Three-Dimensional Uncertainty Modeling in Intelligent Logistics: Fermatean Neutrosophic Rough Tensor Decomposition for Supply Chain Optimization

Kamran, Muhammad; Zhang, Qingyu; Nadeem, Muhammad; Allahviranloo, Tofigh; Tahir, Muhammad

doi:10.22111/ijfs.2025.52675.9305

Document Type : Research Paper

Authors

¹ Research Institute of Business Analytics and SCM, College of Management, Shenzhen University, Shenzhen, China

² Department of Mathematics, Lahore Garrison University, Lahore, Pakistan

³ Research Center of Performance and Productivity Analysis, Istinye University, Istanbul, Türkiye

⁴ Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan, 29050, KPK, Pakistan

https://doi.org/10.22111/ijfs.2025.52675.9305

Abstract

The article is a hybrid union of rough fermatean neutrosophic sets (RFNS) and machine learning (ML) to economise the cost of a supply chain in the context of complicated uncertainty. We develop a two-fold approach: the initial one is a numerical procedure of defuzzifying RFNS parameters into sharp figures and addressing them through a conventional VAM and MODI algorithm; the second one is a machine learning model according to which the components of the RFNS are handled by a two-strand neural network. This plan transforms the unclear transportation problem into a hybrid form. To validate the framework, we compare its performance with classical performances and other fuzzy hybrid techniques. Our machine learning strategy's statistical analysis reveal that it is as optimized as traditional approaches, and it offers a platform for real-time decisions in dynamic environments.

Keywords

Main Subjects

Fuzzy Math

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Three-Dimensional Uncertainty Modeling in Intelligent Logistics: Fermatean Neutrosophic Rough Tensor Decomposition for Supply Chain Optimization

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References

Volume 23, Issue 1January and February 2026Pages 37-73

Volume 23, Issue 1
January and February 2026
Pages 37-73