Session | ||
Regular Session : AI-based approaches for quality and performance improvement of production systems
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Presentations | ||
ID: 233
/ RS-07: 1
Regular Paper Submission Data-driven Production Management: Machine Learning and Artificial Intelligence Smart Manufacturing & Industry 4.0: Advanced, Digital and Smart Manufacturing Keywords: Neural network, Quality control, Machine Learning, Manufacturing A Neural Network Model for Quality Prediction in the Automotive Industry The Norwegian University of Science and Technology, Norway ID: 403
/ RS-07: 2
Regular Paper Submission Digital Supply Networks: Artificial Intelligence and Blockchain Technologies in Logistics and DSN Data-driven Production Management: Machine Learning and Artificial Intelligence Smart Manufacturing & Industry 4.0: Advanced, Digital and Smart Manufacturing Keywords: Operations management, Artificial Intelligence, Big Data, process industry. AI and BD in Process Industry: a literature review with an operational perspective 1CNR, Italy; 2IRIS; 3IML Fraunhofer; 4ZLC; 5ciaotech ID: 412
/ RS-07: 3
Regular Paper Submission Digital Supply Networks: Artificial Intelligence and Blockchain Technologies in Logistics and DSN, Intelligent Logistics Networks Management Smart Manufacturing & Industry 4.0: Advanced, Digital and Smart Manufacturing, Cyber-Physical Production Systems and Digital Twins Keywords: JSSP, Online Scheduling, MAPPO, MARL, Reinforcement Learning Implementing an Online Scheduling Approach for Production with Multi Agent Proximal Policy Optimization (MAPPO) Siemens AG, Germany ID: 427
/ RS-07: 4
Regular Paper Submission Data-driven Production Management: Novel Production Planning & Control Approaches Smart Manufacturing & Industry 4.0: Advanced, Digital and Smart Manufacturing, Connected, Smart Factories of the Future Keywords: Operation Twins, Production-Intralogistics Synchronization, Production and Operations Management, Real-Time Visibility and Traceability, Industry 4.0, Smart Manufacturing Operation Twins: Synchronized Production-Intralogistics for Industry 4.0 Manufacturing 1epartment of Industrial and Manufacturing Systems Engineering, HKU-ZIRI Lab for Physical Internet, The University of Hong Kong, Hong Kong S.A.R. (China); 2Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen |