用强化学习优化生产排程，从次优解逐步改进。
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling
- 用Transformer学习工序间关系，生成可交换的工序对。
- 在真实工业数据上优于传统启发式方法，显著提升多目标排程效果。
- 适合需要快速迭代优化的工业调度场景。
将强化学习（RL）与启发式方法结合是解决优化问题的新兴趋势，利用RL从搜索过程中生成的数据中学习。一种有前景的方法是训练一个RL智能体作为改进启发式，从次优解开始，通过施加小规模修改逐步优化。本文将该方法应用于一个真实的多目标生产排程问题。我们的方法采用包含Transformer编码的网络结构，以学习工序之间的关系；随后生成概率矩阵，从中采样工序对并进行交换以改进解。我们在来自合作企业的实际数据上对本方法与其他启发式方法进行了基准测试，结果表明其表现更优。
原文摘要 · Abstract (English)
The integration of Reinforcement Learning (RL) with heuristic methods is an emerging trend for solving optimization problems, which leverages RL's ability to learn from the data generated during the search process. One promising approach is to train an RL agent as an improvement heuristic, starting with a suboptimal solution that is iteratively improved by applying small changes. We apply this approach to a real-world multiobjective production scheduling problem. Our approach utilizes a network architecture that includes Transformer encoding to learn the relationships between jobs. Afterwards, a probability matrix is generated from which pairs of jobs are sampled and then swapped to improve the solution. We benchmarked our approach against other heuristics using real data from our industry partner, demonstrating its superior performance.
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