This project will address inventory control problems with partially observed, non-stationary random demand. A demand process is partially observed when the probability distribution of demand in a given time period is not known with certainty. Instead, it is only partially observed through the previous demand observations which are random variables. The demand process is non-stationary in that the probability distribution of demand may randomly change from one time period to the next, and such a change is not known but is indirectly observed through the random demand observations. The problem is modeled as a composite-state, partially observed Markov decision process. Structural results for optimal control policies will be developed for a wide range of problems that include backordering, lost sales, zero and non-zero fixed order cost, and positive lead times. Because of the computational and memory requirements to compute optimal policies, the project will also develop a range of suboptimal control policies that will have greater potential for use in industry. These policies will be tested on a wide range of problem instances and compared either with an optimal policy or a lower bound. These policies will also be compared with strategies typically used in industry in order to evaluate their potential industrial impact. This problem is becoming more important as the length of product life cycles continues to decrease and as market factors such as competition and product proliferation increase the volatility of individual product demand. In this environment, classical inventory models are ineffective. The value of information and the effective use of information in constructing control policies are important issues in this problem. The results of this project will have a broader impact on related problems in supply chain planning and other problems that have a similar structure of uncertainty such as process control, maintenance, and yield planning. The immediate practical benefit of this project will be the development of inventory control policies that will improve customer service and reduce inventory and production costs in the face of extremely uncertain customer demand.
