Bayesian enhanced strategic decision making for reliability.

Percy, DF ORCID: https://orcid.org/0000-0002-2733-3855 2002, 'Bayesian enhanced strategic decision making for reliability.' , European Journal of Operational Research, 139 (1) , pp. 133-145.

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Abstract

Successful strategies for maintenance and replacement require good decisions. We might wish to determine how often to perform preventive maintenance, or the optimal time to replace a system. Alternatively, our interest might be in selecting a threshold to adopt for action under condition monitoring, or in choosing suitable warranty schemes for our products. Stochastic reliability models involving unknown parameters are often used to answer such questions. In common with other problems in operational research, some applications of maintenance and replacement are notorious for their lack of data. We present a general review and some new ideas for improving decisions by adopting Bayesian methodology to allow for the uncertainty of model parameters. These include recommendations for specifying suitable prior distributions using predictive elicitation and simple methods for Bayesian simulation. Practical demonstrations are given to illustrate the potential benefits of this approach.

Item Type:	Article
Themes:	Subjects / Themes > Q Science > QA Mathematics > QA275 Mathematical Statistics Subjects outside of the University Themes
Schools:	Schools > Salford Business School > Salford Business School Research Centre
Journal or Publication Title:	European Journal of Operational Research
Publisher:	Elsevier
Refereed:	Yes
ISSN:	0377-2217
Depositing User:	H Kenna
Date Deposited:	21 Aug 2007 12:42
Last Modified:	27 Aug 2021 21:59
URI:	https://usir.salford.ac.uk/id/eprint/267

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