Date: **July 12th 2019, 11am**

Speaker:** Sebastian Rojas Gonzales (KU Leuven)
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Title: **Gaussian processes for simulation optimization
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Room: **Kepler**

**Abstract**

The use of kriging metamodels (also known as gaussian processes) in simulation optimization has become increasingly popular during recent years. The majority of the algorithms so far uses the ordinary (deterministic) kriging approach for constructing the metamodel, assuming that observations have been sampled with infinite precision. This is a major issue when the simulation problem is stochastic: ignoring the noise in the outcomes may lead to inaccurate predictions. In this work, we propose a stochastic multiobjective simulation optimization algorithm that contains two crucial elements: the search phase implements a kriging method that is able to account for the inherent noise in the outputs when constructing the metamodel, and in the identification phase uses a Bayesian multiobjective ranking and selection procedure in view of maximizing the probability of selecting the true non-dominated points by optimally allocating the available computational budget. We evaluate the impact of these elements on the search and identification effectiveness on a set of artificial test problems with varying levels of heteroscedastic noise. Our results show that the characterization of the noise is crucial in improving the prediction efficiency; yet, the allocation procedure appears to lose effectiveness in settings with high noise. This emphasizes the need for further research on multiobjective ranking and selection methods.