Abstract
Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.
Highlights
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
With the intensification of market competition and the increasing environmental pressure of carbon emission reduction, the market environment faced by the modern steel industry is gradually changing in the direction of diversified market demand, personalized customization, green manufacturing process, and intelligent manufacturing plan
The traditional research on steel production scheduling problems focuses on steel production scheduling modeling and algorithm design
Summary
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China. Research Center of Biologic Manipulator and Intelligent Measurement and Control, Wuhan University of
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