The Flexible Job Shop Scheduling Problem (FJSSP) has been extensively studied
in the literature, and multiple approaches have been proposed within the
heuristic, exact, and metaheuristic methods. However, the industry's demand to
be able to respond in real-time to disruptive events has generated the
necessity to be able to generate new schedules within a few seconds. Among
these methods, under this constraint, only dispatching rules (DRs) are capable
of generating schedules, even though their quality can be improved. To improve
the results, recent methods have been proposed for modeling the FJSSP as a
Markov Decision Process (MDP) and employing reinforcement learning to create a
policy that generates an optimal solution assigning operations to machines.
Nonetheless, there is still room for improvement, particularly in the larger
FJSSP instances which are common in real-world scenarios. Therefore, the
objective of this paper is to propose a method capable of robustly solving
large instances of the FJSSP. To achieve this, we propose a novel way of
modeling the FJSSP as an MDP using graph neural networks. We also present two
methods to make inference more robust: generating a diverse set of scheduling
policies that can be parallelized and limiting them using DRs. We have tested
our approach on synthetically generated instances and various public benchmarks
and found that our approach outperforms dispatching rules and achieves better
results than three other recent deep reinforcement learning methods on larger
FJSSP instances.

提出了一种使用图神经网络将灵活作业车间调度问题 (FJSSP) 建模为马尔可夫决策过程 (MDP) 的方法，通过生成多样化的调度策略并利用调度规则进行限制，实现对大型 FJSSP 实例的稳健解决，以胜过其他深度强化学习方法和调度规则，提供更好的结果。

利用深度强化学习生成多样的调度策略解决大规模柔性车间调度实例

Solving large flexible job shop scheduling instances by generating a  diverse set of scheduling policies with deep reinforcement learning

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。