Optimization problems arising in practice usually contain several random parameters. Hence, in order to obtain optimal solutions being robust with respect to random parameter variations, the mostly available statistical information about the random parameters should be considered already at the...

General Remarks on Robust Solutions -- Modeling of Uncertainty and Probabilistic Issues -- On Joint Modelling of Random Uncertainty and Fuzzy Imprecision -- On the Approximation of a Discrete Multivariate Probability Distribution Using the New Concept of -Cherry Junction Tree -- Robust Solutions...

In engineering and economics a certain vector of inputs or decisions must often be chosen, subject to some constraints, such that the expected costs arising from the deviation between the output of a stochastic linear system and a desired stochastic target vector are minimal. In many cases the...

Managing safety of diverse systems requires decision-making under uncertainties and risks. Such systems are typically characterized by spatio-temporal heterogeneities, inter-dependencies, externalities, endogenous risks, discontinuities, irreversibility, practically irreducible uncertainties,...

This volume includes a selection of refereed papers presented at the GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications", held at the Federal Armed Forces University Munich,May 29 - 31, 1990. The objective of this meeting was to bring together scientists...

In order to obtain more reliable optimal solutions of concrete technical/economic problems, e.g. optimal design problems, the often known stochastic variations of many technical/economic parameters have to be taken into account already in the planning phase. Hence, ordinary mathematical programs...