SOON: Social Network of Machines to Optimize Task Scheduling in Smart Manufacturing

The aim of the Social Network of machines (SOON) project is to investigate the impact of using of autonomous social agents to optimize manufacturing processes in the framework of Industry 4.0. In this article, we present the multi-agent SOON architecture and the built solutions aiming at optimizing the scheduling of tasks. Two different scheduling approaches are proposed. The first approach is based on an ‘auction’ paradigm where the task assignment is decided according to the capability of a machine agent to bid for a task. The second approach is built on a heterarchical agents network where agents learn the acquisition of cooperative tasks. Both solutions are capable of managing and synchronizing the communication between agents while performing their tasks. To describe each approach, two industrial use cases are illustrated: wire rod mill manufacturing and mechanical part manufacturing. Finally, in the heterarchical network, agents are trained with reinforcement learning to maximize the cumulative reward and optimize the manufacturing scheduling. Results show that reinforcement learning allows learning the optimal behavior in multiple scenarios.