Stochastic programming models are not static, but dynamic and adaptive. As new information, data, or feedback becomes available, you need to update and refine your model to incorporate the changes ...

Stochastic programming can be applied to a wide range of OR problems and domains, such as production planning, inventory management, supply chain management, project management, finance, energy ...

In the contribution presented we deal with one method of stochastic programming - probabilistic programming. Probabilistic programming is a method of stochastic programming in which the probabilities ...

Discover the power of stochastic quadratic programming with imperfect probability distribution. Explore our paper on the innovative linear partial information (LPI) theory and a direct optimizing ...

Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper ...

We employ a stochastic programming approach to solve these problems, which has advantages over other methods and captures the underlying institutional structure of the sidecar. We detail the method ...

Stochastic programming methods can be applied to portfolio optimization in the context of pension fund management. In this paper we apply stochastic programming wherein the allocations are found among ...