Stochastic simulators based optimization by Gaussian process metamodels -- Application to maintenance investments planning issues

TL;DR

This paper introduces a Gaussian process-based metamodeling approach to efficiently optimize maintenance strategies for industrial assets by approximating the NPV distribution from stochastic simulations, reducing the need for extensive simulator runs.

Contribution

It develops a novel quantile function metamodel using Gaussian processes and an adaptive design method (QFEI) to optimize maintenance strategies with fewer simulations.

Findings

Effective approximation of NPV quantiles without extensive simulations

Successful application to maintenance planning for multiple systems

Reduction in computational effort for stochastic optimization

Abstract

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 get, 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 QFEI) is defined by extending in our case the well known EGO algorithm. This allows to obtain an "optimal" solution using a small number of simulator runs.