The Robust Optimization and Bayesian Updating in Centralized Supply Chain

This paper mainly focuses on demand forecasting uncertainty in supply chain. Given to demand forecasting uncertainty, we introduce robust optimization as well as Bayesian updating to reduce uncertainty. In the context that the quantity of products the supplier offered is fixed, we establish two different models. The one has classic expression and the other has robust expression. In order to acquire a more accurate demand, robust optimization is proposed to modify the probability distribution of the demand. As to the continuous sales period, we combine robust optimization with Bayesian updating to revise the probability distribution of the demand further.