A DRL based approach for adaptive scheduling of one-of-a-kind production

•Reinforcement learning has strong competitiveness in static testing.•Reinforcement learning can balance scheduling results and computing time.•Reinforcement learning are more advantageous under higher uncertainty.•Reinforcement learning is suitable for adaptive scheduling and control of OKP.