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

Nassima Aissani, Bouziane Beldjilali and Damien Trentesaux

International Journal of Industrial and Systems Engineering, 2008, vol. 3, issue 4, 474-497

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.

Keywords: manufacturing control; heterarchical manufacturing; total performance; multi-agent systems; MAS; agent-based systems; reinforcement learning; real time control; machine learning; continuous improvement. (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (1)

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