The air cargo industry is a challenging environment due to the high competition between the stakeholders involved. This demands, as example, high efficiency from cargo airlines. Efficiency can be ensured by designing loading strategies that fully exploit the available cargo volume. Unknowns in the booking dimensions and flight information make this task challenging. We propose an optimisation and forecasting framework for ULD packing. Goal of the framework is to assist analysts with their decision-making, when assessing whether a new booking should be accepted or declined. Unknown booking dimensions and flight details are forecasted using an ensemble learning method. This information is used as input for a bin packing heuristic, which assesses if the booking fits in the aircraft. In addition, we propose a risk index that quantifies what are the chances the proposed loading configuration (per ULD) is not feasible due to a mismatch between predicted and real booking dimensions.

As more and more people choose the airplane as a means of transportation, the aviation industry faces new challenges in order to fulfill the increasing demand, while decreasing the costs and emissions. In this report, the authors present a design of a narrow-body aircraft that is meant to provide a 30 % direct operating costs reduction, as well as a 20 % reduction in NOx and CO2 emissions while being ready for market introduction by 2030. Furthermore, the aircraft should provide a noise reduction of 10 % which translates to a reduction of 29 [EPNdB] and it should house 177 passengers.