TY - JOUR
T1 - Chapter 18 Metamodel-Based Simulation Optimization
AU - Barton, Russell R.
AU - Meckesheimer, Martin
N1 - Funding Information:
This work was partially supported by NSF Grant No. DMI-0084918. The authors appreciate the suggestions made by the editors, Barry Nelson and Shane Henderson, and by Averill Law. The authors also acknowledge helpful discussions with Andrew J. Booker and Stephen P. Jones from Applied Statistics, Phantom Works, the Boeing Company.
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
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U2 - 10.1016/S0927-0507(06)13018-2
DO - 10.1016/S0927-0507(06)13018-2
M3 - Review article
AN - SCOPUS:77950468650
SN - 0927-0507
VL - 13
SP - 535
EP - 574
JO - Handbooks in Operations Research and Management Science
JF - Handbooks in Operations Research and Management Science
IS - C
ER -