Process Optimization

Author : Enrique del Castillo Year : 2007

This book is intended as a textbook for a second course in experimental optimization techniques for industrial production processes and other “noisy” systems where the main emphasis is process optimization. This includes courses in “Response Surface Methods” and related topics. The book has outgrown from class notes of a graduate course that I have given for the past 10 years to Industrial Engineering and Operations Research students at Penn State University and at the University of Texas at Arlington. Typically, students come to this course with some background in either Design of Experiments (DOE) or Linear Regression. Many students also come to the course with a background in optimization methods. After teaching this course for several years based on other DOE and Response Surface Methods (RSM) books, it became clear the need for a book more suited to graduate engineering students, who learn about a wide variety of optimization techniques in other courses yet are somewhat disenchanted because there is no apparent connection between those optimization techniques and DOE/RSM.

For a person with a more traditional Statistics or Quality Engineering background, the present book will serve as a reference to techniques that complement and extend basic process optimization techniques from DOE and RSM, including statistical issues that arise in process optimization, Bayesian methods for process optimization, and an introduction to Stochastic Approximation, Kriging methods and “computer experiment” techniques. For a person with an Operations Research background which includes mathematical programming techniques, the present book will not only serve as a guide to DOE and RSM, but will show how important statistical considerations need to be taken into account while optimizing a noisy process.

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