The Flexible Job Shop Scheduling Problem (FJSP) originates from real production lines, while some practical constraints are often ignored or idealized in current FJSP studies, among which the limited buffer problem has a particular impact on production efficiency. To this end, we study an extended problem that is closer to practical scenarios--the Flexible Job Shop Scheduling Problem with Limited Buffers and Material Kitting. In recent years, deep reinforcement learning (DRL) has demonstrated considerable potential in scheduling tasks. However, its capacity for state modeling remains limited when handling complex dependencies and long-term constraints. To address this, we leverage a heterogeneous graph network within the DRL framework to model the global state. By constructing efficient message passing among machines, operations, and buffers, the network focuses on avoiding decisions that may cause frequent pallet changes during long-sequence scheduling, thereby helping improve buffer utilization and overall decision quality. Experimental results on both synthetic and real production line datasets show that the proposed method outperforms traditional heuristics and advanced DRL methods in terms of makespan and pallet changes, and also achieves a good balance between solution quality and computational cost. Furthermore, a supplementary video is provided to showcase a simulation system that effectively visualizes the progression of the production line.

翻译：柔性作业车间调度问题源于实际生产线，而当前FJSP研究常忽略或理想化某些实际约束，其中有限缓冲区问题对生产效率具有特殊影响。为此，我们研究了一个更贴近实际场景的扩展问题——带有限缓冲区与物料配套的柔性作业车间调度问题。近年来，深度强化学习在调度任务中展现出显著潜力，但在处理复杂依赖关系与长期约束时，其状态建模能力仍存在局限。针对这一问题，我们在DRL框架中引入异构图网络对全局状态进行建模。通过构建机器、工序与缓冲区之间的高效消息传递机制，该网络专注于避免在长序列调度中可能导致托盘频繁更换的决策，从而帮助提升缓冲区利用率和整体决策质量。在合成数据集与实际生产线数据集上的实验结果表明，所提方法在完工时间与托盘更换次数方面均优于传统启发式方法与先进DRL方法，同时在求解质量与计算成本之间取得了良好平衡。此外，我们提供了补充视频以展示能有效可视化生产线进程的仿真系统。