Claudia Fecarotti
A mathematical programming approach to railway network asset management
Fecarotti, Claudia; Andrews, John
Authors
Professor JOHN ANDREWS john.andrews@nottingham.ac.uk
PROFESSOR OF INFRASTRUCTURE ASSET MANAGEMENT
Abstract
A main challenge in railway asset management is selecting the maintenance strategies to apply to each asset on the network in order to effectively manage the railway infrastructure given that some performance and safety targets have to be met under budget constraints. Due to economic, functional and operational dependencies between different assets and different sections of the network,# optimal solutions at network level not always include the best strategies available for each asset group. This paper presents a modelling approach to support decisions on how to effectively maintain a railway infrastructure system. For each railway asset, asset state models combining degradation and maintenance are used to assess the impact of any maintenance strategy on the future asset performance. The asset state models inform a network-level optimisation model aimed at selecting the best combination of maintenance strategies to manage each section of a given railway network in order to minimise the impact of the assets conditions on service, given budget constraints and performance targets. The optimisation problem is formulated as an integer-programming model. By varying the model parameters, scenario analysis can be performed so that the infrastructure manager is provided with a range of solutions for different combination of budget available and performance targets.
Citation
Fecarotti, C., & Andrews, J. A mathematical programming approach to railway network asset management. Presented at ESREL 2018
Conference Name | ESREL 2018 |
---|---|
End Date | Jun 21, 2018 |
Acceptance Date | Jan 31, 2018 |
Publication Date | Jun 17, 2018 |
Deposit Date | Mar 29, 2018 |
Publicly Available Date | Jun 17, 2018 |
Peer Reviewed | Peer Reviewed |
Public URL | https://nottingham-repository.worktribe.com/output/939044 |
Contract Date | Mar 29, 2018 |
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