TY - BOOK ID - 4868270 TI - Stochastic Optimization Methods : Applications in Engineering and Operations Research PY - 2015 SN - 9783662462140 3662462133 9783662462133 3662462141 PB - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, DB - UniCat KW - Economics/Management Science. KW - Operation Research/Decision Theory. KW - Optimization. KW - Computational Intelligence. KW - Economics. KW - Mathematical optimization. KW - Engineering. KW - Operations research. KW - Economie politique KW - Optimisation mathématique KW - Ingénierie KW - Recherche opérationnelle KW - Stochastic processes. KW - Management KW - Business & Economics KW - Management Theory KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Random processes KW - Business. KW - Decision making. KW - Computational intelligence. KW - Business and Management. KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - Simulation methods KW - System analysis KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Deciding KW - Decision (Psychology) KW - Decision analysis KW - Decision processes KW - Making decisions KW - Management decisions KW - Choice (Psychology) KW - Problem solving KW - Operational analysis KW - Operational research KW - Industrial engineering KW - Management science KW - Research KW - System theory KW - Trade KW - Economics KW - Commerce KW - Industrial management KW - Decision making KW - Probabilities KW - Operations Research/Decision Theory. KW - Construction KW - Industrial arts KW - Technology UR - https://www.unicat.be/uniCat?func=search&query=sysid:4868270 AB - 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 stochastic 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 stochastic 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, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research. ER -