This Small Business Innovation Research Phase II project will develop neuro-dynamic programming (NDP) methods to address a commercially important complex stochastic control problem--that of supply-chain management. The Phase I research successfully established that NDP algorithms could lead to significant savings over well-accepted heuristics on a class of supply chains that was used as a testbed. This Phase II program will further develop and streamline the NDP methodology. Furthermore, algorithms will be generalized so that they apply to a very broad class of realistic supply chains, including manufacturing and distribution networks. Performance on these new problems will be assessed through extensive experimentation and comparison with current state-of-the-art heuristics. Once the NDP methodology is fully developed, licensing arrangements will be sought to integrate NDP-based optimization modules into the many supply-chain management products currently in widespread use. An improved approach for addressing the logistics of supply-chain management will be of great commercial interest to companies across all industries. Integration of the technology into existing commercial supply-chain management softuare products widely used in manufacturing can enhance the efficiency of numerous U.S. corporations. Furthermore, the general NDP methods developed in this research have a much broader potential scope, in that they can be used to address other complex stochastic control problems that arise in many areas of national importance, including process control, queuing and scheduling, and data network optimization.
