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Use of machine learning for continuous improvement of the real time heterarchical manufacturing control system performances

Author

Listed:
  • Nassima Aissani
  • Bouziane Beldjilali
  • Damien Trentesaux

Abstract

Heterarchic manufacturing control system offer a significant potential in terms of capacity, adaptation, self-organisation and real time control for dynamic manufacturing system. In this paper, we present our steps to work out a manufacturing control system where the decisions taken by the system are the result of an agents group work, these agents ensure a continuous improvement of these performance, thanks to the reinforcement learning technique which was introduced to them. This technique of learning makes it possible for the agents to learn the best behaviour in their various roles (answer the requests (risks), self-organisation, plan, etc.) without attenuating the system real time quality. We also introduce a new type of agents called 'observant agent', which has the responsibility to supervise the evolution of the system's total performance. A computer implementation and experimentation of this model are provided in this paper to demonstrate the contribution of our approach.

Suggested Citation

  • Nassima Aissani & Bouziane Beldjilali & Damien Trentesaux, 2008. "Use of machine learning for continuous improvement of the real time heterarchical manufacturing control system performances," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 3(4), pages 474-497.
    • Handle: RePEc:ids:ijisen:v:3:y:2008:i:4:p:474-497
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    Cited by:

    1. Isa Ebtehaj & Keyvan Soltani & Afshin Amiri & Marzban Faramarzi & Chandra A. Madramootoo & Hossein Bonakdari, 2021. "Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning," Sustainability, MDPI, vol. 13(14), pages 1-23, July.

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