Air cargo is a vital component in the worldwide supply chain and economy. In the KLM Ground Handling (GH) terminal, Unit Load Device (ULD) breakdown and shipment pickup are scheduled separately, with limited consideration of storage capacity and truck delays, which results in inefficiencies in schedules and higher storage requirements. To counteract these issues, this study proposes a scheduling model which synchronises ULD breakdown and shipment pickup and determines the effect of a truck slot confirmation system where drivers must confirm their presence some time before their scheduled slot, called the confirmation horizon. A Mixed-Integer Linear Programming (MILP) model extended from cross-docking literature is considered. Moreover, a Genetic Algorithm (GA) is developed to improve computational complexity. It is extended to a rolling horizon (RH) implementation, called the RH-GA, which can react to discovered delays. Using the baseline MILP (b-MILP) a comparison to the current scheduling method is performed. Moreover, experiments on realistic KLM GH scenarios are carried out using the RH-GA. The MILP failed to find feasible solutions within 8 hours for many small/mid-size scenarios. The GA always reached feasibility in tested scenarios and its runtime scaled linearly with scenario size. In the few scenarios which could be benchmarked, the synchronised GA outperformed the b-MILP. Moreover, introducing a non-zero confirmation horizon significantly improved the objective compared to no confirmation horizon. Across tested cases, mean peak inventory decreased by 9% and total tardiness by 2-3% relative to the 0-hour horizon case. Synchronised scheduling with a slot confirmation system can reduce storage requirements and improve on-time performance at the KLM GH terminal, indicating that further development and implementation of both the model and confirmation system may be valuable.

Training in realistic conditions is crucial for fighter pilots. During this training, a red air team is used to represent adversary threats. Currently, the red air team is made up of friendly aircraft that mimic the tactics of the expected adversaries. However, this method has its limitations, such as that these friendly aircraft do not correctly mimic the performance and detectable emissions of the real adversary aircraft. Furthermore, using real combat aircraft has other downsides. They require active fighters and pilots that require expensive training, and using real aircraft means that these expensive combat aircraft need to spend a lot of their service life filling the role of red air instead of flying real missions. As red air flying hours are not considered to be useful training for the pilots flying them, there is no need for using combat-ready aircraft that can carry real armament, nor for a pilot in the cockpit. Using real combat aircraft has other extensive costs attached to it and is unsustainable looking at its real intended purpose. Just to have a real combat aircraft in the red air fleet requires acquisition of the aircraft, taking it away from active service that it was designed for. It needs a (ground)crew to operate it. It also needs lots of maintenance, requiring mechanics, engineers, tools, hardware, and much more. All of this and the combat aircraft is not used for its designed capabilities in flag missions when it is part of the red team. Therefore, there is a desire for a UAV that can match the performance of the real adversaries, is less expensive to operate, and is more sustainable than the current alternatives to fill the role of red air...