Abstract
Most real - world decision problems involve both resources and uncertain future events. Very often resources available are not known exactly, owing to the stochastic character of the system. In this thesis we consider stochastic nonlinear integer programming where the right - hand sides of constraints are subject to given probability distributions. Two stochastic nonlinear integer programming problems are considered. One is the distribution problem in which the distribution function of the optimal value of the objective function is derived; The other is a parametric chance - constrained problem in which parametric analysis is made on the probability levels of the chance constraints. we propose solution methods for these two problems and illustrative applications of these methodologies to system reliability optimization problems are also given.