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

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.