Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning
Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning
Shu Luo
2020 · DOI: 10.1016/j.asoc.2020.106208
Applied Soft Computing · 417회 인용
TLDR
This paper addresses the dynamic flexible job shop scheduling problem (DFJSP) under new job insertions aiming at minimizing the total tardiness and confirms both the superiority and generality of DQN compared to each composite rule, other well-known dispatching rules as well as the stand Q-learning-based agent.