用神经网络减少调度优化中的重复计算，提升长周期任务的求解速度与质量。
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
- 用神经网络预测无需重优化的操作，缩小子问题规模
- 在FJSP上提速最高达54%，且解的质量显著优于现有方法
- 适合需要快速响应的在线调度场景，通用性强
长周期组合优化问题（如柔性作业车间调度问题FJSP）涉及长时间跨度内的复杂依赖决策，现有求解器面临挑战。滚动时域优化（RHO）通过将问题分解为重叠的短时域子问题来缓解此问题，但重叠部分常导致冗余计算。本文提出L-RHO，首个面向组合优化问题的学习引导型滚动时域优化框架。L-RHO利用神经网络智能地固定那些事后证明无需重优化的变量，从而生成更小、更易求解的子问题。针对FJSP，这体现为识别连续子问题间机器分配未变化的操作。实验表明，该方法在FJSP上使RHO加速最高达54%，并显著提升解的质量，优于其他启发式及学习基线方法。我们还深入分析了L-RHO在多种FJSP变体、分布、在线场景和基准实例上的适应性与泛化能力，并提供了理论分析，阐明了学习带来收益的条件。
原文摘要 · Abstract (English)
Long-horizon combinatorial optimization problems (COPs), such as the Flexible Job-Shop Scheduling Problem (FJSP), often involve complex, interdependent decisions over extended time frames, posing significant challenges for existing solvers. While Rolling Horizon Optimization (RHO) addresses this by decomposing problems into overlapping shorter-horizon subproblems, such overlap often involves redundant computations. In this paper, we present L-RHO, the first learning-guided RHO framework for COPs. L-RHO employs a neural network to intelligently fix variables that in hindsight did not need to be re-optimized, resulting in smaller and thus easier-to-solve subproblems. For FJSP, this means identifying operations with unchanged machine assignments between consecutive subproblems. Applied to FJSP, L-RHO accelerates RHO by up to 54% while significantly improving solution quality, outperforming other heuristic and learning-based baselines. We also provide in-depth discussions and verify the desirable adaptability and generalization of L-RHO across numerous FJSP variates, distributions, online scenarios and benchmark instances. Moreover, we provide a theoretical analysis to elucidate the conditions under which learning is beneficial.
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