arXiv cs.AI· Xiaobin Li, Yanbin Gao, Jianing Long, Wuming Lei·· 12 小时前AI 评分18
图学习辅助的逻辑型 Benders 分解求解中断下分布鲁棒站内列车重调度
Graph learning-assisted logic-based Benders decomposition for distributionally robust in-station train rescheduling under disruptions
AI 导读
GNN-LBBD 用逻辑型 Benders 分解协调路径与资源调度，并以图神经网络引导资源松弛子问题的求解顺序，在五轨车站 28 个测试实例上平均总完成延误为 61.13 train-min，比禁忌搜索低 16.63%、比变邻域搜索低 20.06%，平均总计算时间接近禁忌搜索。
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