Percy, DF 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
Uncontrolled Keywords:	Reliability; Bayesian analysis; Predictive elicitation; Simulation
Themes:	Subjects / Themes > Q Science > QA Mathematics > QA275 Mathematical Statistics Subjects outside of the University Themes
Schools:	Colleges and Schools > College of Business & Law > Salford Business School > Management Science and Statistics
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 13:42
Last Modified:	13 Mar 2013 12:17
URI:	http://usir.salford.ac.uk/id/eprint/267

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