This book is an ideal 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, amongst other things, confidence regions on the optimal settings of a process and stopping rules in experimental optimization. It presents a detailed treatment of Bayesian Optimization approaches. It 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.