Computational experiments with stochastic models for the assembly-to-order system under demand uncertainty We consider an assembly-to-order problem, whereby components must be manufactured under demand uncertainty, and end items are assembled only after the demand is realized, in order to maximize profit. We analyze the impact of problem features such as demand variability and skewness, number of specific versus common components, profit margin, and capacity tightness. In the first part of the work, we use a sampled scenario tree for the demand to build a two-stage stochastic linear programming model, the associated expected value problem, and a model based on linear decision rules.