Abstract
Life-cycle cost (LCC) is used as a cost-effective decision support for maintenance of railway track infrastructure. However, a fair degree of uncertainty associated with the estimation of LCC is due to the statistical characteristics of reliability and maintainability parameters. This paper presents a methodology for estimation of uncertainty linked with LCC, by a combination of design of experiment and Monte Carlo simulation. The proposed methodology is illustrated by a case study of Banverket (Swedish National Rail Administration). The paper also includes developed maintenance cost models for track.
Highlights
Life cycle cost (LCC) takes into account all costs associated with the life time of the system, such as operating costs, maintenance costs, energy costs, and taxes apart from capital costs
It was decided to explore a methodology that combines the use of Design of Experiment (DoE) principles with Monte Carlo simulation to estimate the uncertainty involved with LCC
Level II uncertainty can be due to economic parameters, e.g. discounting rate, which has not been explored in this paper
Summary
Life cycle cost (LCC) takes into account all costs associated with the life time of the system, such as operating costs, maintenance costs, energy costs, and taxes apart from capital costs. Level I uncertainty is costs due to penalties imposed by traffic operators on the infrastructure manager due to such factors as train delay, traffic disruption, or derailment. These anomalies can be caused by planned or unplanned maintenance actions, and by lack. There is some research related to the stochastic nature of R&M parameters included in LCC estimation of railway infrastructure, see e.g. No published research about the estimation of the uncertainty in LCC of railway infrastructure has been found.
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