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Fecarotti, Claudia (2018) Modelling resilient railway systems. PhD thesis, University of Nottingham.

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Abstract

The complexity of the railway asset management process motivates the need for bespoke tools to enable optimal asset management decisions. To address such a need, a Railway Asset Management Modelling Framework is presented to support a structured and systematic decision making process on asset interventions. The framework describes the structure and requirements of the railway asset management system for delivering a safe and reliable railway, whilst minimising the life-cycle costs. It specifies the models and data needed to predict assets’ and network performance indicators on a whole-life, whole-system basis. Depending on the level of abstraction, the models support decisions at asset/route/network level to ultimately meet service and safety targets for the minimum cost. Two main types of models are described: (i) predictive models to forecast the performance of the system of interest under a variety of circumstances, and (ii) optimisation models that use real and predicted data to achieve optimal decisions on the asset interventions. To the first group belong the asset state models aimed at assessing the assets’ response to a range of maintenance strategies.

To demonstrate the capabilities of such models, a track asset management model is presented. It combines the description of the degradation and intervention processes involved in the maintenance of the overall track geometry. The model is built for a line section to account for dependencies due to opportunistic maintenance and renewals. The technique adopted to develop the model is based on Coloured Petri nets with the Monte Carlo simulation method used for its analysis. The asset state models provide statistics on the asset's behaviour which inform a network-level optimisation model for the selection of the optimal combination of intervention strategies for all assets along a given route. A nonlinear integer model is presented along with the ad hoc solution approaches developed to address the nonlinearities. Relaxation tools offered by the mathematical programming formulation enable the percentage error to be estimated thus giving a measure of the quality of the approximate solutions.

Effective asset management strategies result in higher reliability and availability of the assets. However failures and possessions of the infrastructure cannot be completely avoided, and a capability is needed to tolerate disruptions. Crossovers enable trains to switch track, and thus are essential to provide a flexible and connected network. If their number and distribution on the network is optimised, then they unlock the potential for a fault tolerant network. A nonlinear bi-objective mixed-integer optimisation model is developed to this purpose along with a solution approach. The aim is to find the number and distribution of crossovers for the minimum costs, which also minimises the loss of train flow and enables availability targets to be achieved for each line.

Both optimisation models are applied to analyse a variety of scenarios for different values of the system parameters. The analysis of the results enables an evaluation of the robustness of the solutions towards the system parameters.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Andrews, John Remenyte-Prescott, Rasa
Keywords:	Infrastructure Asset Management, Railway Systems, Resilience, System Performance modelling, Maintenance Optimisation.
Subjects:	T Technology > TF Railroad engineering and operation
Faculties/Schools:	UK Campuses > Faculty of Engineering
Item ID:	55503
Depositing User:	Fecarotti, Claudia
Date Deposited:	06 Nov 2018 16:52
Last Modified:	01 Oct 2021 14:50
URI:	https://eprints.nottingham.ac.uk/id/eprint/55503

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