Reinforcement Learning Optimization of Preventive Maintenance Scheduling Policies
Research develops reinforcement learning agents that learn optimal maintenance scheduling strategies by interacting with simulated bioprocess equipment environments. This contributes new dynamic optimization methods that adapt maintenance policies to evolving equipment degradation and operational demands.