用深度强化学习解决制造中的工艺与调度难题
Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning
- 将工艺调度建模为马尔可夫决策过程，用异构图神经网络捕捉多主体关系
- 在大规模实例上显著提升求解效率与方案质量，优于传统方法
- 适合智能制造、工业优化领域研究者与工程师参考
集成工艺规划与调度（IPPS）问题结合了工艺路线规划与车间排程，对实现制造高效率和最大化资源利用率至关重要，是现代制造系统的核心挑战。传统基于混合整数线性规划（MILP）和启发式算法的方法在求解质量与速度之间难以兼顾。本文提出一种新型端到端深度强化学习（DRL）方法，将IPPS建模为马尔可夫决策过程（MDP），并采用异构图神经网络（GNN）捕捉作业、工序与机器间的复杂关系。为优化调度策略，使用近端策略优化（PPO）。实验结果表明，相比传统方法，该方法在大规模IPPS实例中显著提升了求解效率与方案质量，为现代智能制造系统提供更优的调度策略。
原文摘要 · Abstract (English)
The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in manufacturing and maximize resource utilization, which is crucial for modern manufacturing systems. Traditional methods using Mixed Integer Linear Programming (MILP) and heuristic algorithms can not well balance solution quality and speed when solving IPPS. In this paper, we propose a novel end-to-end Deep Reinforcement Learning (DRL) method. We model the IPPS problem as a Markov Decision Process (MDP) and employ a Heterogeneous Graph Neural Network (GNN) to capture the complex relationships among operations, machines, and jobs. To optimize the scheduling strategy, we use Proximal Policy Optimization (PPO). Experimental results show that, compared to traditional methods, our approach significantly improves solution efficiency and quality in large-scale IPPS instances, providing superior scheduling strategies for modern intelligent manufacturing systems.
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