Simulation optimization using metamodels

Research output: Chapter in Book/Report/Conference proceedingConference contribution

76 Scopus citations


Many iterative optimization methods are designed to be used in conjunction with deterministic objective functions. These optimization methods can be difficult to apply to an objective generated by a discrete-event simulation, due to the stochastic nature of the response(s) and the potentially extensive run times. A metamodel aids simulation optimization by providing a deterministic objective with run times that are generally much shorter than the original discrete-event simulation. Polynomial metamodels generally provide only local approximations, and so a series of metamodels must be fit as the optimization progresses. Other classes of metamodels can provide global fit; fitting can be done either by constructing the global model once at the start of the optimization, or by using the optimization results to identify additional discrete-event runs to refine the global model. This tutorial surveys both local and global metamodel-based optimization methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 2009 Winter Simulation Conference, WSC 2009
Number of pages9
StatePublished - 2009
Event2009 Winter Simulation Conference, WSC 2009 - Austin, TX, United States
Duration: Dec 13 2009Dec 16 2009

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2009 Winter Simulation Conference, WSC 2009
Country/TerritoryUnited States
CityAustin, TX

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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