Cost-based filtering is a novel approach that combines techniques from Operations Research and Constraint Programming to filter from decision variable domains values that do not lead to better solutions [7]. Stochastic Constraint Programming is a framework for modeling combinatorial optimization problems that involve uncertainty [19]. In this work, we show how to perform cost-based filtering fo...

Introduction With IBM's new commercial release of a general-purpose solver, stochastic programming is finally moving out of the research laboratories and into the field. The focus is now on methods and technologies that support the ''practical art'' of stochastic programming. In this simple article, I will present the basic features of IBM's stochastic programming solver as they may concern pra...

The mine production planning defines a sequence of block extraction to obtain the highest NPV under a number of constraints. Mathematical programming has become a widespread approach to optimize production planning, for open pit mines since the 1960s. However, the previous and existing models are found to be limited in their ability to explicitly incorporate the ore grade uncertainty into the p...

For a particular class of minimax stochastic programming models, we show that the problem can be equivalently reformulated into a standard stochastic programming problem. This permits the direct use of standard decomposition and sampling methods developed for stochastic programming. We also show that this class of minimax stochastic programs is closely related to a large family of mean-risk sto...

A framework of stochastic multilevel programming is proposed for modelling decentralized decision-making problem in stochastic environment. According to different decision criteria, the stochastic decentralized decision-making problem is formulated as expected value multilevel programming, and chanceconstrained multilevel programming. In order to solve the proposed stochastic multilevel program...

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...

Stochastic programming deals with optimization under uncertainty. A stochastic programming problem with recourse is referred to as a two-stage stochastic problem. We consider the stochastic programming problem with simple integer recourse in which the value of the recourse variable is restricted to a multiple of a nonnegative integer. The algorithm of a dynamic slope scaling procedure to solve ...

Abstract In this paper, we study multistage stochastic mixed-integer nonlinear programs (MS-MINLP). This general class of problems encompasses, as important special cases, convex optimization with non-Lipschitzian value functions and linear optimization. We develop dual dynamic programming (SDDP) type algorithms nested decomposition, deterministic sampling, sampling. The key ingredient is a new...