用高效序列建模解决柔性作业车间调度问题，速度快且效果优。
Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
- 采用Mamba模型捕捉工序与机器间的全局依赖关系。
- 在多个基准上求解速度更快，性能超越现有学习方法。
- 适合制造调度、生产优化等领域研究者参考。
柔性作业车间问题（FJSP）是广泛应用于制造与生产调度的经典组合优化问题，旨在将任务分配至不同机器以最小化总完成时间。当前基于学习的方法多依赖局部特征提取模型，难以捕捉跨工序和机器的全局依赖。本文提出一种创新架构，利用具有线性计算复杂度的状态空间模型Mamba，实现针对FJSP的高效序列建模。该模型包含双Mamba编码器，分别提取工序与机器特征，并引入高效的交叉注意力解码器，学习工序与机器间的交互嵌入。实验表明，该方法在多个基准数据集上均实现更快求解速度，且性能优于现有最先进学习方法。
原文摘要 · Abstract (English)
The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assigning jobs to various machines to optimize criteria, such as minimizing total completion time. Current learning-based methods in this domain often rely on localized feature extraction models, limiting their capacity to capture overarching dependencies spanning operations and machines. This paper introduces an innovative architecture that harnesses Mamba, a state-space model with linear computational complexity, to facilitate comprehensive sequence modeling tailored for FJSP. In contrast to prevalent graph-attention-based frameworks that are computationally intensive for FJSP, we show our model is more efficient. Specifically, the proposed model possesses an encoder and a decoder. The encoder incorporates a dual Mamba block to extract operation and machine features separately. Additionally, we introduce an efficient cross-attention decoder to learn interactive embeddings of operations and machines. Our experimental results demonstrate that our method achieves faster solving speed and surpasses the performance of state-of-the-art learning-based methods for FJSP across various benchmarks.
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