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.
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.
