用自评估机制提升流水线调度的准确性，避免错误累积。
Self-Evaluation for Job-Shop Scheduling
- 构建政策模型与自评估函数，分步生成并验证任务分配。
- 在经典基准上优于现有最先进方法，显著减少调度误差。
- 适合需要高可靠性的工业调度场景，如制造业排程。
组合优化问题（如调度和路径规划）在多个行业中至关重要，但因其NP难特性而计算复杂。神经组合优化方法利用机器学习应对挑战，但通常依赖序列决策，易因小错误累积导致整体偏差。受大语言模型自评估启发，我们提出一种新框架，通过生成并评估任务分配子集，突破传统逐步方式。针对作业车间调度问题，该方法结合异构图神经网络与Transformer，构建策略模型与自评估函数。在多个知名难点基准上的实验验证了其有效性，性能超越现有最先进方法。
原文摘要 · Abstract (English)
Combinatorial optimization problems, such as scheduling and route planning, are crucial in various industries but are computationally intractable due to their NP-hard nature. Neural Combinatorial Optimization methods leverage machine learning to address these challenges but often depend on sequential decision-making, which is prone to error accumulation as small mistakes propagate throughout the process. Inspired by self-evaluation techniques in Large Language Models, we propose a novel framework that generates and evaluates subsets of assignments, moving beyond traditional stepwise approaches. Applied to the Job-Shop Scheduling Problem, our method integrates a heterogeneous graph neural network with a Transformer to build a policy model and a self-evaluation function. Experimental validation on challenging, well-known benchmarks demonstrates the effectiveness of our approach, surpassing state-of-the-art methods.
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