In this work we extend to the multistage case two recent risk averse measures for two-stage stochastic programs based on first- and second-order stochastic dominance constraints induced by mixed-integer linear recourse. Additionally, we consider Time Stochastic Dominance (TSD) along a given...

The optimization of stochastic linear problems, via scenario analysis, based on Benders decomposition requires to appending feasibility and/or optimality cuts to the master problem until the iterative procedure reaches the optimal solution. The cuts are identified by solving the auxiliary...

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of...

Preprint submitted to Computers & Operations Research

The aim of this technical report is to present some detailed explanations in order to help to understand and use the algorithm Branch and Fix Coordination for solving MultiStage Mixed Integer Problems (BFC- MSMIP). We have developed an algorithmic approach implemented in a C++ experimental code...

In this paper we study solution methods for solving the dual problem corresponding to the Lagrangean Decomposition of two stage stochastic mixed 0-1 models. We represent the two stage stochastic mixed 0-1 problem by a splitting variable representation of the deterministic equivalent model, where...

We present an algorithmic approach for solving two-stage stochastic mixed 0-1 problems. The first stage constraints of the Deterministic Equivalent Model have 0--1 variables and continuous variables. The approach uses the Twin Node Family (TNF) concept within the algorithmic framework so-called...