Empirical model-building and response surface
Empirical model-building and response surface
Small response-surface designs
Technometrics
Designing simulation experiments
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Modeling multiple input switching of CMOS gates in DSM technology using HDMR
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Paintshop production line optimization using response surface methodology
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Hi-index | 0.02 |
We propose "low cost response surface methods" (LCRSM) that typically require half the experimental runs of standard response surface methods based on central composite and Box Behnken designs but yield comparable or lower modeling errors under realistic assumptions. In addition, the LCRSM methods have substantially lower modeling errors and greater expected savings compared with alternatives with comparable numbers of runs, including small composite designs and computer-generated designs based on popular criteria such as D-optimality. Therefore, when simulation runs are expensive, low cost response surface methods can be used to create regression meta-models for queuing or other system optimization. The LCRSM procedures are also apparently the first experimental design methods derived as the solution to a simulation optimization problem. For these reasons, we say that LCRSM are "for and from" simulation optimization. We compare the proposed LCRSM methods with a large number of alternatives based on six criteria. We conclude that the proposed methods offer attractive alternative to current methods in many relevant situations.