arXiv：cs.LG· Taicheng Zheng, Dan Li, Jie Li·· 13 小时前
面向不完整前视信息的多用途批处理过程贝叶斯动态调度
Bayesian dynamic scheduling of multipurpose batch processes under incomplete look-ahead information
AI 导读
研究提出一种贝叶斯动态调度方法，通过从扰动概率分布中学习贝叶斯网络来量化各工序受扰动影响的概率，并在在线执行中随新扰动更新后验分布以指导重调度。在四个基准问题上，该方法的长期成本与系统波动均统计性优于固定间隔从头生成新调度表的周期性重调度策略。作者还证明，当扰动相互独立时，影响量化变量天然满足贝叶斯网络所需独立性假设，便于推广至作业车间调度等场景。
正文
Abstract:Multipurpose batch processes become increasingly popular in manufacturing industries since they adapt to low-volume, high-value products and shifting demands. These processes often operate in a dynamic environment, which faces disturbances such as processing delays and demand changes. To minimise long-term cost and system nervousness (i.e., disruptive changes to schedules), schedulers must design rescheduling strategies to address such disturbances effectively. Existing methods often assume complete look-ahead information over the scheduling horizon. This assumption contrasts with realistic situations where schedulers can only access incomplete look-ahead information. Sticking with existing methods may lead to suboptimal long-term costs and high-level system nervousness. In this work we propose a Bayesian dynamic scheduling method. Our method relies on learning a Bayesian Network from the probability distribution of disturbances. Specifically, the Bayesian Network represents how likely each operation will be impacted by disturbances. During the online execution, when new disturbances become observed, this method updates the posterior distribution and therefore guides the rescheduling strategy. We compare our method with the existing periodic rescheduling strategy (which generates new schedules from scratch at fixed intervals) on four benchmark problems. Computational results show that our method achieves statistically better long-term costs and system nervousness. In the theoretical aspect, we prove that if disturbances are mutually independent, the impact-quantifying variables inherently satisfy the independence assumptions required by Bayesian Networks. As an implication, practitioners can extend the method to other scheduling problems (such as job shop scheduling and continuous processes), given that they define the problem-specific dependencies between operations.
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC); Machine Learning (stat.ML) |
| Cite as: | arXiv:2512.01093 [cs.LG] |
| (or arXiv:2512.01093v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2512.01093 arXiv-issued DOI via DataCite |
Submission history
From: Taicheng Zheng [view email]
[v1]
Sun, 30 Nov 2025 21:27:52 UTC (1,350 KB)
[v2]
Fri, 9 Oct 2026 09:42:06 UTC (2,695 KB)
来源：arXiv：cs.LG · arxiv.org