In this thesis, by being motivated from the challenges in perinatal networks, we address design, evaluation and flow control of a stochastic healthcare network where there exist multiple levels of hospitals and different types of patients. Patients are supposed urgent; thus they can be rejected and overflow to another facility in the same network if no service capacity is available at their arrival. Rejection of patients due to the lack of service capacity is the common phenomenon in overflow networks. We approach the problem from both strategic and operational perspectives. In strategic part, we address a location & capacity planning problem for adjusting the network to better meet demographic changes. In operational part, we study the optimal patient admission control policies to increase flexibility in allocation of resources and improve the control of patient flow in the network. Finally, in order to evaluate the performance of the network, we develop new approximation methodologies that estimate the rejection probabilities in each hospital for each arriving patient group, thus the overflow probabilities among hospitals. Furthermore, an agent-based discrete-event simulation model is constructed to adequately represent our main applicationarea: Nord Hauts-de-Seine Perinatal Network. The simulation model is used to evaluate the performance of the complex network and more importantly evaluate the strength of the optimal results of our analytical models. The developed methodologies in this thesis are combined in a decision support tool, foreseen under the project “COVER”, which aims to assist health system managers to effectively plan strategic and operational decisions of a healthcare network and evaluate the performance of their decisions.