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.
A distributed approach solving partially flexible job-shop scheduling problem with a q- learning effect
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.