Simulation optimization of a multi-stage multi-product paint shop line with Response Surface Methodology

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...

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Bibliographic Details
Published in:Simulation (San Diego, Calif.) Calif.), 2014-03, Vol.90 (3), p.265-274
Main Authors: Dengiz, Berna, Belgin, Onder
Format: Article
Language:English
Subjects:
Applied sciences
Computer simulation
Decision theory. Utility theory
Exact sciences and technology
Experimental design
Manufacturing engineering
Mathematical analysis
Mathematical models
Mathematical programming
Mathematics
Mechanical engineering. Machine design
Operational research and scientific management
Operational research. Management science
Optimization
Probability and statistics
Response surface methodology
Sciences and techniques of general use
Sensitivity analysis
Statistics
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Summary: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.
ISSN:0037-5497
1741-3133
DOI:10.1177/0037549713516508