To accommodate the fast growing air traffic in a safe and efficient way, an ATM paradigm shift from current Time Based Operations towards Trajectory Based Operations (TBO) is envisioned. TBO entails the exchange, maintenance and use of consistent 4D trajectory information for collaborative decision-making on the flight. This thesis addresses a key research gap in the field of TBO, namely, balancing flight efficiency and network stability in a TBO environment.

Balancing efficiency and stability in the mid-term planning phase is done by a network-wide 4D trajectory planning algorithm which consists of two processes. First, the trajectory interaction detection process based on a grid-based hash table method. Second, the trajectory interaction resolution process. The objective is to resolve the trajectory interactions with minimum network trajectory modification costs (fuel and delay). The decision variables are departure delay and flight level decrease. The optimization problem is solved using a genetic algorithm. The performance of the algorithm is tested on a European case study.