Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data,...

This work was completed during my tenure as a scientific assistant and d- toral student at the Institute for Operations Research at the University of St. Gallen. During that time, I was involved in several industry projects in the field of power management, on the occasion of which I was...

Models -- and Examples -- Uncertainty and Modeling Issues -- Basic Properties -- Basic Properties and Theory -- The Value of Information and the Stochastic Solution -- Solution Methods -- Two-Stage Linear Recourse Problems -- Nonlinear Programming Approaches to Two-Stage Recourse Problems --...

Why Do We Simulate? -- Simulation Programming: Quick Start -- Examples -- Simulation Programming with VBASim -- Two Views of Simulation -- Simulation Input -- Simulation Output -- Experiment Design and Analysis -- Simulation for Research -- VBASim

Einführung -- Materialplanung bei konventioneller und kreislaufgeführter Produktion -- Materialplanung bei externen Produktkreisläufen -- Materialplanung bei internen Produktkreisläufen -- Schlussbetrachtung.

DIE METHODISCHE GRUNDLEGUNG -- AUSGANGSBASIS: DIE BESTEHENDE ZINSSTRUKTURKURVE -- DIE ÄLTEREN THEORIEN ZUR ZINSSTRUKTUR -- DIE GRUNDMODELLE DER ZINSSTRUKTUR -- DIE DETERMINISTISCHEN FAKTOREN ZUR ZINSSTRUKTUR -- DIE SUBSTITUTIVEN ARBITRAGEPROZESSE ZUR ZINSSTRUKTUR -- DIE STOCHASTISCHEN...

Basic Concepts -- Regular Performance Measures -- Irregular Performance Measures -- Stochastic Machine Breakdowns -- Optimal Stopping Problems -- Multi-armed Bandit Processes -- Dynamic Policies -- Stochastic Scheduling with Incomplete Information -- Optimal Policies in Time-varying Scheduling...

This textbook gives a comprehensive introduction to stochastic processes and calculus in the fields of finance and economics, more specifically mathematical finance and time series econometrics. Over the past decades stochastic calculus and processes have gained great importance, because they...

This is a complete update of the first edition of Level Crossing Methods in Stochastic Models, which was published in 2008. Level crossing methods are a set of sample-path based mathematical tools used in applied probability to establish reliable probability distributions. Since the basis for...