Add to ORKGABSTRACT: The aim of this study is to analyse the resolution of Stochastic Programming Problems in which the objective function depends on parameters which are continuous random variables with a known distribution probability. In the literature on these questions different solution concepts have been defined for problems of these characteristics. These concepts are obtained by applying a transformation criterion to the stochastic objective which contains a statistical feature of the objective, implying that for the same stochastic problem there are different optimal solutions available which, in principle, are not comparable. Our study analyses and establishes some relations between these solution concepts
Stochastic programming is a mathematical optimization model for decision making when the uncertainty...
We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stoch...
Abstract. Optimality functions define stationarity in nonlinear programming, semi-infinite opti-miza...
Finding optimal decisions often involves the consideration f certain random or unknown parameters. A...
This report presents an approach to stochastic programming. It treats mainly the difficulties arisin...
In this paper we consider stochastic programming problems where the objec-tive function is given as ...
In this paper we consider stochastic programming problems where the objective function is given as a...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
In practice we often have to solve optimization problems with several criteria. These problems are c...
summary:The aim of this paper is to present some ideas how to relax the notion of the optimal soluti...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
The optimal solution, as well as the objective of stochastic programming problems vary with the unde...
This thesis concentrates on different approaches of solving decision making problems with an aspect ...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
Stochastic problems (both two-stage and multistage) can be formulated in several di erent ways which...
Stochastic programming is a mathematical optimization model for decision making when the uncertainty...
We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stoch...
Abstract. Optimality functions define stationarity in nonlinear programming, semi-infinite opti-miza...
Finding optimal decisions often involves the consideration f certain random or unknown parameters. A...
This report presents an approach to stochastic programming. It treats mainly the difficulties arisin...
In this paper we consider stochastic programming problems where the objec-tive function is given as ...
In this paper we consider stochastic programming problems where the objective function is given as a...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
In practice we often have to solve optimization problems with several criteria. These problems are c...
summary:The aim of this paper is to present some ideas how to relax the notion of the optimal soluti...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
The optimal solution, as well as the objective of stochastic programming problems vary with the unde...
This thesis concentrates on different approaches of solving decision making problems with an aspect ...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
Stochastic problems (both two-stage and multistage) can be formulated in several di erent ways which...
Stochastic programming is a mathematical optimization model for decision making when the uncertainty...
We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stoch...
Abstract. Optimality functions define stationarity in nonlinear programming, semi-infinite opti-miza...