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

We investigate the quality of solutions obtained from sample-average approximations to two-
stage stochastic linear programs with recourse. We use a recently developed software tool …

We consider three models of investments in generation capacity in restructured electricity
systems that differ with respect to their underlying economic assumptions. The first model …

Stochastic programming with recourse usually assumes uncertainty to be exogenous. Our
work presents modelling and application of decision-dependent uncertainty in mathematical …

This paper focuses on Benders decomposition techniques and Monte Carlo sampling
(importance sampling) for solving two-stage stochastic linear programs with recourse, a …

In this chapter we begin with a brief explanation of linear programming (LP) since integer
programming (IP) is usually regarded as an extension of LP. Also most practical methods of …

For many practical problems, solutions obtained from deterministic models are
unsatisfactory because they fail to hedge against certain contingencies that may occur in the …

We generalize the definition of the bounds for the optimal value of the objective function for
various deterministic equivalent models in multistage stochastic programs. The parameters …

In this paper, a new model for generation and transmission expansion is presented. This
new model considers as random events the demand, the equivalent availability of the …

Our goal is to demonstrate for an important class of multistage stochastic models that three
techniques—namely nested decomposition, Monte Carlo importance sampling, and parallel …