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.

Mountain ranges all over Earth have long been tourist attractions for their monumental size, beautiful nature, clean air and possible leisure activities. Though attractive, mountain activities may form a threat to human safety. Search and rescue (SAR) teams are constantly stand-by and often have multiple rescue sorties per day. These missions are slow and dangerous for the involved personnel. In extremely rare cases rescue missions use helicopters for search by pilot eyesight. These mission types on average cost e3, 300 per hour, are dangerous for personnel, have a small endurance and have a large downtime. There is a clear need to improve effectiveness and safety of these rescue missions...