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


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.



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