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arXiv AI By Xiaobin Li, Yanbin Gao, Jianing Long, Wuming Lei

Graph learning-assisted logic-based Benders decomposition for distributionally robust in-station train rescheduling under disruptions

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The paper introduces a rolling‑horizon distributionally robust model for in‑station train rescheduling that updates predictions of resource closure durations as disruptions resolve. It jointly optimizes train routes, sequences, and timings to minimize worst‑case expected completion delay, using a logic‑based Benders decomposition algorithm guided by a graph neural network (GNN‑LBBD). Experiments on 28 five‑track station instances show GNN‑LBBD reduces mean completion delay by 16.63% compared to tabu search and 20.06% compared to variable neighborhood search, and that incorporating disruption‑resolution observations cuts delay by about 21.7%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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