Guest Editors
Assist. Prof. Bin Hu
Email: bhu@kean.edu
Affiliation: Department of Computer Science and Technology, Kean University, Union, 07083, United States
Homepage:
Research Interests: mobile sensing and computing; cybersecurity and privacy; efficient deep learning; Sustainable Manufacturing
Prof. Jiacun Wang
Email: jwang@monmouth.edu
Affiliation: Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, 07764, United States
Homepage:
Research Interests: machine learning; software engineering; discrete event systems; formal methods; wireless networking; real-time distributed systems
Assist. Prof. Xiwang Guo
Email: x.w.guo@163.com
Affiliation: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, 07102, United States; Information and Control Engineering College, Liaoning Petrochemical University, Fushun 113001, China
Homepage:
Research Interests: deep reinforcement learning; intelligent optimization algorithms; autonomous vehicles; detection model; artificial intelligence; intelligent manufacturing
Summary
The rapid advancement of artificial intelligence (AI) and information technologies is transforming modern manufacturing and remanufacturing systems into intelligent, adaptive, and sustainable operations. As industries face increasingly dynamic markets and stringent environmental regulations, efficiency, safety, and sustainability have become core objectives of the next-generation smart factory. Integrating AI-driven approaches into production and disassembly systems enables data-informed decision-making, predictive optimization, and human–robot collaboration, thus enhancing productivity while minimizing energy consumption and environmental impact.
This special issue focuses on the application of AI techniques—including machine learning, deep learning, reinforcement learning, evolutionary computation, and heuristic algorithms—to address key challenges in intelligent manufacturing and remanufacturing. Topics include AI-based scheduling and optimization, real-time process control, digital twin-enabled predictive maintenance, and smart sensing for quality and energy management. By leveraging intelligent algorithms and cyber-physical integration, these approaches aim to create resilient and autonomous manufacturing ecosystems that drive sustainable industrial transformation.
This issue provides an international forum for researchers and practitioners to share the latest theoretical developments, computational methodologies, and practical applications of AI in manufacturing. The topics of interest include, but are not limited to:
· AI-driven design, control, and optimization of assembly and disassembly systems
· Machine learning and reinforcement learning in intelligent manufacturing
· Digital twins and predictive analytics for smart factories
· Real-time scheduling, task allocation, and operation management
· Smart sensing, monitoring, and adaptive control
· Energy-efficient and sustainable production and recycling
· Heuristic and evolutionary optimization methods for complex manufacturing problems
· Human–robot collaboration and autonomous systems in production lines
· System simulation, fault diagnosis, and performance evaluation
Keywords
Artificial Intelligence, Sustainable Manufacturing, Sustainable Remanufacturing, Smart Factory, Digital Twin, Energy Efficiency, Assembly and Disassembly Optimization, Machine Learning, Real-Time Scheduling, Smart Sensing and Control
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