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Multi-agent System and Reinforcement Learning Approach for Distributed Intelligence in a Flexible Smart Manufacturing System
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- Authors
- Yun Geon Kim, Seokgi Lee, Jiyeon Son, Heechul Bae, Byung Do Chung
- Issue Date
- 2020-10
- Citation
- Journal of Manufacturing Systems, v.57, pp.440-450
- ISSN
- 0278-6125
- Publisher
- Society of Manufacturing Engineers (SME)
- Language
- English
- Type
- Journal Article
- Abstract
- Personalized production has emerged as a result of the increasing customer demand for more personalized products. Personalized production systems carry a greater amount of uncertainty and variability when compared with traditional manufacturing systems. In this paper, we present a smart manufacturing system using a multi-agent system and reinforcement learning, which is characterized by machines with intelligent agents to enable a system to have autonomy of decision making, sociability to interact with other systems, and intelligence to learn dynamically changing environments. In the proposed system, machines with intelligent agents evaluate the priorities of jobs and distribute them through negotiation. In addition, we propose methods for machines with intelligent agents to learn to make better decisions. The performance of the proposed system and the dispatching rule is demonstrated by comparing the results of the scheduling problem with early completion, productivity, and delay. The obtained results show that the manufacturing system with distributed artificial intelligence is competitive in a dynamic environment.
- KSP Keywords
- Changing environment, Customer demand, Dispatching rule, Distributed Intelligence, Distributed artificial intelligence, Dynamic Environment, Early completion, Learning approach, Multi-agent system(MAS), Production system, Reinforcement learning(RL)