A Deep Reinforcement Learning Approach for Smart Coordination Between Production Planning and Scheduling

The hierarchical approach of the production planning and control system proposes to divide decisions into various levels. Some data used in the planning level are based on predictions that anticipate the behavior of the workshop; nevertheless, these predictions can be adjusted at the schedule level. Feedback between both levels would allow better coordination; however, this feedback is not implemented due to interoperability problems and the complexity of the problem. This paper proposes an agent-based system that implements deep reinforcement learning to generate solutions based on artificial intelligence.