Process Optimization A Statistical Approach
stopping rules in experimental optimization and more; Contains a discussion on robust optimization methods as used in mathematical programming and their application in response surface optimization; Offers software programs written in MATLAB and MAPLE to implement Bayesian and frequentist process op...
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| Format: | eBook |
| Language: | English |
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New York, NY
Springer US
2007, 2007
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| Edition: | 1st ed. 2007 |
| Series: | International Series in Operations Research & Management Science
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| Summary: | stopping rules in experimental optimization and more; Contains a discussion on robust optimization methods as used in mathematical programming and their application in response surface optimization; Offers software programs written in MATLAB and MAPLE to implement Bayesian and frequentist process optimization methods; Provides an introduction to the optimization of computer and simulation experiments including and introduction to stochastic approximation and stochastic perturbation stochastic approximation (SPSA) methods; Includes an introduction to Kriging methods and experimental design for computer experiments; Provides extensive appendices on Linear Regression, ANOVA, and Optimization Results. The major features of PROCESS OPTIMIZATION: A Statistical Approach are: It provides a complete exposition of mainstream experimental design techniques, including designs for first and second order models, response surface and optimal designs; Discusses mainstream response surface method in detail, including unconstrained and constrained (i.e., ridge analysis and dual and multiple response) approaches; Includes an extensive discussion of Robust Parameter Design (RPD) problems, including experimental design issues such as Split Plot designs and recent optimization approaches used for RPD; Presents a detailed treatment of Bayesian Optimization approaches based on experimental data (including an introduction to Bayesian inference), including single and multiple response optimization and model robust optimization; Provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries. |
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| Physical Description: | XVIII, 462 p. 76 illus online resource |
| ISBN: | 9780387714356 |