Improving theoretically-optimal and quasi-optimal inventory and transportation policies using adaptive critic based approximate dynamic programming

We demonstrate the possibility of improving on theoretically-optimal fixed policies for control of physical inventory systems in a nonstationary fitness terrain, based on the combined application of evolutionary search and adaptive critic terrain following. We show that adaptive critic based approximate dynamic programming techniques based on plant-controller Jacobians can be used with systems characterized by discrete valued states and controls. Improvements over the best fixed policies (found using either an LP model or a genetic algorithm) in a high-penalty environment, average 83% under conditions both of stationary and nonstationary demand using real world data.