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학술지 Multi-agent System and Reinforcement Learning Approach for Distributed Intelligence in a Flexible Smart Manufacturing System
Cited 24 time in scopus Download 4 time Share share facebook twitter linkedin kakaostory
저자
김윤건, 이석기, 손지연, 배희철, 정병도
발행일
202010
출처
Journal of Manufacturing Systems, v.57, pp.440-450
ISSN
0278-6125
출판사
Society of Manufacturing Engineers (SME)
DOI
https://dx.doi.org/10.1016/j.jmsy.2020.11.004
협약과제
20ZR1100, 자율적으로 연결·제어·진화하는 초연결 지능화 기술 연구, 박준희
초록
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.
키워드
Distributed decision making, Multi-agent system, Reinforcement learning, Smart manufacturing
KSP 제안 키워드
Changing environment, Customer demand, Dispatching rule, Distributed Decision Making, Distributed Intelligence, Distributed artificial intelligence, Dynamic Environment, Early completion, Intelligent agents, Learning approach, Multi-agent system(MAS)