The behaviour of stochastic programming problems is studied in case of the underlying
probability distribution being perturbed and approximated, respectively. Most of the …

Publisher Summary This chapter focuses on stochastic programming. The stochastic
programming model can be viewed as an extension of the linear and nonlinear …

The aim of stochastic programming is to find optimal decisions in problems which involve
uncertain data. This field is currently developing rapidly with contributions from many …

Quantitative stability of optimal values and solution sets to stochastic programming problems
is studied when the underlying probability distribution varies in some metric space of …

For stochastic programs with recourse and with (several joint) probabilistic constraints,
respectively, we derive quantitative continuity properties of the relevant expectation …

Stability and sensitivity studies for stochastic programs have been motivated by the problem
of incomplete information about the true probability measure through which the stochastic …

For two-stage stochastic programs with integrality constraints in the second stage, we study
continuity properties of the expected recourse as a function both of the first-stage policy and …

Management and measurement of risk is an important issue in almost all areas that require
decisions to be made under uncertain information. Chance Constrained Programming …

Stochastic Programming offers models and methods for decision problems wheresome of
the data are uncertain. These models have features and structural properties which are …

Introducing probabilistic constraints leads in general to nonconvex, nonsmooth or even
discontinuous optimization models. In this paper, necessary and sufficient conditions for …