We address the inventory planning problem in process networks under uncertainty through stochastic programming models. The scope of inventory planning requires the formulation of multiperiod models to represent the time-varying conditions of industrial process, but the multistage stochastic programming formulations are often too large to solve. We propose a policy-based approximation of the mul...

Unit commitment (UC) is a key operational problem in power systems used to determine an optimal daily or weekly generation commitment schedule. Incorporating uncertainty in this already difficult mixed-integer optimization problem introduces significant computational challenges. Most existing stochastic UC models consider either a two-stage decision structure, where the commitment schedule for ...

The capacity planning and operation problem of a multi-purpose water reservoir has been extensively studied in the literature. Optimization techniques, especially linear programming, have been used quite successfully. Although most models are robust enough as planning tools, they fall short of providing an operational policy for the shorter term. In this paper, we consider the operation of a mu...

We consider power system expansion planning under uncertainty. In our approach, integer programming and stochastic programming provide a basic framework. We develop a multistage stochastic programming model in which some of the variables are restricted to integer values. By utilizing the special property of the problem, called block separable recourse, the problem is transformed into a two-stag...

In stochastic programming and decision analysis, an important issue consists in the approximate representation of the multidimensional stochastic underlying process in the form of scenario tree. This paper presents the approach to generate the multistage multidimensional scenario tree out of a set of scenario fans. For this purpose, the multistage K-means clustering algorithm is developed. The ...