Process Optimization: A Statistical Approach by Enrique Del CastilloProcess Optimization: A Statistical Approach by Enrique Del Castillo

Process Optimization: A Statistical Approach

byEnrique Del Castillo

Hardcover | August 6, 2007

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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.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, 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.
Title:Process Optimization: A Statistical ApproachFormat:HardcoverDimensions:480 pagesPublished:August 6, 2007Publisher:Springer USLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0387714340

ISBN - 13:9780387714349

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Table of Contents

Preface.- An overview of empirical process optimization.- Optimization based on 1st order polynomial models.- Experimental designs for 1st order models.- Analysis and optimization of 2nd order models.- Designs for 2nd order models.- Statistical inference in 1st order RSM.- Statistical inference in 2nd order RSM.- The bias vs. variance debate.- Robust parameter design.- Robust optimization.- Introduction to Bayesian inference.- Bayesian methods process optimization.- Simulation optimization.- Kriging and computer experiments.- Appendices.- References.- Index.