GRWS: A Deep Reinforcement Learning Method With Graph Attention Networks for Flexible Workflow Scheduling in Industrial Manufacturing Scenarios

In 6G-enabled smart manufacturing factories, software systems rapidly customize and deploy workflows through virtualization, modularization, and servitization. This enables flexible and efficient production scheduling. However, uncertainties such as equipment failures, changing task priorities, and dynamic resource demands are significant workflow execution challenges. This paper presents a method based on graph attention networks and deep reinforcement learning for workflow scheduling (GRWS), which is aimed at optimizing the workflow execution time and the associated cost, increasing the efficiency of task scheduling, and supporting flexible production manufacturing. First, topological sorting is applied to determine task dependencies, and tasks are matched with the corresponding containers to construct a container queue. By calculating the sub-deadlines of each container, the execution order of the containers is prioritized to ensure that tasks are completed efficiently within the specified time frame. Second, a reinforcement learning framework combined with a graph attention network is used to optimize aggregation and collaboration between machine nodes. This method minimizes the machine leasing cost while ensuring that the container-to-machine scheduling process meets the appropriate deadlines, thereby increasing the system’s overall efficiency. Third, to address uncertainties such as sudden workflow arrivals and machine failures, a dynamic adjustment strategy is designed to increase the robustness of the system. Finally, experiments show that compared with the existing state-of-the-art algorithms under various conditions, the proposed method reduces the incurred leasing costs by approximately 7.1%, increases the success rate by approximately 2.6%, and reduces the deadline violation rate by approximately 40.1%.