MULTI-STAGE STOCHASTIC MODELS FOR PRODUCTION PLANNING OF A FURNITURE MANUFACTURING COMPANY UNDER UNCERTAINTY

In this article, two multi-stage stochastic linear programming models are developed where the uncertainty of the random variable is modeled using a continuous probability distribution or a discrete probability distribution. The developed models are applied to an aggregate production plan for a furniture manufacturing company located in the state of Hidalgo, Mexico, which has important customers such as chain stores with presence throughout the country. Production capacity is defined as the random variable of the model. Uncertainty is modeled through a scenario tree in a multi-stage environment. The main purpose of this research is to determine a feasible solution to the aggregate production plan in a reasonable computational time. The Lingo software was used to find a solution of the model, using the Branch and Bound solver (B-and-B). Furthermore the two developed models were compared in terms of accuracy and computational time. The study is complemented with an extensive sensitivity analysis, where it is assessed the effect of several costs on the optimal solution. Besides, the impact of the service level constraint on the decision variables is analyzed.