An optimized asset management petri net model for railway sections

An optimized asset management petri net model for railway sections

Ali Saleh, Darren Prescott, Rasa Remenyte, Manuel Chiachio

Abstract. Railways are important ways of transportation that are used massively. This makes it important to create an optimized asset management model that helps in reducing its Operation and Maintenance (O&M) costs while maintaining the quality of service and safety. Reinforcement learning (RL) is an adequate model for optimizing decisions based on unrelated factors as it connects the decision to a final goal without understanding the problem details. Also, it allows for automatic policy updates without any user intervention. On the other hand, the Petri net (PN) model, which is a bipartite graph of transitions and places, are adequate to be combined with Reinforcement learning since RL actions can be directly described by the PN transitions. In addition, PNs are suitable for maintenance modeling since it can model heterogeneous information, parallel operations, and synchronization, and provide a graphical interpretation. In this study, the Petri net method is used with Reinforcement Learning to create a tool for modeling and optimizing decisions within the maintenance of railway sections while taking into account several factors.

Keywords
Intelligent Decision Making, Petri Net, Reinforcement Learning, Railway

Published online 3/30/2023, 7 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Ali Saleh, Darren Prescott, Rasa Remenyte, Manuel Chiachio, An optimized asset management petri net model for railway sections, Materials Research Proceedings, Vol. 27, pp 285-291, 2023

DOI: https://doi.org/10.21741/9781644902455-37

The article was published as article 37 of the book Structural Health Monitoring

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] O. Economics. “The economic contribution of UK rail.” https://www.oxfordeconomics.com/resource/the-economic-contribution-of-uk-rail/ (accessed.
[2] Office of Rail and Road 2021, “Annual efficiency and finance assessment of Network Rail 2020-21,” 2021.
[3] Steven Clarke, Darren Prescott, and J. Andrews, “Railway track asset management modelling,” University of Nottingham, Nottingham eTheses, 2021. [Online]. Available: http://eprints.nottingham.ac.uk/id/eprint/64797
[4] E. T. Selig and J. M. Waters, Track geotechnology and substructure management. Thomas Telford, 1994. https://doi.org/10.1680/tgasm.20139
[5] B. Aursudkij, “A laboratory study of railway ballast behaviour under traffic loading and tamping maintenance,” University of Nottingham Nottingham, UK, 2007.
[6] M. Chiachío, A. Saleh, S. Naybour, J. Chiachío, and J. Andrews, “Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation,” Reliability Engineering & System Safety, vol. 222, p. 108365, 2022/06/01/ 2022. https://doi.org/10.1016/j.ress.2022.108365
[7] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018.
[8] T. Murata, “Petri nets: Properties, analysis and applications,” Proceedings of the IEEE, vol. 77, no. 4, pp. 541-580, 1989. https://doi.org/10.1109/5.24143
[9] N. Rail, “Track geometry and ballast condition on NR track – parametric analysis for tier 1 and 2 modelling.,” Technical Report 1 2012.
[10] M. Ottomanelli, M. Dell’Orco, and D. Sassanelli, “A Decision Support System Based on Neuro-Fuzzy System for Railroad Maintenance Planning,” in ICEIS (2), 2005, pp. 43-49.