Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter 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
Low cost response surface methods for and from simulation optimization
Proceedings of the 32nd conference on Winter simulation
Simulation with Arena
Proceedings of the 33nd conference on Winter simulation
Recent advances in simulation optimization: response surface methodology revisited
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Automated response surface methodology for stochastic optimization models with unknown variance
WSC '04 Proceedings of the 36th conference on Winter simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
A generalized multiple response surface methodology for complex computer simulation applications
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
The impact of ordinal on response surface methodology
Proceedings of the 38th conference on Winter simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Response surface methodology for simulating hedging and trading strategies
Proceedings of the 40th Conference on Winter Simulation
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Recently, Response Surface Methodology (RSM) has attracted a growing interest, along with other simulation optimization (SO) techniques, for non-parametric modeling and robust optimization of systems. In the optimization stage of this study, the authors use RSM to find optimum working conditions of a system. The authors also use discrete event simulation modeling, optimization stage integration, design of experiment (DOE) and sensitivity analysis (a) to investigate the behavior of a real paint shop production line via construction of response surface plots and (b) to reveal the influence of input variables, as well as to determine interaction effects between them. The proposed approach presents an approximation model management structure for the computation-intensive optimization problem of an automotive factory with reduced variance, computational cost and amount of effort.