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.

本文提出了一种新颖的深度学习框架，该框架可以利用深度特征提取和强化学习进行可扩展决策选择，以解决灵活作业车间调度问题。这种框架使用了自我关注模型和双重自我关注网络来准确地表示和简洁地描述操作和设备之间的关系。实验结果显示，该方法在某些情况下可以比传统的基于优先级分配规则和当前最先进的强化学习方法更好地解决该问题，具有较好的泛化能力。

基于双重注意力网络增强学习的柔性车间调度

Flexible Job Shop Scheduling via Dual Attention Network Based  Reinforcement Learning

Objects are entities we act upon, where the functionality of an object is
determined by how we interact with it. In this work we propose a Dual Attention
Network model which reasons about human-object interactions. The
dual-attentional framework weights the important features for objects and
actions respectively. As a result, the recognition of objects and actions
mutually benefit each other. The proposed model shows competitive
classification performance on the human-object interaction dataset
Something-Something. Besides, it can perform weak spatiotemporal localization
and affordance segmentation, despite being trained only with video-level
labels. The model not only finds when an action is happening and which object
is being manipulated, but also identifies which part of the object is being
interacted with. Project page: https://dual-attention-network.github.io/.

本文提出了一个双重注意力网络模型，该模型可以同时识别人类以及物体之间的动作，实现了物体及动作的同时识别，并在 Something-Something 数据集上展现了非常好的性能。除此之外，此模型还能够进行弱空间时间定位和可供性分割，可以识别物体的不同部分相互之间的交互。