Stochastic Optimization Methods: Applications in Engineering and Operations Resear...

This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under shastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and shastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, shastic approximation and gradient procedures and differentiation formulas for probabilities and expectations.
In the third edition, this book further develops shastic optimization methods. In particular, it now shows how to apply shastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.