Adaptive critic based approximate dynamic programming: A new tool for smart manufacturing

This work supported in part by the National Science Foundation under grant ECS-9904378. Abstract Adaptive critic based approximate dynamic programming techniques are gradient based methods for finding optimal policies for multi-stage decision processes. We believe adaptive critic methods are now developed to the point that they can be applied to the full spectrum of decision and control problems, including inventory control and job scheduling problems of interest in manufacturing. In this paper we illustrate the use one such technique for the development of a policy manager for a distribution system. We compare the performance of both neural net based and fuzzy rule based policy managers with that of LP and GA based fixed policies. Both adaptive critic based soft computing techniques outperform the fixed policies for stationary and nonstationary stochastic demand conditions.