Int. J. of Industrial and Systems Engineering   »   2008 Vol.3, No.4

 

 

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

 

Author: Nassima Aissani, Bouziane Beldjilali, Damien Trentesaux

 

Addresses:
Department of Computer Science, University of Oran, ES-Senia, El M
naour Wilaya, BP 1524, d
Oran Algerie.
Department of Computer Science, University of Oran, ES-Senia, El M
naour Wilaya, BP 1524, d
Oran Algerie.
Laboratory of Industrial and Human Automation, Mechanics and Computer Science, Department of Production Systems, University of Valenciennes, Le mont Houy, Valenciennes cedex 09, F-59313 France

 

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.

 

DOI: 10.1504/IJISE.2008.017555

 

Int. J. of Industrial and Systems Engineering, 2008 Vol.3, No.4, pp.474 - 497

 

Available online: 17 Mar 2008

 

 

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