Preface.

Abstract

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. The point of view of the book is to provide in the form of a text a contemporary account not only of the classical techniques and tools used in DOE and RSM but also to present relatively more advanced process optimization techniques from the recent literature which, perhaps due to lack of exposure or due to their young age, have not been used that much in industrial practice. The book 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. For a person with a more traditional Statistics or Quality Engineering background, the present book will serve as a reference to techniques that