Stochastic Simulators Based Optimization by Gaussian Process Metamodels - Application to Maintenance Investments Planning Issues

This paper deals with the optimization of industrial asset management strategies, whose profitability is characterized by the Net Present Value (NPV) indicator which is assessed by a Monte Carlo simulator. The developed method consists in building a metamodel of this stochastic simulator, allowing t...

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Bibliographic Details
Published in:Quality and reliability engineering international 2016-10, Vol.32 (6), p.2067-2080
Main Authors: Browne, Thomas, Iooss, Bertrand, Gratiet, Loïc Le, Lonchampt, Jérôme, Remy, Emmanuel
Format: Article
Language:English
Subjects:
adaptive design
asset management
computer experiments
Gaussian
Gaussian process
Maintenance
Mathematical models
Metamodels
Optimization
Quantiles
Simulators
stochastic simulator
Stochasticity
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Summary:This paper deals with the optimization of industrial asset management strategies, whose profitability is characterized by the Net Present Value (NPV) indicator which is assessed by a Monte Carlo simulator. The developed method consists in building a metamodel of this stochastic simulator, allowing to obtain, for a given model input, the NPV probability distribution without running the simulator. The present work is concentrated on the emulation of the quantile function of the stochastic simulator by interpolating well chosen basis functions and metamodeling their coefficients (using the Gaussian process metamodel). This quantile function metamodel is then used to treat a problem of strategy maintenance optimization (four systems installed on different plants), in order to optimize an NPV quantile. Using the Gaussian process framework, an adaptive design method (called quantile function expected improvement) is defined by extending in our case the well‐known efficient global optimization algorithm. This allows to obtain an “optimal” solution using a small number of simulator runs. Copyright © 2016 John Wiley & Sons, Ltd.
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.2028