Deep Reinforcement Learning With Graph Attention Networks for Workflow Scheduling in Smart Manufacturing

The Fourth Industrial Revolution has driven the formation of smart manufacturing—where production processes are highly automated based on real‐time data. A core component of this model is the Industrial Internet of Things (IIoT), which enables devices, such as robots, CNC machines, sensor systems, AI cameras, conveyor belts, and automated guided vehicles (AGVs) to connect into a unified network. These devices not only collect and transmit data but also coordinate processing, decision‐making, and automatically adjust operations to optimize production performance. In the smart factory model, IIoT systems are deployed to collect and transmit status data from production equipment, such as CNC machines, robots, sensors, conveyor belts, and AGVs. This infrastructure is typically built according to a layered architecture, in which IoT devices and the edge layer are responsible for recording signals and status in real‐time, while the fog and cloud layers support data storage and synchronization on a broader scale. Through this continuous connection mechanism, the system can accurately observe the operational status of machinery and workflow at the shop floor, creating a reliable data foundation for monitoring, analysis, and scheduling tasks in smart factories. Integrating IoT into manufacturing helps factories achieve many important benefits, such as (1) reducing cycle time through automated monitoring and coordination, (2) increasing accuracy and product quality through sensors and AI inspection, (3) reducing energy costs through data‐driven operation optimization, (4) increasing self‐recovery capability when the system can automatically detect and adjust to failures, and (5) optimizing workflow with tight coordination between devices. Workflow scheduling plays a role in intelligent coordination among robots, CNC machines, sensors, AI inspection systems, and automated transportation infrastructure, ensuring that each task is executed on the most suitable type of resource, within the system’s capacity limits, at the most appropriate time while contributing to reducing energy costs. This research aims to develop an intelligent scheduling algorithm based on deep reinforcement learning combined with graph attention networks (GATs) to optimize the completion time of workflows in the smart factory model.