Chapter 18 Metamodel-Based Simulation Optimization

Russell R. Barton, Martin Meckesheimer

Research output: Contribution to journalReview article

217 Scopus citations

Abstract

Simulation models allow the user to understand system performance and assist in behavior prediction, to support system diagnostics and design. Iterative optimization methods are often used in conjunction with engineering simulation models to search for designs with desired properties. These optimization methods can be difficult to employ with a discrete-event simulation, due to the stochastic nature of the response(s) and the potentially extensive run times. A metamodel, or model of the simulation model, simplifies the simulation optimization in two ways: the metamodel response is deterministic rather than stochastic, and the run times are generally much shorter than the original simulation. Metamodels based on first- or second-order polynomials generally provide good fit only locally, and so a series of metamodels are fit as the optimization progresses. Other classes of metamodels can provide good global fit; in these cases one can fit a (global) metamodel once, at the start of the optimization, and use it to find a design that will meet the optimality criteria. Both approaches are discussed in this chapter and illustrated with an example.

Original languageEnglish (US)
Pages (from-to)535-574
Number of pages40
JournalHandbooks in Operations Research and Management Science
Volume13
Issue numberC
DOIs
StatePublished - Dec 1 2006

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics
  • Computer Science Applications
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Chapter 18 Metamodel-Based Simulation Optimization'. Together they form a unique fingerprint.

  • Cite this