Abstract
We consider the problem of pattern detection in large scale railway timetables. This problem arises in rolling stock optimization planning in order to identify invariant sections of the timetable for which a cyclic rotation plan is adequate. We propose a dual reduction technique which leads to an decomposition and enumeration method. Computational results for real world instances demonstrate that the method is able to produce optimal solutions as fast as standard MIP solvers.
The work for this article has been conducted within the Research Campus Modal funded by the German Federal Ministry of Education and Research (fund number 05M14ZAM).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Gap to optimality: \(\vert \text {primalbound} - \text {dualbound} / \min \{\vert \text {primalbound}\vert ,\vert \text {dualbound}\vert \}\vert \) if both bounds have same sign, or infinity, if they have opposite sign.
References
Borndörfer, R., Reuther, M., Schlechte, T., Waas, K., & Weider, S. (2016). Integrated optimization of rolling stock rotations for intercity railways. Transportation Science, 50(3), 863–877.
Maher, S., Miltenberger, M., Pedroso, J. P., Rehfeldt, D., Schwarz, R., Serrano, F. (2016). PySCIPOpt: Mathematical programming in python with the SCIP optimization suite. In: Mathematical Software ICMS 2016 (Vol. 9725, pp. 301–307)
Maher, S.J., Fischer, T., Gally, T., Gamrath, G., Gleixner, A., Gottwald, R.L., & et al. (2017). The SCIP Optimization Suite 4.0. Tech. Rep. 17–12, ZIB, Takustr. 7, 14195 Berlin
Schade, S., Borndörfer, R., Breuer, M., Grimm, B., Reuther, M., Schlechte, T., et al. (2017). Pattern detection for large-scale railway timetables. In Proceedings of the IAROR conference RailLille
Acknowledgements
The work for this article has been conducted within the Research Campus Modal funded by the German Federal Ministry of Education and Research (fund number 05M14ZAM).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Schade, S., Schlechte, T., Witzig, J. (2018). Structure-Based Decomposition for Pattern-Detection for Railway Timetables. In: Kliewer, N., Ehmke, J., Borndörfer, R. (eds) Operations Research Proceedings 2017. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-89920-6_95
Download citation
DOI: https://doi.org/10.1007/978-3-319-89920-6_95
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-89919-0
Online ISBN: 978-3-319-89920-6
eBook Packages: Business and ManagementBusiness and Management (R0)