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Three-Dimensional Uncertainty Modeling in Intelligent Logistics: Fermatean Neutrosophic Rough Tensor Decomposition for Supply Chain Optimization | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 23، شماره 1، فروردین و اردیبهشت 2026، صفحه 37-73 اصل مقاله (6.59 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2025.52675.9305 | ||
| نویسندگان | ||
| Muhammad Kamran* 1؛ Qingyu Zhang1؛ Muhammad Nadeem2؛ Tofigh Allahviranloo3؛ Muhammad Tahir4 | ||
| 1Research Institute of Business Analytics and SCM, College of Management, Shenzhen University, Shenzhen, China | ||
| 2Department of Mathematics, Lahore Garrison University, Lahore, Pakistan | ||
| 3Research Center of Performance and Productivity Analysis, Istinye University, Istanbul, Türkiye | ||
| 4Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan, 29050, KPK, Pakistan | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Rough Sets؛ Fermatean Neutrosophic Sets؛ Supply Chain؛ Machine Learning | ||
| مراجع | ||
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