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Constraint-Based In-Station Train Dispatching

Authors Andreas Schutt , Matteo Cardellini , Jip J. Dekker , Daniel Harabor , Marco Maratea , Mauro Vallati

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ACM Subject Classification
  • Applied computing → Operations research
  • Computing methodologies → Planning and scheduling
Keywords
  • in-station train dispatching
  • train scheduling
  • railway scheduling
  • constraint programming
  • mixed-integer programming

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Abstract

In-station dispatching is the problem of planning the movements of scheduled trains inside a railway station. Effective solutions for in-station dispatching are important for maximising the utilisation of railway infrastructure and for mitigating the impact of incidents and delays in the broader network.
In this paper, we explore a constraint-based approach to perform in-station train dispatching. Our extensive empirical analysis of multiple modelling, search strategy, and solver choices, performed over synthetically generated, yet realistic, data, shows that our method outperforms the existing planning-based state-of-the-art approach. In addition, we present different optimisation criteria, which can be effortless defined thanks to the constraint-based approach.

Cite As Get BibTex

Andreas Schutt, Matteo Cardellini, Jip J. Dekker, Daniel Harabor, Marco Maratea, and Mauro Vallati. Constraint-Based In-Station Train Dispatching. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 33:1-33:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.CP.2025.33

Author Details

Andreas Schutt
  • Data61, CSIRO, Melbourne, Australia
Matteo Cardellini
  • DIBRIS, University of Genova, Italy
Jip J. Dekker
  • Department of Data Science and Artificial Intelligence, Monash University, Clayton, Australia
  • ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Melbourne, Australia
Daniel Harabor
  • Department of Data Science and Artificial Intelligence, Monash University, Clayton, Australia
Marco Maratea
  • Department of Mathematics and Informatics, University of Calabria, Rende, Italy
Mauro Vallati
  • Department of Computer Science, University of Huddersfield, UK

Supplementary Materials

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