Automated response surface methodology for stochastic optimization models with unknown variance

Robin P. Nicolai; Rommert Dekker; Nanda Piersma; Gerrit J. Van Oortmarssen

Automated response surface methodology for stochastic optimization models with unknown variance

Robin P. Nicolai^*
, Rommert Dekker
, Nanda Piersma
, Gerrit J. Van Oortmarssen

^*Corresponding author for this work

Erasmus University Rotterdam

Research output: Contribution to journal › Conference article › Popular

17 Citations (Scopus)

Abstract

Response Surface Methodology (RSM) is an optimization tool that was introduced in the early 50's by Box and Wilson (1951). In this paper we are interested in finding the best settings for an automated RSM procedure when there is very little information about the objective function. We will present a framework of the RSM procedures that is founded in recognizing local optima in the presence of noise. We emphasize both stopping rules and restart procedures. The results show that considerable improvement is possible over the proposed settings in the existing literature.

Original language	English
Pages (from-to)	491-499
Number of pages	9
Journal	Proceedings - Winter Simulation Conference
Volume	1
Publication status	Published - 2004
Event	Proceedings of the 2004 Winter Simulation Conference - Washington, DC, United States Duration: 5 Dec 2004 → 8 Dec 2004

Cite this

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title = "Automated response surface methodology for stochastic optimization models with unknown variance",

abstract = "Response Surface Methodology (RSM) is an optimization tool that was introduced in the early 50's by Box and Wilson (1951). In this paper we are interested in finding the best settings for an automated RSM procedure when there is very little information about the objective function. We will present a framework of the RSM procedures that is founded in recognizing local optima in the presence of noise. We emphasize both stopping rules and restart procedures. The results show that considerable improvement is possible over the proposed settings in the existing literature.",

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T1 - Automated response surface methodology for stochastic optimization models with unknown variance

AU - Nicolai, Robin P.

AU - Dekker, Rommert

AU - Piersma, Nanda

AU - Van Oortmarssen, Gerrit J.

PY - 2004

Y1 - 2004

N2 - Response Surface Methodology (RSM) is an optimization tool that was introduced in the early 50's by Box and Wilson (1951). In this paper we are interested in finding the best settings for an automated RSM procedure when there is very little information about the objective function. We will present a framework of the RSM procedures that is founded in recognizing local optima in the presence of noise. We emphasize both stopping rules and restart procedures. The results show that considerable improvement is possible over the proposed settings in the existing literature.

AB - Response Surface Methodology (RSM) is an optimization tool that was introduced in the early 50's by Box and Wilson (1951). In this paper we are interested in finding the best settings for an automated RSM procedure when there is very little information about the objective function. We will present a framework of the RSM procedures that is founded in recognizing local optima in the presence of noise. We emphasize both stopping rules and restart procedures. The results show that considerable improvement is possible over the proposed settings in the existing literature.

UR - https://www.scopus.com/pages/publications/17744374816

M3 - Conference article

AN - SCOPUS:17744374816

SN - 0891-7736

VL - 1

SP - 491

EP - 499

JO - Proceedings - Winter Simulation Conference

JF - Proceedings - Winter Simulation Conference

T2 - Proceedings of the 2004 Winter Simulation Conference

Y2 - 5 December 2004 through 8 December 2004

ER -

Automated response surface methodology for stochastic optimization models with unknown variance

Abstract

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