The first part of the thesis extends the theory of supply chain flexibility by considering placement of vertical flexibility across multiple stages in a supply chain. We consider two types of flexibility---logistics flexibility and process flexibility---and examine how demand, production and supply variability at a single stage impacts the best stage in the supply chain for each type of flexibility. The second part of the thesis studies decisions involved in managing the vehicle life cycle: product portfolio planning, plant assignment and production allocation. These decisions are typically decoupled in practice due to complexity. But they clearly impact one another. We present a Markov Decision Process model to assess the value of integrating portfolio planning and plant assignment. Our analytical and numerical results suggest that decoupling these decisions can lead to substantial loss of profit, particularly when plant capacity utilization is moderate and flexible tooling makes it significantly cheaper to introduce new models into plants. The third part of the thesis proposes an empirical stochastic branch-and-bound algorithm for discrete optimization via simulation. This algorithm keeps the partition structure of stochastic branch-and-bound, while estimating bounds based on sampling. It also computes empirical bounds to allocate sampling effort. The algorithm is globally convergent. We compare the performance of our algorithm with the nested partitions method via numerical experiments.
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