(This paper considers admission control policies in a pure-loss hierarchical perinatal network where there are 2 parallel multi-server (target) hospitals fed by new arriving patients and overflowed patients from a set of parallel multi-server hospitals. In this perinatal network setting, we consider the problem of finding an optimal admission policy that recommends how many beds to reserve in two target hospitals for each arriving stream in order to maximize total revenue in the system. At first, we assumed a Markovian system and model the system as a Markov Decision Process (MDP). By using value iteration algorithm, optimal admission policy is computed. Afterwards, we evaluate various policy scenarios (including MDP optimal policy) with a simulation model which strengthens the decision making process by incorporating the complexity which cannot be captured by MDP and we assess the impact of Markovian assumption in a complex healthcare setting.)