A PPO-trained DRL agent selects from established dispatching rules to minimize total job completion time in FJSP with random arrivals, outperforming single rules and performing competitively with arrival-triggered MILP on heterogeneous datasets.
Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach
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Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
A PPO-trained DRL agent selects from established dispatching rules to minimize total job completion time in FJSP with random arrivals, outperforming single rules and performing competitively with arrival-triggered MILP on heterogeneous datasets.