Dynamic Scheduling of Simulation Workflows in Product Design With Meta-Reinforcement Learning

Product design involves the conceptualization and creation of products, where simulation workflows often require dynamic scheduling. Although deep reinforcement learning has shown competitive performance in addressing dynamic scheduling problems, they encounter challenges when dealing with multiple types of dynamic events in workflow scheduling. To this end, this article introduces a novel meta-reinforcement learning-based dynamic scheduling algorithm (MDSA) to tackle multiple dynamic events. Four types of dynamic events are considered and modeled, including random insertion, loop execution, structural adjustments, and resource uncertainties. The Markov decision process is formulated for the workflow scheduling problem, defining the state, action, reward, and state transition. To capture time-sequential information, a context-aware discretized policy is introduced, and a meta-training algorithm is employed to extract common knowledge across various scheduling scenarios. The effectiveness of the proposed method is demonstrated through a case study in the semiconductor display industry. The simulation environments are constructed using industry-inspired synthetic data, with reference to deployment practices from BOE and task patterns derived from Alibaba cluster traces, to ensure practical relevance while maintaining experimental control. Experimental results indicate that the approach demonstrates strong generalization capabilities, especially in terms of its consistent performance across varying task data distribution and its robustness in adapting to multiple dynamic conditions, thereby enhancing its adaptability in practical applications.