New Markov Decision Process Formulations and Optimal Policy Structure for Assemble-to-Order and New Product Development Problems

This thesis examines two complex, dynamic problems by employing the theory of Markov De-cision Processes (MDPs). The first two chapters consider assemble-to-order (ATO) inventory systems. An ATO system consists of several components and several products, and assembles products as demand is realized; it is becoming increasingly popular since it provides greater flexibility in manufacturing at a reasonable cost. This work contributes to the ATO research stream by characterizing optimal inventory replenishment and allocation policies. The last chapter examines new product development (NPD) with scarce resources and many projects in parallel, each lasting several periods in the face of uncertainty. This study advances the NPD literature by revealing that optimal project selection and resource allocation decisions are congestion-dependent. Below, we elaborate on the novel optimal policies and structural results we obtain using MDP formulations, which is the overarching theme of each chapter. In the first chapter, we consider generalized ATO “M-systems ” with multiple components and multiple products. These systems involve a single “master ” product which uses multiple units from each component, and multiple individual products each of which consumes multipl