Application of the Two-Stage Stochastic Programming for Optimizing Chemical Production Planning under Uncertainties

摘要

Most models for chemical production planning are based on deterministic programming approaches without considering uncertainty. This paper presents a two-stage stochastic programming model for chemical production planning optimization with management of purchase and inventory under economic uncertainties including prices of raw materials, product prices and demands, and uses the Monte Carlo sampling method to solve it. The expected profit is maximized taking into account raw materials costs, inventory costs, operating costs and costs of lost demand under economic uncertainties, while the production planning and purchase scheme are optimized simultaneously. The proposed model is validated by a real chemical enterprise based on GIOCIMS (Graphical I/O Chemical Industry Modeling System). The results indicate that the two-stage stochastic programming model can suggest a solution with higher expected profit and lower risk than the one suggested by deterministic programming model.