Computational Methods in Stochastics and Optimization

Abstract

Fundamentals of stochastic simulation methods and non-linear optimization. Application of stochastical methods for the prediction of process stability and robsutness. Methods of non-linear optimizaion for complex manufacturing systems.

Objective

Real systems are submitted to process parameter variations. In spite of this most research is performed assuming deterministic boundary conditions, in which all parameters are constant. As a consequence, such research cannot draw conclusions on real system behavior, but only on behavior under singular conditions.

Content

Fundamentals of stochastic simulation methods and non-linear optimization are treated. After defining the fundamental parameters in process sensitivity and reliability (Cp-, Ck-value, n-Sigma process) the course focuses on the computational methods necessary to predict these parameters. In this context the most important methods of the statistic process-planing Monte Carlo, Latin Hypercube,...) will be treated.

Resources