Supply chain optimization with stochastic parameters by using the sample average approximation method

Abstract

This paper presents a supply chain design problem with stochastic parameters for a large-scale company. The main problem consists to determine the decisions of expansion or contraction of some echelons by considering the variability of the demand. The problem is formulated as a two-stage stochastic model. The first-stage decisions are strategic, while the second-stage decisions are tactical. The model is based on a real-world case from amultinational food company, which suppliesthe Colombian territory and different international markets such as: Venezuela, Ecuador, Chile and some Central American Countries. The solution strategy adopted is known as Sample Average Approximation (SAA). This strategy uses an approximation scheme by sample averages for solving stochastic problems. Computational experiments with different sample sizes are presented. The results show the importance and efficiency of the proposed approach as analternative to the treatment of the variability for large-scale supply chains.