Deep Reinforcement Learning for Multi-Objective Airport Ground Handling

Abstract

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.