Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture

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@Article{GU:2024:cie,

• author = "Wenbin Gu and Siqi Liu and Zhenyang Guo and Minghai Yuan and Fengque Pei",
• title = "Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture",
• journal = "Computer \& Industrial Engineering",
• pages = "110155",
• year = "2024",
• ISSN = "0360-8352",
• DOI = "doi:10.1016/j.cie.2024.110155",
• URL = "https://www.sciencedirect.com/science/article/pii/S0360835224002766",
• keywords = "genetic algorithms, genetic programming, Intelligent workshop, Multi-agent manufacturing system, Data-based with combination of virtual and physical agent (DB-VPA), Dynamic scheduling mechanism, IGP-PPO",
• abstract = "With the development and changes of industry and market demand, the personalized customization production mode with small batch and multiple batches has gradually become a new production mode. This makes production environment become more complex and dynamic. However, traditional production workshops cannot effectively adapt to this environment. Combining with new technologies, transforming traditional workshops into intelligent workshop to cope with new production mode become an urgent problem. Therefore, this paper proposes a multi-agent manufacturing system based on IoT for intelligent workshop. Meanwhile, this paper takes flexible job shop scheduling problem (FJSP) as a specific production scenario and establishes relevant mathematics model. To build the agent in intelligent workshop, this paper proposes a data-based with combination of virtual and physical agent (DB-VPA) which has information layer, software layer and physical layer. Then, based on the manufacturing system, this paper designs a dynamic scheduling mechanism with deep reinforcement learning (DRL) for intelligent workshop. This method contains three aspects: (1) Modeling production process based on Markov decision process (MDP). (2) Designing communication mechanism for DB-VPAs. (3) Designing scheduling model combining with improved genetic programming and proximal policy optimization (IGP-PPO) which is a DRL method. Finally, relevant experiments are executed in a prototype experiment platform. The experiments indicate that the proposed method has superiority and generality in solving scheduling problem with dynamic events",

}

Genetic Programming entries for Wenbin Gu Siqi Liu Zhenyang Guo Minghai Yuan Fengque Pei

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