Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning

  • Authors:

  • N. Aissani;A. Bekrar;D. Trentesaux;B. Beldjilali

  • Affiliations:

  • LIO, Department of Computer Sciences, University of Oran, Oran, Algeria;University Lille Nord de France, Lille, France 59000 and UVHC, TEMPO Lab., Valenciennes, France 59313;University Lille Nord de France, Lille, France 59000 and UVHC, TEMPO Lab., Valenciennes, France 59313;LIO, Department of Computer Sciences, University of Oran, Oran, Algeria

  • Venue:
  • Journal of Intelligent Manufacturing
  • Year:
  • 2012

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Abstract

In recent years, most companies have resorted to multi-site or supply-chain organization in order to improve their competitiveness and adapt to existing real conditions. In this article, a model for adaptive scheduling in multi-site companies is proposed. To do this, a multi-agent approach is adopted in which intelligent agents have reactive learning capabilities based on reinforcement learning. This reactive learning technique allows the agents to make accurate short-term decisions and to adapt these decisions to environmental fluctuations. The proposed model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. The proposed approach is compared to a genetic algorithm and a mixed integer linear program algorithm to prove its feasibility and especially, its reactivity. Experimentations on a real case study demonstrate the applicability and the effectiveness of the model in terms of both optimality and reactivity.