Stock photo: cover may vary
Process Optimization Hardback - - 1st Edition
by Enrique del Castillo
- New
- Hardback
A$139.32
A$5.72
Delivery within USA
Standard delivery: 9 to 14 days
More delivery options
Standard delivery: 9 to 14 days
Ships from Cold Books (New York, United States)
Details
- Title Process Optimization
- Author Enrique del Castillo
- Binding Hardback
- Edition number 1st
- Edition 1
- Condition New
- Pages 462
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date pp. 484
- Features Bibliography, Index, Table of Contents
- Bookseller's Inventory # 6289024
- 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)
- Category Technology & Industrial Arts
- Library of Congress subjects Mathematical optimization, Optimaliseren
- Library of Congress Catalogue Number 2007922933
- Dewey Decimal Code 620
- Quantity available 4
About Cold Books New York, United States
Reader reviews for Process Optimization
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
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.
