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

We demonstrate the possibility of optimal 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 Jacobeans can be used with systems characterized by discrete valued states and controls. Improvements upon a quasi-optimal policy found using a genetic algorithm in a high-penalty environment, average 66% under conditions both of stationary and non-stationary demand.