Robust Fractional Fuzzy Digital Twin Modeling for Artificial Intelligence assisted Sustainability Optimization in Smart Industrial Systems

eisa abdolmaleki

doi:10.48314/des.vi.124

نویسندگان

عیسی عبدالملکی *

https://doi.org/10.48314/des.vi.124

چکیده

The transition to Industry 5.0 has increased the need for intelligent industrial systems that can operate under uncertainty, dynamic production conditions, and complex supply chain interactions. Conventional industrial models often fail to accurately capture long-term memory behavior, uncertain operating environments, and real-time cyber-physical synchronization. To address these challenges, this paper presents a novel AI-based fractional fuzzy digital twin framework for smart industrial systems and supply chain sustainability. The proposed framework integrates fractional-order dynamics, fuzzy uncertainty modeling, digital twin synchronization, and AI-based predictive control into a unified industrial architecture. Caputo fractional derivatives are employed to describe memory-dependent production dynamics, while fuzzy logic is used to model uncertain demand fluctuations, machinery degradation, and transportation variability. In addition, an AI-assisted predictive control strategy is developed to improve inventory management, predictive maintenance, and energy-efficient operation in smart manufacturing environments. The stability of the proposed system is investigated using fractional Lyapunov theory, and sufficient conditions for strong stability are established. Numerical simulations based on realistic industrial production and inventory scenarios show that the proposed framework outperforms conventional integer-order and fuzzy classical approaches in terms of supply chain resilience, operational stability, energy efficiency, and intelligent resource allocation. The results confirm that the integration of fractional dynamics, fuzzy reasoning, digital twins, and AI-based control provides an effective and scalable framework for next-generation Industry 5.0 applications, including autonomous manufacturing systems, smart logistics, and cyber physical industrial networks.