Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system

Yun Geon Kim, Seokgi Lee, Jiyeon Son, Heechul Bae, Byung Do Chung

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)440-450
Number of pages11
JournalJournal of Manufacturing Systems
Volume57
DOIs
StatePublished - Oct 2020

Keywords

  • Distributed decision making
  • Multi-agent system
  • Reinforcement learning
  • Smart manufacturing

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering

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