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Process Optimization: A Statistical Approach Hardback - 2007 - 1st Edition
by del Castillo, Enrique
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- Hardback
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Details
- Title Process Optimization: A Statistical Approach
- Author del Castillo, Enrique
- Binding Hardback
- Edition number 1st
- Edition 2007
- Condition New
- Pages 462
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2007-08-06
- Features Bibliography, Index, Table of Contents
- Bookseller's Inventory # DADAX0387714340
- ISBN 9780387714349 / 0387714340
- Weight 1.76 lbs (0.80 kg)
- Dimensions 9.38 x 6.51 x 1.09 in (23.83 x 16.54 x 2.77 cm)
- Size 6.25x1.00x9.25
- Category Technology & Industrial Arts
- Library of Congress subjects Mathematical optimization, Optimaliseren
- Library of Congress Catalogue Number 2007922933
- Dewey Decimal Code 620
- Quantity available 1
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From the publisher
From the rear cover
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.
