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 SIC for mean response surface metamodeling with a fixed simulation budget. Through numerical examples of different features, we show that SIC with the proposed strategies applied holds great promise for achieving high predictive accuracy by striking a good balance between exploration and exploitation. Published by Elsevier B.V.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/626021 |
| Date | 10 1900 |
| Creators | Chen, Xi, Zhou, Qiang |
| Contributors | Univ Arizona, Dept Syst & Ind Engn |
| Publisher | ELSEVIER SCIENCE BV |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | Published by Elsevier B.V. |
| Relation | http://linkinghub.elsevier.com/retrieve/pii/S0377221717302643 |
Page generated in 0.0023 seconds