Multi-agent reinforcement learning for adaptive scheduling: application to multi-site company

Abstract In recent years, most companies have resorted to multi-site organization in an effort to improve their competitiveness and to adapt to current conditions. In this article, we propose a model for adaptive scheduling in multi-site companies. We adopt a multi-agent approach in which intelligent agents have reactive learning ability. This allows them to make accurate short-term decisions. Our model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. Experimentations on a real case study demonstrate the applicability and the effectiveness of our model concerning both optimality and reactivity.