REA Press null REA Press3042-13303042-1330 REA Press https://doi.org/10.48313/uda.v2i1.68 Research Article Neutrosophic Z-number sets, AHP method, Neutrosophic sets, Z-numbers, Expert consensus, Uncertainty Neutrosophic Z-number analytic hierarchy process: A framework for enhanced group decision-making under uncertaintyNeutrosophic Z-number analytic hierarchy process: A framework for enhanced group decision-making under uncertainty Nguyen Phi-Hung Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam. Thi Nguyen Lan-Anh Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam. Vu Tra-Giang Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam. Phan Minh-Thang Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam. Ngo Van-Hien Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam. Do Dieu-Linh Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam. 03 2025 22 03 2025 2 1 © 2025 REA Press 2025

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 Neutrosophic Z-number analytic hierarchy process: A framework for enhanced group decision-making under uncertainty

Multi-Criteria Decision-Making (MCDM) often faces challenges in handling uncertainty, imprecision, and unreliable expert judgments, limiting the effectiveness of traditional methods like the Analytic Hierarchy Process (AHP). This study proposes the Neutrosophic Z-Number AHP (NZN-AHP) method to enhance decision-making by addressing these complexities. The NZN-AHP method integrates Neutrosophic Z-Numbers (NZNs), which model truth, indeterminacy, falsity, and reliability, with AHP’s structured pairwise comparison framework. Linguistic scales and advanced aggregation operators, such as Dombi and Aczel–Alsina, are employed to process expert evaluations, ensuring robust handling of uncertain data. The NZN-AHP method achieves consistent outcomes (CR < 0.1), outperforming traditional and fuzzy AHP by incorporating reliability and indeterminacy, thus providing more accurate prioritization of criteria in complex decision-making scenarios. NZN-AHP offers a versatile and precise framework for MCDM, effectively capturing multifaceted uncertainties and enhancing decision-making across domains like logistics, finance, and strategic planning. It sets a foundation for future research into integrating NZNs with other MCDM methods, advancing the field of decision sciences.

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