This paper considers stochastic linear programming models for pvoduttkmpla^ cost coefficient and RHS term uncertainties arc represented by finite discrete probability distribution functions. The sedation of the two-stage fixed recourse problem is considered, for which a sensitivity-based successive disaggregatkm algorithm is outlined. The bounding properties of the aggregate sub-problems are ex...

In this paper, a Multi-Choice Stochastic Bi-Level Programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabilistic constraints are transformed into deterministic constraints using stochastic programming approach. Further, a general tran...

In this paper we discuss computational complexity and risk averse approaches to two and multistage stochastic programming problems. We argue that two stage (say linear) stochastic programming problems can be solved with a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of multistage programs grows fast with increase of the number of stages. We ...

Stochastic integer programs are notoriously difficult. Very few properties are known and solution algorithms are very scarce. In this paper, we introduce the class of stochastic programs with simple integer recourse, a natural extension of the simple recourse case extensively studied in stochastic continuous programs. Analytical as well as computational properties of the expected recourse funct...

In this paper, assuming cooperative behavior of the decision makers, solution methods for decisionmaking problems in hierarchical organizations under fuzzy randomenvironments are considered. To deal with the formulated two-level linear programming problems involving fuzzy random variables, α-level sets of fuzzy random variables are introduced and an α-stochastic two-level linear programming pro...

This article aims to explain the Nested Benders algorithm for the solution of large-scale stochastic programming problems in a way that is intelligible to someone coming to it for the first time. In doing so it gives an explanation of Benders decomposition and of its application to two-stage stochastic programming problems (also known in this context as the L-shaped method), then extends this t...