mlsanityData to 14 Oct 2026

Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study

arXiv 2305.03574 ↗First posted 5 May 2023math.OC cs.LGcited by 0

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Abstract (arXiv)

With the aim to stimulate future research, we describe an exploratory study of a railway rescheduling problem. A widely used approach in practice and state of the art is to decompose these complex problems by geographical scope. Instead, we propose defining a core problem that restricts a rescheduling problem in response to a disturbance to only trains that need to be rescheduled, hence restricting the scope in both time and space. In this context, the difficulty resides in defining a scoper that can predict a subset of train services that will be affected by a given disturbance. We report preliminary results using the Flatland simulation environment that highlights the potential and challenges of this idea. We provide an extensible playground open-source implementation based on the Flatland railway environment and Answer-Set Programming.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 30 references that are in the map. First posted on arXiv 5 May 2023.

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3 of 30 references.

References · 3

  1. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
  2. 2021Learning to Delegate for Large-scale Vehicle Routingcited by 109
  3. 2020Train Scheduling with Hybrid Answer Set Programmingcited by 7

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