The integration of simulation, optimisation, and Bayesian inference for enhancing airport stand allocation

Airport stakeholders are routinely challenged with the allocation of scheduled flights to available stands and parking positions in the most cost-efficient way. At the same time, they need to comply with airline preferences and service contracts and ensure passenger comfort. In the last years, a goal to reduce pollutant emissions added even more complexity to the modern air transport network, which often suffers from congestion problems and related operational disruptions. To address these problems and facilitate airports with related decision support, this research presents a novel approach to the most common airport problem – efficient stand assignment. The proposed concept of a disruption-aware stand allocation tool combines advantages of Bayesian inference, simulation, and evolutionary optimisation and provides qualitative assignment schedules with robustness to a certain level of flight schedule deviations. The presented stand allocation approach coupled with simulation is innovative as it allows 1) to tackle the burden of schedule disruptions on the airport capacity, 2) optimise stand capacity use from multiple management perspectives, 3) helps to release resources that are usually blocked by extensive buffer times between allocated flights, and 4) improves airport environmental footprint. The research presented in this dissertation contributes to the body of literature with the following: • A disruption-aware stand allocation methodology provides decision support to tackle the interests of passengers, airport stakeholders, and the environment in a balanced way. • The presented approach facilitates mitigation of operational variability on airport stand capacity management and its environmental footprint. • The methodology generates solutions that consider historical disruptions and specific emissions characteristics of each aircraft, which provides airport stakeholders with more realistic stand assignment planning. • The developed stand assignment approach is coupled with simulation to provide a qualitative assessment of the generated solutions and consider stochasticity of the real-life system not captured by the assignment-generating framework. The developed approach could be further extended to consider all steps of the aircraft turnaround process. Moreover, the disruption-aware stand assignment approach could be directly incorporated into the airport simulation model to increase robustness and realism of the generated solutions.