Rather general multiobjective optimization problems depending on a probability measure correspond often to situations in which an economic or financial process is simultaneously influenced by a random factor and a “decision” parameter; moreover simultaneously it is reasonable to evaluate the process by a few objective functions and it seems reasonable to determine the decision with to the mathematical expectation of objectives. A complete knowledge of the probability measure is a necessary assumption to analyze the problem. However, in applications mostly the problem has to be solved on the data base. A relationship between “characteristics” obtained on the base of complete knowledge of the probability measure and them obtained on the abov...
Many economic and financial situations depend simultaneously on a random element and on a decision p...
The thesis presents stochastic programming with chance contraints. We begin with the definition of c...
To model decision problems involving uncertainty and probability, we propose stochastic constraint p...
Multiobjective optimization problems depending on a probability measure correspond to many economic...
Many economic and financial situations depend simultaneously on a random element and a decision par...
In practice we often have to solve optimization problems with several criteria. These problems are c...
Many Optimization problems in engineering and economics involve the challenging task of pondering bo...
In this paper, the author looks at some quite general optimization problems on the space of probabil...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
In this work we study optimization problems subject to a failure constraint. This constraint is expr...
Nonlinear dependence on a probability measure begins to appear (last time) in a stochastic optimizat...
Stochastic optimization is an effective tool for analyzing decision problems under uncertainty. In s...
Deterministic optimization models are usually formulated as problems of mini-mizing or maximizing a ...
This thesis is a small contribution to the Mathematical framework of Multi-Objective Optimization. S...
Real-world optimization problems are often subject to uncertainties, which can arise regarding stoch...
Many economic and financial situations depend simultaneously on a random element and on a decision p...
The thesis presents stochastic programming with chance contraints. We begin with the definition of c...
To model decision problems involving uncertainty and probability, we propose stochastic constraint p...
Multiobjective optimization problems depending on a probability measure correspond to many economic...
Many economic and financial situations depend simultaneously on a random element and a decision par...
In practice we often have to solve optimization problems with several criteria. These problems are c...
Many Optimization problems in engineering and economics involve the challenging task of pondering bo...
In this paper, the author looks at some quite general optimization problems on the space of probabil...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
In this work we study optimization problems subject to a failure constraint. This constraint is expr...
Nonlinear dependence on a probability measure begins to appear (last time) in a stochastic optimizat...
Stochastic optimization is an effective tool for analyzing decision problems under uncertainty. In s...
Deterministic optimization models are usually formulated as problems of mini-mizing or maximizing a ...
This thesis is a small contribution to the Mathematical framework of Multi-Objective Optimization. S...
Real-world optimization problems are often subject to uncertainties, which can arise regarding stoch...
Many economic and financial situations depend simultaneously on a random element and on a decision p...
The thesis presents stochastic programming with chance contraints. We begin with the definition of c...
To model decision problems involving uncertainty and probability, we propose stochastic constraint p...