Airport surface congestion results in significant increases in taxi times, fuel burn and emissions at major airports. This thesis proposes a control strategy to improve the performance of airport operations at Boston Logan International Airport. First of all, an Eulerian model of the airport is built in order to be used for the purpose of control. This model can be used with different control strategies, however, in this project a strategy based in Model Predictive Control is chosen, which allows to include some feedback in the decision algorithms. This approach determines which are the optimal moments to release aircraft from the gates (pushback) in order to prevent the airport surface from entering congested states and to reduce the time that flights spend with engines on while taxiing to the runway. Finally, simulations results are shown in order to compare the efficiency of the proposed approach versus the current strategy used in ATC, the well-known First Come, First Served policy. The results demonstrate that lots of benefits are obtained through the proposed method in situations with high demand.