is not only the most reliable in terms of ranking stability and consistency, but also capable of outperforming conventional approaches by successfully reflecting the interdependencies among attributes while simultaneously handling uncertainties in the data. The significance of this research becomes clearer when considering that existing MADM methods often struggle to manage relationships among the objects they address or rarely impose membership constraints sufficiently, alongside methodological inadequacies. This is where the present paper employs the dual neutrosophic set framework combined with Hamy mean operators to go beyond the limitations of existing MADM solutions. As a result, a less resource-demanding yet more robust decision-making process is obtained, which is not only suitable for supply chain management but can also be extended to sectors such as risk assessment and strategic planning, ultimately supporting better decision-making under uncertainty.