Flight Gate Assignment Problem with Reinforcement Learning

Abstract

The operation of an airport is a very complex task involving many actors. The primary mission of airport management is to provide sufficient capacity and the best working conditions to all airlines ground handling and service provider companies. Flight gate assignment is one of the essential planning problems airport management needs to address assigning incoming aircraft to the available gates or stands while satisfying operational constraints. Generally flight arrivals and departures are considered deterministic and various operational research methods have been applied to solve this combinatorial problem. However in real-life scenarios deterministic solutions are generally infeasible because arrival and departure times are uncertain. It is crucial to deal with these uncertainties to create a robust schedule. In this study we develop a Reinforcement Learning (RL) algorithm to solve the flight gate assignment problem since it is a sequential decision-making method and allows adaptive solutions to address urgent and frequent changes. © 2023 Elsevier B.V. All rights reserved.