A Reinforcement Learning Approach for Learning Coordination Rules in Production Networks

In production networks companies need fast reactions due to changes of supply and demand. To realize such a change management in an effective way the involved companies have to synchronize their quantities and capacities collaboratively. For these purposes the multiagent system MASCOPP was developed at the Heinz Nixdorf Institute, which tries to eliminate conflicts in a production network, based on changes of plans, through bilateral communication between the involved companies. Human experts have to configure the system by creating coordination rules to solve the conflicts. In this paper we introduce a machine learning concept to learn these coordination rules objectively by a reinforcement learning approach.