Resilient Robust Model Predictive Control of Inventory Systems for Perishable Good Under Uncertain Forecast Information

We consider the inventory control problem for supply chains with deteriorating items and an uncertain future customer demand which is assumed to fluctuate inside a given compact set. The problem is to define a smart and adaptive replenishment policy keeping the actual inventory as close as possible to a desired (possibly time varying) reference despite uncertainties on the decay factor of stocked goods and unexpected customer demand behaviors violating the bounds of the compact set. We propose a method based on a Resilient Robust Model Predictive Control (RRMPC) approach. This requires dealing with a constrained min-max optimization problem. To dramatically reduce the numerical complexity of the algorithm, the control signal is parametrized using B-spline functions.