Reinforcement learning and digital twin-based real-time scheduling method in intelligent manufacturing systems

Lixiang Zhang, Yan Yan, Yaoguang Hu, Weibo Ren

Research output: Contribution to journalConference articlepeer-review

15 Citations (Scopus)

Abstract

Optimization efficiency and decision-making responsiveness are two conflicting objectives to be considered in intelligent manufacturing. Therefore, we proposed a reinforcement learning and digital twin-based real-time scheduling method, called twins learning, to satisfy multiple objectives simultaneously. First, the interaction of multiple resources is constructed in a virtual twin, including physics, behaviors, and rules to support the decision-making. Then, the real-time scheduling problems are modeled as Markov Decision Process and reinforcement learning algorithms are developed to learn better scheduling policies. The case study indicates the proposed method has excellent adaptability and learning capacity in intelligent manufacturing.

Original languageEnglish
Pages (from-to)359-364
Number of pages6
JournalIFAC-PapersOnLine
Volume55
Issue number10
DOIs
Publication statusPublished - 2022
Event10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 - Nantes, France
Duration: 22 Jun 202224 Jun 2022

Keywords

  • Real-time scheduling
  • digital twin
  • intelligent manufacturing
  • reinforcement learning

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