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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

A hybrid VIKOR model for kidney stone drug prioritization using triangular bipolar neutrosophic Dombi-based aggregation in multi-criteria uncertainty

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pp. 1415–1424Vol. 47Issue 4April 2026DOI: 10.47974/JIOS-2172XML
Received:
01 Nov 2025
Published Online:
04 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2172
Pages:
1415–1424

Abstract

The purpose of this research to present an improved decision support model that develops from the VIKOR method provides new ways to handle uncertainties and imprecisions within the decision-making process of real-world environments by incorporating Triangular Bipolar Neutrosophic Fuzzy Numbers (TBNFNs). By using Dombi triangular norm and triangular conorm operators to combine multiple criteria during the aggregation process, the proposed model allows for more flexibility when performing calculations, leading to more accurate results and providing greater reliability to users. By applying this framework to prioritize medication therapy options for treatment for kidney stone disease (an area where health care professionals’ judgments often contain uncertainties and conflicting opinions), the overall direction of treatment can be based on the systematic ranking of therapeutic options and the structured framework in which clinical choices can be evaluated. Evidence-based reasoning allows for the application of this framework to complex medical decision-making processes, thus supporting the physician in selecting an optimal treatment strategy. The results of this research show that the combination of bipolar neutrosophic modeling with the VIKOR method improve the accuracy of MCDM when compared with traditional techniques (fuzzy logic). This research provides a model that allows for the further customization of the decision-making process and ultimately aids physicians in developing personalized recommendations for their patients, ultimately leading to improved outcomes for patients in clinical practice.

Keywords

Subject Classifications

68R1005C7205C76

References

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