This paper presents a tutorial on the state-of-the-art software for solution of two-stage (mixed-integer) linear stochastic programs and provides list designed this purpose. The methodologies are classified according to decomposition alternatives types variables in problem. We review fundamentals Benders decomposition, dual progressive hedging, as well possible improvements variants. also prese...

This paper deals with two-stage and multi-stage stochastic programs in which the right-hand sides of the constraints are Gaussian random variables. Such problems are of interest since the use of Gaussian estimators of random variables is widespread. We introduce algorithms to nd upper bounds on the optimal value of two-stage and multi-stage stochastic (minimization) programs with Gaussian right...

We propose a scenario decomposition algorithm for stochastic 0-1 programs. The algorithm recovers an optimal solution by iteratively exploring and cutting-off candidate solutions obtained from solving scenario subproblems. The scheme is applicable to quite general problem structures and can be implemented in a distributed framework. Illustrative computational results on standard two-stage stoch...

This paper proposes an integrated methodology and its solution for supplier selection problem. A twostage stochastic programming model and fuzzy TOPSIS methods are consolidated in this methodology. After pre-research phase, in the second part of the methodology, fuzzy TOPSIS is used for ranking potential suppliers considering qualitative data under fuzzy environment. In the third part of the me...