A Robust Approach to Inventory Optimization Under Uncertainty

In this paper we present an application of robust optimization to inventory optimization under uncertainty. We represent uncertainty in a constraint based framework derived from basic economic principles. This approach offers the ability to use information theoretic concepts to quantify the amount of information used in the optimization. The results are shown to correspond to classical models such as EOQ in simple cases. Not only this, the presented model easily incorporates more realistic constraints, which are complicated and are not easily incorporated by the classical models