Adaptive scheduling of key processes in production lines based on fuzzy logic and reinforcement learning

To facilitate automation and intelligent scheduling of pivotal production line processes, a novel adaptive scheduling approach is introduced, leveraging fuzzy logic and reinforcement learning. It identifies key influencing factors— equipment status, raw material availability, order demands—as inputs for the fuzzy logic framework. By translating input values into fuzzy set membership degrees, it addresses production uncertainties. Reinforcement learning constructs an adaptive neural network model for scheduling, enacting actions guided by the strategy's generated instructions. Experimental outcomes demonstrate enhanced production efficiency, optimized resource utilization, cost reduction, and significant economic benefits for enterprises.