Ebook: Process Optimization: A Statistical Approach

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.

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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.

 

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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.

 

Content:
Front Matter....Pages i-xviii
Front Matter....Pages 2-2
An Overview of Empirical Process Optimization....Pages 3-25
Front Matter....Pages 28-28
Optimization Of First Order Models....Pages 29-43
Experimental Designs For First Order Models....Pages 45-83
Analysis and Optimization of Second Order Models....Pages 85-107
Experimental Designs for Second Order Models....Pages 109-156
Front Matter....Pages 158-158
Statistical Inference in First Order RSM Optimization....Pages 159-192
Statistical Inference in Second Order RSM Optimization....Pages 193-208
Bias Vs. Variance....Pages 209-219
Front Matter....Pages 222-222
Robust Parameter Design....Pages 223-278
Robust Optimization....Pages 279-287
Front Matter....Pages 290-290
Introduction to Bayesian Inference....Pages 291-319
Bayesian Methods for Process Optimization....Pages 321-364
Front Matter....Pages 366-366
Simulation Optimization....Pages 367-378
Kriging and Computer Experiments....Pages 379-395
Front Matter....Pages 398-398
Basics of Linear Regression....Pages 399-412
Analysis of Variance....Pages 413-427
Matrix Algebra and Optimization Results....Pages 429-441
Some Probability Results Used in Bayesian Inference....Pages 443-444
Back Matter....Pages 445-459

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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.

 

Content:
Front Matter....Pages i-xviii
Front Matter....Pages 2-2
An Overview of Empirical Process Optimization....Pages 3-25
Front Matter....Pages 28-28
Optimization Of First Order Models....Pages 29-43
Experimental Designs For First Order Models....Pages 45-83
Analysis and Optimization of Second Order Models....Pages 85-107
Experimental Designs for Second Order Models....Pages 109-156
Front Matter....Pages 158-158
Statistical Inference in First Order RSM Optimization....Pages 159-192
Statistical Inference in Second Order RSM Optimization....Pages 193-208
Bias Vs. Variance....Pages 209-219
Front Matter....Pages 222-222
Robust Parameter Design....Pages 223-278
Robust Optimization....Pages 279-287
Front Matter....Pages 290-290
Introduction to Bayesian Inference....Pages 291-319
Bayesian Methods for Process Optimization....Pages 321-364
Front Matter....Pages 366-366
Simulation Optimization....Pages 367-378
Kriging and Computer Experiments....Pages 379-395
Front Matter....Pages 398-398
Basics of Linear Regression....Pages 399-412
Analysis of Variance....Pages 413-427
Matrix Algebra and Optimization Results....Pages 429-441
Some Probability Results Used in Bayesian Inference....Pages 443-444
Back Matter....Pages 445-459
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