Optimisation of Matrix Production System Reconfiguration with Reinforcement Learning

Matrix production systems (MPSs) offer significant advantages in flexibility and scalability when compared to conventional line-based production systems. However, they also pose major challenges when it comes to finding optimal decision policies for production planning and control, which is crucial to ensure that flexibility does not come at the cost of productivity. While standard planning methods such as decision rules or metaheuristics suffer from low solution quality and long computation times as problem complexity increases, search methods such as Monte Carlo Tree Search (MCTS) with Reinforcement Learning (RL) have proven powerful in optimising otherwise inhibitively complex problems. Despite its success, open questions remain as to when RL can be beneficial for industrial-scale problems. In this paper, we consider the application of MCTS with RL for optimising the reconfiguration of an MPS. We define two operational scenarios and evaluate the potential of RL in each. Taken more generally, our results provide context to better understand when RL can be beneficial in industrial-scale use cases.