Automated response surface methodology for stochastic optimization models with unknown variance

Authors:
Robin P. Nicolai;Rommert Dekker;Nanda Piersma;Gerrit J. van Oortmarssen
Affiliations:
Erasmus University Rotterdam, Rotterdam, The Netherlands;Erasmus University Rotterdam, Rotterdam, The Netherlands;Erasmus University Rotterdam, Rotterdam, The Netherlands;Erasmus University Rotterdam, Rotterdam, The Netherlands
Venue:
WSC '04 Proceedings of the 36th conference on Winter simulation
Year:
2004

Citing 7
Cited 5

Empirical model-building and response surface

Empirical model-building and response surface
Response surfaces: designs and analyses

Response surfaces: designs and analyses
Response surface methodology: 1966–1988

Technometrics
An enhanced response surface methodology (RSM) algorithm using gradient deflection and second-order search strategies

Computers and Operations Research
New advances for wedding optimization and simulation

Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
A framework for Response Surface Methodology for simulation optimization

Proceedings of the 32nd conference on Winter simulation
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments

Response Surface Methodology: Process and Product in Optimization Using Designed Experiments

Stochastic trust region gradient-free method (strong): a new response-surface-based algorithm in simulation optimization

Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Paintshop production line optimization using response surface methodology

Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Simulation optimization using metamodels

Winter Simulation Conference
Stochastic Trust-Region Response-Surface Method STRONG---A New Response-Surface Framework for Simulation Optimization

INFORMS Journal on Computing
Simulation optimization of a multi-stage multi-product paint shop line with Response Surface Methodology

Simulation

Quantified Score

Hi-index	0.00

Visualization

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.