TOPSIS Method Based on Entropy Weight for Interval-Valued Neutrosophic Numbers and Its Application in Multi-Attribute Group Decision-Making

Given the inherent ambiguity in human thinking and judgment, as well as the complexities of objective realities and decision-making contexts, accurately expressing decision-making information with precise numerical values is challenging. To address the uncertainties, incompleteness, and inconsistencies in multi-attribute decision-making, along with the issue of unknown attribute weights, this study introduces the entropy weight-based interval-valued neutrosophic set method. By employing this method, an extended TOPSIS approach is proposed, which integrates the TOPSIS method with intervalvalued neutrosophic set theory. Through the TOPSIS method, candidate solutions are assessed based on their proximity to both the ideal solution and the negative ideal solution. Ultimately, the effectiveness and applicability of this methodology are demonstrated through a case study involving the selection of an e-commerce platform, validating its utility in real-world decision-making scenarios.