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Distributed Real-Time Scheduling by Using Multi-agent Reinforcement Learning

  • Koji Iwamura
  • Nobuhiro Sugimura
Chapter

Abstract

Autonomous Distributed Manufacturing Systems (ADMS) have been proposed to realise flexible control structures of manufacturing systems. In the previous researches, a real-time scheduling method based on utility values has been proposed and applied to the ADMS. Multi-agent reinforcement learning is newly proposed and implemented to the job agents and resource agents, in order to improve their coordination processes. The status, the action and the reward are defined for the individual job agents and the resource agents to evaluate the suitable utility values based on the status of the ADMS. Some case studies of the real-time scheduling have been carried out to verify the effectiveness of the proposed methods.

Keywords

Objective Function Schedule Problem Reinforcement Learning Machine Operation Machine Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Graduate School of EngineeringOsaka Prefecture UniversityOsakaJapan

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