Air transport has enormous impact on economic, social, and environmental factors worldwide. According to the International Air Transport Association significant year on year increases can be noticed recently, in both passenger and cargo traffic. However, with this increasing demand for air transport, airports are faced with a rising trend in congestion, delays, and other problematic inefficiencies. Although increasing demand is a factor in these challenges, airline or airport causes, such as ground handling, remain the second largest cause of delays. This highlights the need for efficient and optimal scheduling of ground handling processes to minimise delays, and thereby the negative impact this might have on the airlines or airports. Most existing studies address only simplified sub-problems of Airport Ground Handling (AGH), by relaxing constraints or by leaving them out, rather than tackling the complete problem. Furthermore, these approaches tend to focus on single objective optimisation of AGH, while in practice there can be many (conflicting) objectives that need to be optimised simultaneously. This research explores the possibility of extending a neural model, which is trained to optimise instances of AGH on a single objective, with a generic learning-based approach that approximates the Pareto set for multi-objective optimisation, and applying further hypothetical improvements to this combined model. The implemented models are compared against each other, heuristic approaches for multi-objective combinatorial optimisation, and against the original single-objective neural model.

During this project, we have explored the possibilities for an algorithm that can schedule multiple visits taking into consideration constraints and KPIs. The solution we came up with has been integrated into Dropboard and is ready to be used in production by Dropboard’s clients. Our algorithm takes as input the visits to be scheduled. It then sorts these visits based on heuristics and schedules each visit in this order on its best location, using the single visit/ single resource allocation algorithm that was already present in Dropboard. Multiple plans are generated and post schedule optimised to possibly improve their rating. These plans are shown to the user with their rating in each KPI, and the user can select them to view them on the chart. Finally, a user is able to select a plan and persist it into the operational plan.