###### Abstract  
The workflow scheduling on edge computing platforms in industrial scenarios aims to efficiently utilize the computing resources of edge platforms to meet user service requirements. Compared to ordinary task scheduling, tasks in workflow scheduling come with predecessor and successor constraints. The solutions to scheduling problems typically include traditional heuristic methods and modern deep reinforcement learning approaches. For heuristic methods, an increase in constraints complicates the design of scheduling rules, making it challenging to devise suitable algorithms. Additionally, whenever the environment undergoes updates, it necessitates the redesign of the scheduling algorithms. For existing deep reinforcement learning-based scheduling methods, there are often challenges related to training difficulty and computation time. The addition of constraints makes it challenging for neural networks to make decisions while satisfying those constraints. Furthermore, previous methods mainly relied on RNN and its variants to construct neural network models, lacking a computation time advantage. In response to these issues, this paper introduces a novel workflow scheduling method based on reinforcement learning, which utilizes neural networks for direct decision-making. On the one hand, this approach leverages deep reinforcement learning, eliminating the need for researchers to define complex scheduling rules. On the other hand, it separates the parsing of the workflow and constraint handling from the scheduling decisions, allowing the neural network model to focus on learning how to schedule without the necessity of learning how to handle workflow definitions and constraints among sub-tasks. The method optimizes resource utilization and response time, as its objectives and the network are trained using the PPO algorithm combined with Self-Critic, and the parameter transfer strategy is utilized to find the balance point for multi-objective optimization. Leveraging the advantages of reinforcement learning, the network can be trained and tested using randomly generated datasets. The experimental results indicate that the proposed method can generate different scheduling outcomes to meet various scenario requirements without modifying the neural network. Furthermore, when compared to other deep reinforcement learning methods, the proposed approach demonstrates certain advantages in scheduling performance and computation time. edge computing workflow scheduling; deep reinforcement learning; proximal policy optimization; transformer +
Footnote †: journal: Applied  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00000-0  
industrial scenarios based on edge computing is rapidly growing [2]. These data-intensive storage and computing tasks, carried out at the edge, heavily rely on computational and storage resources, and have stricter requirements for real-time execution of workflows [3]. Typically, the analysis and processing of heterogeneous data from multiple sources in industrial processes involves various steps, including data acquisition, data correlation analysis, AI inference, and industrial equipment feedback control. These steps form a workflow with multiple sub-tasks that need to be executed collaboratively and in real-time [4]. Moreover, due to the concurrent nature of real-time data processing tasks in industrial processes (e.g., device operation condition monitoring and fault diagnosis, product quality inspection, and optimization during production), edge computing platform are required to run multiple sub-tasks concurrently as part of the workflow. The real-time scheduling of these concurrent workflows on resource-constrained edge computing platforms, including computation and storage, poses a critical challenge for current industrial IoT edge computing [3,5]. The issue of concurrency in cloud computing or edge computing can be considered the same type of problem.