This Small Business Innovation Research (SBIR) project develops techniques for solving complex stochastic control problems using neuro-dynamic programming (NDP). Although stochastic control problems are critical in many applications, satisfactory solutions have not been found because of their great complexity. However, recent advances in NDP may enable solutions to stochastic control problems that were once dismissed as intractable. Phase I will develop a methodological framework for the application of NDP and apply the technology to a complex real-world stochastic control problem of commercial interest, namely, supply chain management. This approach exploits all information that is critical for optimal control and is expected to lead to a realistic modeling of a generic supply-chain. It is expected to surpass existing strategies for stochastic control, and a rigorous comparison will be made to establish NDP as an important and practical technology. After Phase I a user-friendly commercial software product can be developed to enable the widespread application of neuro-dynamic programming. Furthermore, strategies developed from the case study in supply-chain management would be integrated into existing commercial supply-chain management software and marketed to manufacturing companies.
