  Flexible manufacturing has given rise to complex scheduling problems such as
the flexible job shop scheduling problem (FJSP). In FJSP, operations can be
processed on multiple machines, leading to intricate relationships between
operations and machines. Recent works have employed deep reinforcement learning
(DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the
quality of solutions still has room for improvement relative to that by the
exact methods such as OR-Tools. To address this issue, this paper presents a
novel end-to-end learning framework that weds the merits of self-attention
models for deep feature extraction and DRL for scalable decision-making. The
complex relationships between operations and machines are represented precisely
and concisely, for which a dual-attention network (DAN) comprising several
interconnected operation message attention blocks and machine message attention
blocks is proposed. The DAN exploits the complicated relationships to construct
production-adaptive operation and machine features to support high-quality
decisionmaking. Experimental results using synthetic data as well as public
benchmarks corroborate that the proposed approach outperforms both traditional
PDRs and the state-of-the-art DRL method. Moreover, it achieves results
comparable to exact methods in certain cases and demonstrates favorable
generalization ability to large-scale and real-world unseen FJSP tasks.
