Comparative Study of Metamodeling and Sampling Design for Expensive and Semi-Expensive Simulation Models Under Uncertainty

62 Pages Posted: 17 Jun 2019

See all articles by Amir Parnianifard

Amir Parnianifard

University Putra Malaysia

A Azfanizam

University Putra Malaysia

Mohd Khairol Anuar Mohd Ariffin

University Putra Malaysia

Mohd Idris Shah Ismail

University Putra Malaysia

Date Written: June 5, 2019

Abstract

In spite of the wide improvements in computer simulation packages, many complex simulation models, particularly under uncertainty, may be inefficient to run in terms of time, computation, and resources. To address such a challenge, integrating metamodels and robust design optimization has been applied. In the current paper, a systematic comparative study is implemented to evaluate the performance of three common metamodels, namely polynomial regression, kriging, and radial basis function. The required experiments are designed by different space-filling methods including the orthogonal array design and three forms of Latin hypercube sampling such as randomized, maximin, and correlation approaches. Although, the impact of sample size on the performance of metamodels in robust optimization results are investigated. All methods are analyzed using five two-dimensional test problems and one engineering problem while all of them are considered in two forms that are expensive (with a small sample size) and semi-expensive (with a large sample size). Uncertainty is assumed in all problems as a source of variability, so all test problems are conducted in the format of robust optimization in the class of dual response surface in order to estimate robust Pareto frontier. The performances of methods are studied in two terms of accuracy and robustness. Finally, the results of comparison, an applicable guideline is provided to aid the practitioners in selecting the appropriate combination of metamodels and sampling design methods for investigating set of robust optimal points (estimated Pareto frontier) in simulation–optimization problems under uncertainty.

Keywords: metamodel, kriging, radial basis function, polynomial regression, simulation optimization, Latin hypercube sampling, orthogonal array

Suggested Citation

Parnianifard, Amir and Azfanizam, A and Ariffin, Mohd Khairol Anuar Mohd and Ismail, Mohd Idris Shah, Comparative Study of Metamodeling and Sampling Design for Expensive and Semi-Expensive Simulation Models Under Uncertainty (June 5, 2019). Available at SSRN: https://ssrn.com/abstract=3399318 or http://dx.doi.org/10.2139/ssrn.3399318

Amir Parnianifard (Contact Author)

University Putra Malaysia ( email )

Selangor Darul Ehsan
Serdang, Selangor 43400
Malaysia
00989138207031 (Phone)

HOME PAGE: http://https://sites.google.com/site/aparnianifard/

A Azfanizam

University Putra Malaysia ( email )

Selangor Darul Ehsan
Serdang, Selangor 43400
Malaysia

Mohd Khairol Anuar Mohd Ariffin

University Putra Malaysia ( email )

Selangor Darul Ehsan
Serdang, Selangor 43400
Malaysia

Mohd Idris Shah Ismail

University Putra Malaysia ( email )

Selangor Darul Ehsan
Serdang, Selangor 43400
Malaysia

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