Optimization by simulation metamodelling methods
Optimization by simulation metamodelling methods
We consider how simulation metamodels can be used to optimize the performance of a system that depends on a number of factors. We focus on the situation where the number of simulation runs that can be made is limited, and where a large number of factors must be included in the metamodel. Bayesian methods are particularly useful in this situation and can handle problems for which classical stochastic optimization can fail.
We describe the basic Bayesian methodology, and then an extension to this that fits a quadratic response surface which, for function minimization, is guaranteed to be positive definite. An example is presented to illustrate the methods proposed in this paper.
Simulation, optimization
485-490
Cheng, Russell C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Currie, Christine S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
2004
Cheng, Russell C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Currie, Christine S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Cheng, Russell C.H. and Currie, Christine S.M.
(2004)
Optimization by simulation metamodelling methods.
2004 Winter Simulation Conference, Washington DC, USA.
05 - 08 Dec 2004.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We consider how simulation metamodels can be used to optimize the performance of a system that depends on a number of factors. We focus on the situation where the number of simulation runs that can be made is limited, and where a large number of factors must be included in the metamodel. Bayesian methods are particularly useful in this situation and can handle problems for which classical stochastic optimization can fail.
We describe the basic Bayesian methodology, and then an extension to this that fits a quadratic response surface which, for function minimization, is guaranteed to be positive definite. An example is presented to illustrate the methods proposed in this paper.
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Published date: 2004
Venue - Dates:
2004 Winter Simulation Conference, Washington DC, USA, 2004-12-05 - 2004-12-08
Keywords:
Simulation, optimization
Organisations:
Operational Research
Identifiers
Local EPrints ID: 29634
URI: http://eprints.soton.ac.uk/id/eprint/29634
PURE UUID: 3856ffa6-7419-4c70-9b84-71493718d64f
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Date deposited: 15 May 2006
Last modified: 09 Jan 2022 03:11
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