Integration of Machine Learning, Advanced Materials, and Real-Time Scheduling in Smart Manufacturing and Edge AI Systems

Abstract

The convergence of machine learning algorithms, advanced material engineering, and real-time scheduling frameworks represents a critical frontier in modern industrial and technological systems. This paper synthesizes recent advances across three interconnected domains: logistics network optimization through machine learning, performance enhancement of new energy vehicle components via advanced materials, and energy-efficient scheduling for edge AI systems. By examining the synergistic relationships between these areas, we demonstrate that integrated approaches yield superior outcomes compared to isolated optimizations. The analysis reveals that machine learning-based logistics optimization, when combined with advanced material applications and real-time scheduling strategies, can significantly enhance system reliability, energy efficiency, and operational performance across manufacturing and transportation networks. Key findings indicate that knowledge graph-based intelligent response systems and structure-aware deep reinforcement learning approaches provide promising frameworks for achieving robust, scalable solutions in complex industrial environments.