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

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.

کلیدواژه ها:

Rough Sets ، Fermatean Neutrosophic Sets ، supply chain ، Machine Learning