用机器学习设计调度算法，更快更准地处理任务延迟问题。
Minimizing the Weighted Number of Tardy Jobs: Data-Driven Heuristic for Single-Machine Scheduling
- 结合机器学习与任务特征，生成可保证可行的调度策略。
- 在多个数据场景下，最优解数量和误差率均优于现有方法。
- 系统对比模型性能，适合工业调度等实际应用。
单机调度问题中，现有研究多依赖精确算法，但在某些问题区域表现下降。本文针对每项任务包含权重、加工时间、截止日期和期限的情况，旨在最小化延误任务的总权重。提出一种新型数据驱动调度启发式算法，融合机器学习与问题特性的优势，确保解的可行性——这是基于机器学习算法的常见难题。实验表明，该方法在最优性差距、最优解数量及跨数据场景适应性方面显著优于当前最佳方法，展现出良好的实用性。此外，系统分析了多种机器学习模型，填补了同类研究中模型选择过程不透明的空白，提供了为何选用特定模型的深入洞察。
原文摘要 · Abstract (English)
Existing research on single-machine scheduling is largely focused on exact algorithms, which perform well on typical instances but can significantly deteriorate on certain regions of the problem space. In contrast, data-driven approaches provide strong and scalable performance when tailored to the structure of specific datasets. Leveraging this idea, we focus on a single-machine scheduling problem where each job is defined by its weight, duration, due date, and deadline, aiming to minimize the total weight of tardy jobs. We introduce a novel data-driven scheduling heuristic that combines machine learning with problem-specific characteristics, ensuring feasible solutions, which is a common challenge for ML-based algorithms. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art in terms of optimality gap, number of optimal solutions, and adaptability across varied data scenarios, highlighting its flexibility for practical applications. In addition, we conduct a systematic exploration of ML models, addressing a common gap in similar studies by offering a detailed model selection process and providing insights into why the chosen model is the best fit.
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