Two-stage risk-neutral stochastic optimization problem has been widely studied recently. The goals of our research are to construct a two-stage distributionally robust model with risk aversion and extend it multi-stage case. We use coherent measure, Conditional Value-at-Risk, describe risk. Due the computational complexity nonlinear objective function proposed model...

Two-stage optimization with recourse model is an important and widely used model, which has been studied extensively these years. In this article, we will look at a new variant of it, called the two-stage optimization with recourse and revocation model. This new model differs from the traditional one in that one is allowed to revoke some of his earlier decisions and withdraw part of the earlier...

Multi-stage optimization under uncertainty techniques can be used to solve long-term management problems. Although many optimization modeling language extensions as well as computational environments have been proposed, the acceptance of this technique is generally low, due to the inherent complexity of the modeling and solution process. In this paper a simplification to annotate multi-stage de...

Multi-stage simulation and optimization models are effective for solving long-term financial planning problems. Prominent examples include: asset-liability management for pension plans, integrated risk management for insurance companies, and long-term planning for individuals. Several applications will be briefly mentioned. A multi-stage framework provides advantages over single-period myopic a...

Robust optimization extends stochastic programming models by incorporating measures of variability into the objective function. This paper explores robust optimization in the context of two-stage planning systems. First, we propose the use of a generalized Benders decomposition algorithm for solving robust models. Next, we argue that using an arbitrary measure for variability can lead to sub-op...