Guest Editors
Prof. Jiacun Wang, Monmouth University, USA
Dr. Xiwang Guo, Liaoning Shihua University, China
Summary
Artificial intelligence (AI) is making great progress in both theoretical and practical aspects. Many AI methods, such as deep learning, machine learning, evolutionary computation, heuristic algorithms, cloud computing, internet of things and big data analysis, are having a growing impact on various domains of the real world. Applying AI technology to specific industrial manufacturing/remanufacturing processes to deal with its intelligent production/disassembly scheduling and optimization issues is a major need of industry and a trendy research domain of academia. The design, optimization, and improvement of the collaborations between workers and machines are essential for making these lines as efficient and sustainable as possible. AI can assist us in handling the complexity of these problems and finding and applying solutions that enhance efficiency and lower the environmental impact of production. Related research can boost operation efficiency, guarantee safety and stability, and reduce energy consumption and production and disassembly costs, which can further elevate the level of intelligent manufacturing and remanufacturing. The special issue seeks to gather the latest development results in the area. Topics to be covered in this special issue include, but are not limited to, the following:
• Design, control and optimization of assembly systems
• Design, control and optimization of disassembly systems
• Digital twin techniques in manufacturing
• Emission control and energy saving in manufacturing
• End-of-life product recycling
• Formal methods in the modeling, verification and analysis of manufacturing systems
• Heuristic search algorithms
• Intelligent factory
• Real-time operation management
• Real-time task allocation
• Real-time task scheduling
• Machine learning and reinforcement learning in manufacturing
• Smart sensing and control
• Smart logistics management
• System simulation and performance evaluation
• Sustainability manufacturing
• Workstation load balancing in manufacturing, assembly and disassembly
Keywords
Artificial intelligence, Manufacturing, Disassembly, Optimization, Efficiency, Sustanability, Deep learning, Reinforcement learning
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