Picture map of Europe with pins indicating European capital cities

Open Access research with a European policy impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

Explore research outputs by the European Policies Research Centre...

Robustness of maintenance decisions : uncertainty modelling and value of information

Zitrou, Athena and Bedford, Tim and Daneshkhah, Ali (2011) Robustness of maintenance decisions : uncertainty modelling and value of information. In: Advances in Safety, Reliability and Risk Management. Balkema, pp. 940-948. ISBN 978-0-415-68379-1

Full text not available in this repository. Request a copy from the Strathclyde author

Abstract

In this paper we show how sensitivity analysis for a maintenance optimisation problem can be undertaken by using the concept of Expected Value of Perfect Information (EVPI). This concept is important in a decision-theoretic context such as the maintenance problem, as it allows us to explore the effect of parameter uncertainty on the cost and the resulting recommendations. To reduce the computational effort required for the calculation of EVPIs, we have used Gaussian Process (GP) emulators to approximate the cost rate model. Results from the analysis allow us to identify the most important parameters in terms of the benefit of ‘learning’ by focussing on the partial Expected Value of Perfect Information for a parameter. Assuming that a parameter can become completely known before a maintenance decision is made, the analysis determines the optimal decision and the expected related cost, for the different values of the parameter. This type of analysis can be used to ensure that both maintenance calculations and resulting recommendations are sufficiently robust.