Erschienen in:CentER Discussion Paper Series ; No. 2017-026
Umfang:
1 Online-Ressource (21 p)
Sprache:
Englisch
DOI:
10.2139/ssrn.2969730
Identifikator:
Entstehung:
Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments May 16, 2017 erstellt
Beschreibung:
This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression metamodels, or Kriging (Gaussian processes). The metamodel type guides the design of the experiment; this design fixes the input combinations of the simulation model. These regression models uses a sequence of local first-order and second-order polynomials known as response surface methodology (RSM). Kriging models are global, but are re-estimated through sequential designs. "Robust" optimization may use RSM or Kriging, and accounts for uncertainty in simulation inputs