Process Optimization, Softcover reprint of hardcover 1st ed. 2007
A Statistical Approach
International Series in Operations Research & Management Science Series, Vol. 105

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Language: English

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Process optimization: a statistical approach (paperback) previously published in hardcover (series: international series in operations
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462 p. · 15.5x23.5 cm · Paperback

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Process optimization: A statistical approach (Int. series in operations research & management science, Vol. 105)
Publication date:
462 p. · 15.5x23.5 cm · Hardback

This book covers several bases at once. It is useful as a textbook for a second course in experimental optimization techniques for industrial production processes. In addition, it is a superb reference volume for use by professors and graduate students in Industrial Engineering and Statistics departments. It will also be of huge interest to applied statisticians, process engineers, and quality engineers working in the electronics and biotech manufacturing industries. In all, it 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.

Preliminaries.- An Overview of Empirical Process Optimization.- Elements of Response Surface Methods.- Optimization Of First Order Models.- Experimental Designs For First Order Models.- Analysis and Optimization of Second Order Models.- Experimental Designs for Second Order Models.- Statistical Inference in Process Optimization.- Statistical Inference in First Order RSM Optimization.- Statistical Inference in Second Order RSM Optimization.- Bias Vs. Variance.- Robust Parameter Design and Robust Optimization.- Robust Parameter Design.- Robust Optimization.- Bayesian Approaches in Process Optimization.- to Bayesian Inference.- Bayesian Methods for Process Optimization.- to Optimization of Simulation and Computer Models.- Simulation Optimization.- Kriging and Computer Experiments.- Appendices.- Basics of Linear Regression.- Analysis of Variance.- Matrix Algebra and Optimization Results.- Some Probability Results Used in Bayesian Inference.

A much stronger treatment of the topic than the Wiley books published in this area for these reasons: (1) on the strength of the book’s author and (2) on its coverage and treatment of process optimization

Provides in the form of a text a contemporary account not only of the classical techniques and tools used in Design of Experiments (DOE) and Response Surface Methods (RSM), but also to present more advanced process optimization techniques from the recent literature which has not been used that much in industrial practice

Contains a mix of technical and practical sections, appropriate for a first year graduate text in the subject or useful for self-study or reference

Includes supplementary material: sn.pub/extras