基于图学习辅助逻辑 Benders 分解的分布鲁棒车站列车扰后重调度方法
Graph learning-assisted logic-based Benders decomposition for distributionally robust in-station train rescheduling under disruptions
AI 导读
一项 arXiv 研究提出滚动时域分布鲁棒模型 GNN-LBBD，用于资源停运期间的车站列车重调度，联合优化列车路由、顺序与时刻，并利用图神经网络引导资源松弛子问题求解顺序。在五个站线的 28 个测试实例上，GNN-LBBD 平均总完成延误为 61.13 列车分钟，比禁忌搜索低 16.63%、比变邻域搜索低 20.06%；利用扰动静解观测信息可使平均总完成延误降低约 21.7%。
正文 · 原文
Abstract:Uncertainty in resource reopening times complicates train holding and rerouting decisions when resource closures coincide with arrival delays and processing-time extensions. We develop a rolling-horizon distributionally robust model that updates predictions of the remaining closure duration as observations of disruption resolution become available. The model jointly optimizes train routes, sequences, and timings. It minimizes the worst-case expected total completion delay over an ambiguity set of scenario probabilities. The solution algorithm, GNN-LBBD, uses logic-based Benders decomposition to coordinate routing and resource scheduling. Conditional cuts are constructed from resource-use patterns, and a graph neural network guides the order in which resource relaxation subproblems are solved. Across 28 test instances for a five-track station, GNN-LBBD achieved a mean total completion delay of 61.13 train-min. This was 16.63\% lower than tabu search and 20.06\% lower than variable neighborhood search, with a mean total computation time close to that of tabu search. Information comparisons on six representative instances showed that using observations of disruption resolution reduced mean total completion delay by approximately 21.7\%. Scenario-based and conditional-mean predictions produced similar overall results under the same information conditions. The benefits of information were more pronounced in some instances with alternative routes. This suggests a relationship between the use of observations, the station resource structure, and the scope for schedule adjustments.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.07520 [cs.AI] |
| (or arXiv:2611.07520v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07520 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xiaobin Li [view email]
[v1]
Thu, 5 Nov 2026 06:30:36 UTC (884 KB)
来源：arXiv cs.AI · arxiv.org