This paper considers the supply chains' problem for agricultural companies considering multiple scenarios using methodology of sample average approximation (SAA). We consider an established chain, in which central consists determination closure and consolidation distribution centres. In this work, a stochastic mathematical model representative chain has been formulated constraints nodes variati...

Proactive scheduling can effectively handle activity duration uncertainty in real-world projects, by generating a baseline solution according to a prior stochastic knowledge. However, most of the previous approaches cannot deal with the activity duration uncertainty caused by time-dependent workability uncertainty. In this paper, we aim at finding a partialorder schedule (POS) that produces the...

Utility-based choice models are often used to determine a consumer’s purchase decision among a list of available products, to provide an estimate of product demands, and when data on purchase decisions or market shares are available, to infer consumers’ preferences over observed product characteristics. They also serve as a building block in modeling firms’ pricing and assortment optimization p...

In this article, we discuss the sample average approximation (SAA) method applied to a class of stochastic mathematical programs with variational (equilibrium) constraints. To this end, we briefly investigate the structure of both – the lower level equilibrium solution and objective integrand. We show almost sure convergence of optimal values, optimal solutions (both local and global) and gener...

In this paper, we discuss the sample average approximation (SAA) method applied to a class of stochastic mathematical programs with variational (equilibrium) constraints. To this end, we briefly investigate the structure of both – the lower level equilibrium solution and objective integrand. We show almost sure convergence of optimal values, optimal solutions (both local and global) and general...

In this work we augment a known Monte Carlo simulationbased approach to stochastic discrete optimization problem, the so called Sample Average Approximation (SAA) method, with a new criterion to decide when the search has to be stopped. Our approach exploits a well known and effective sampling technique, Latin Hypercube Sampling (LHS), and confidence interval analysis, a well established approx...

We study chance-constrained problems in which the constraints involve the probability of a rare event. We discuss the relevance of such problems and show that the existing sampling-based algorithms cannot be applied directly in this case, since they require an impractical number of samples to yield reasonable solutions. Using a Sample Average Approximation (SAA) approach combined with importanc...

This paper is concerned with solving single CVaR and mixed CVaR minimization problems. A CHKS-type smoothing sample average approximation (SAA) method is proposed for solving these two problems, which retains the convexity and smoothness of the original problem and is easy to implement. For any fixed smoothing constant 2, this method produces a sequence whose cluster points are weak stationary ...

Stochastic variational inequalities (SVI) model a large class of equilibrium problems subject to data uncertainty, and are closely related to stochastic optimization problems. The SVI solution is usually estimated by a solution to a sample average approximation (SAA) problem. This paper considers the normal map formulation of an SVI, and proposes a method to build asymptotically exact confidenc...

In this paper we apply the well known sample average approximation (SAA) method to solve a class of stochastic variational inequality problems (SVIPs). We investigate the existence and convergence of a solution to the sample average approximated SVIP. Under some moderate conditions, we show that the sample average approximated SVIP has a solution with probability one and with probability approa...