多工序制造系统生产与维护彼此影响，设备状态和性能损失互相耦合。为量化其维护效果与提升维护策略的经济性，考虑加工设备的状态演化，提出了多成本视角下的生产线维护策略。以维护时机和维护等级为决策变量，构建同时包含预防性维护成本、生产组织扰动成本、交付缺口成本以及产品侧与设备侧性能损失成本的综合成本优化模型。针对由维护动作维度高、时间依赖强等特点带来的模型求解难题，以维护决策单元为图节点，提出基于图注意力网络的近端策略优化算法（GAT-PPO）。案例分析表明，所得维护策略能够自动识别高风险设备与关键时机。此外，通过算法对比，所提算法在不同维护决策计划期下的解质量与在线求解时间具有综合优势，体现出良好的工程应用与扩展潜力。

In multi-operation manufacturing systems, production and maintenance interact, and machine condition is coupled with performance loss. To quantify the effects of maintenance and improve the economic performance of maintenance policies, this study considers the state evolution of machines and proposes a production-line maintenance strategy from a multi-cost perspective. Maintenance timing and level are taken as decision variables, and an integrated cost optimization model is built that includes preventive maintenance cost, downtime cost, delivery gap penalty, and performance loss cost on both product and equipment sides. To handle the high-dimensional and time-dependent action space, maintenance decision units are represented as graph nodes, and a graph-attention-based proximal policy optimization algorithm (GAT-PPO) is developed. Case studies show that the obtained policy can automatically identify high-risk machines and critical periods. Comparative experiments further indicate that the proposed algorithm achieves a favorable trade-off between solution quality and online computation time under different maintenance planning horizons, demonstrating strong potential for engineering applications and extension.