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 not only boosts operational efficiency but also enables precise modelling, offering predictive insights for strategic planning and innovation. The combination of digital twins and optimization techniques supports resource optimization, balancing competing objectives and driving continuous process improvements. With both offline and online optimization approaches, digital twins enable efficient production adjustments while ensuring long-term performance and scalability. Ultimately, this review highlights digital twins as foundational technologies for smart, sustainable manufacturing, incorporating advanced optimization strategies to enhance adaptability and operational resilience. This positions optimization algorithms and digital twins as key drivers in the future of intelligent production systems.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 161-207 |
| Number of pages | 47 |
| Journal | Engineering Optimization |
| Volume | 57 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2025 |
Funding
This study was supported by funding from the NSF Engineering Research Center for Hybrid Autonomous Manufacturing Moving from Evolution to Revolution (ERC-HAMMER) [grant number EEC-2133630], NSF Future Manufacturing [grant numbers 2037026 and 2328032] and the ReMADE Institute research program [grant number DE-EE0007897]. The authors gratefully acknowledge the support from the NSF Engineering Research Center for Hybrid Autonomous Manufacturing Moving from Evolution to Revolution (ERC-HAMMER), NSF Future Manufacturing and the ReMADE Institute research program. Yi-Ping Chen also appreciates the Taiwan\u2013Northwestern Doctoral Scholarship for supporting his doctoral study.
Keywords
- Digital twins
- artificial intelligence
- manufacturing systems and machine learning
- optimization
ASJC Scopus subject areas
- Computer Science Applications
- Control and Optimization
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
- Applied Mathematics
