Abstract
The scheduling of multi-line polyolefin production is a complex decision-making process characterized by sequence-dependent changeovers, strict physicochemical constraints, and dynamic market environments. Traditional optimization methods often suffer from high computational costs and a lack of flexibility in online adjustments. To address these challenges, this paper proposes a Deep Reinforcement Learning (DRL) framework for dynamic scheduling tasks. We first construct a high-fidelity simulation environment that meticulously models realistic industrial constraints, including transition materials, shutdowns, and inventory limits. A Soft Actor-Critic (SAC) agent with a tuple-based action space is employed to mitigate the combinatorial explosion associated with multi-line decisions. Furthermore, a dynamic action masking mechanism embedded with domain knowledge is introduced to strictly enforce hard constraints and significantly improve sample efficiency. Case studies based on real-world industrial data demonstrate that the proposed method can autonomously generate valid schedules that satisfy complex production requirements. Comparative experiments further reveal that the action masking mechanism accelerates training convergence, and the DRL agent exhibits superior adaptability to dynamic price fluctuations.