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Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation

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  • Chen, Xi
  • Zhou, Qiang

Abstract

Stochastic kriging (SK) methodology has been known as an effective metamodeling tool for approximating a mean response surface implied by a stochastic simulation. In this paper we provide some theoretical results on the predictive performance of SK, in light of which novel integrated mean squared error-based sequential design strategies are proposed to apply SK for mean response surface metamodeling with a fixed simulation budget. Through numerical examples of different features, we show that SK with the proposed strategies applied holds great promise for achieving high predictive accuracy by striking a good balance between exploration and exploitation.

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  • Chen, Xi & Zhou, Qiang, 2017. "Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation," European Journal of Operational Research, Elsevier, vol. 262(2), pages 575-585.
    • Handle: RePEc:eee:ejores:v:262:y:2017:i:2:p:575-585
    DOI: 10.1016/j.ejor.2017.03.042
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    References listed on IDEAS

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    2. Zilong Wang & Marianthi Ierapetritou, 2018. "Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise," Journal of Global Optimization, Springer, vol. 71(4), pages 957-985, August.

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