Abstract
This article reviews optimization methods that enhance adaptability, efficiency, and decision-making in modern manufacturing, emphasizing the transformative role of artificial intelligence (AI) and digital twin technologies. By integrating AI and machine learning algorithms within digital twin frameworks, manufacturers can facilitate real-time monitoring, quality control, and dynamic process adjustments.
This synergy boosts operational efficiency and enables precise modeling, offering predictive insights for strategic planning and innovation. The combination of digital twins and optimization techniques supports resource optimization, balances competing objectives, and drives continuous process improvement. With both offline and online optimization approaches, digital twins enable efficient production adjustments while ensuring long-term performance and scalability. Ultimately, the review positions optimization algorithms and digital twins as foundational technologies for smart, sustainable manufacturing and key drivers of future intelligent production systems.
Keywords: Digital Twins, Optimization, Artificial Intelligence, Manufacturing Systems, Machine Learning