In smart manufacturing systems (SMSs), flexible job-shop scheduling with
transportation constraints (FJSPT) is essential to optimize solutions for
maximizing productivity, considering production flexibility based on automated
guided vehicles (AGVs). Recent developments in deep reinforcement learning
(DRL)-based methods for FJSPT have encountered a scale generalization
challenge. These methods underperform when applied to environment at scales
different from their training set, resulting in low-quality solutions. To
address this, we introduce a novel graph-based DRL method, named the
Heterogeneous Graph Scheduler (HGS). Our method leverages locally extracted
relational knowledge among operations, machines, and vehicle nodes for
scheduling, with a graph-structured decision-making framework that reduces
encoding complexity and enhances scale generalization. Our performance
evaluation, conducted with benchmark datasets, reveals that the proposed method
outperforms traditional dispatching rules, meta-heuristics, and existing
DRL-based approaches in terms of makespan performance, even on large-scale
instances that have not been experienced during training.

在智能制造系统中，基于自动导引车的生产灵活性的柔性作业车间调度是优化生产效率的关键，该研究通过引入名为 Heterogeneous Graph Scheduler (HGS) 的新型图结构的深度强化学习方法，可以提高规模泛化性能，超越了传统调度规则、元启发式算法和现有 DRL 方法在制造调度中的性能表现。

基于学习的灵活车间调度算法在可扩展智能制造中的应用

Learning-enabled Flexible Job-shop Scheduling for Scalable Smart  Manufacturing

Smart manufacturing systems are being deployed at a growing rate because of
their ability to interpret a wide variety of sensed information and act on the
knowledge gleaned from system observations. In many cases, the principal goal
of the smart manufacturing system is to rapidly detect (or anticipate) failures
to reduce operational cost and eliminate downtime. This often boils down to
detecting anomalies within the sensor date acquired from the system. The smart
manufacturing application domain poses certain salient technical challenges. In
particular, there are often multiple types of sensors with varying capabilities
and costs. The sensor data characteristics change with the operating point of
the environment or machines, such as, the RPM of the motor. The anomaly
detection process therefore has to be calibrated near an operating point. In
this paper, we analyze four datasets from sensors deployed from manufacturing
testbeds. We evaluate the performance of several traditional and ML-based
forecasting models for predicting the time series of sensor data. Then,
considering the sparse data from one kind of sensor, we perform transfer
learning from a high data rate sensor to perform defect type classification.
Taken together, we show that predictive failure classification can be achieved,
thus paving the way for predictive maintenance.

本研究分析了制造测试台的四个传感器数据集，评估了若干传统和基于机器学习的时间序列预测模型的性能，并通过从高数据速率传感器中执行转移学习来对缺乏数据的传感器进行缺陷类型分类，因此为实现预测维护铺平了道路。