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Process Optimization

A Statistical Approach

  • Enrique Del Castillo

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 105)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Preliminaries

    1. Front Matter
      Pages 2-2
  3. Elements of Response Surface Methods

  4. Statistical Inference in Process Optimization

  5. Robust Parameter Design and Robust Optimization

    1. Front Matter
      Pages 222-222
    2. Pages 223-278
    3. Pages 279-287
  6. Bayesian Approaches in Process Optimization

    1. Front Matter
      Pages 290-290
  7. Introduction to Optimization of Simulation and Computer Models

    1. Front Matter
      Pages 366-366
    2. Pages 367-378
  8. Appendices

  9. Back Matter
    Pages 445-459

About this book

Introduction

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.


 

Keywords

ANOVA Analysis Analysis of variance MATLAB Maple Regression electronics linear regression mathematical programming model optimization programming simulation

Authors and affiliations

  • Enrique Del Castillo
    • 1
  1. 1.Pennsylvania State UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-71435-6
  • Copyright Information Springer-Verlag US 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Business and Economics
  • Print ISBN 978-0-387-71434-9
  • Online ISBN 978-0-387-71435-6
  • Series Print ISSN 0884-8289
  • Buy this book on publisher's site