Rationing is an inventory policy that allows prioritization of different demand classes. In this thesis, we analyze the stock rationing policies for continuous review systems. We clarify some of the ambiguities present in the current literature. Then, we propose a new method for the exact analysis of lot-for-lot inventory systems with backorders under rationing policy. We show that if such an inventory system is sampled at multiples of supply leadtime, the state of the system evolves according to a Markov chain. We provide a recursive procedure to generate the transition probabilities of the embedded Markov chain. It is possible to obtain the steady-state probabilities of interest with desired accuracy by considering a truncated version of the chain. Finally, we propose a dynamic rationing policy, which makes use of the information on the status of the outstanding replenishment orders. We conduct a simulation study to evaluate the performance of the proposed policy.