Optimising airport slot allocation considering flight-scheduling flexibility and total airport capacity constraints

•A Timing Flexibility Indicator Model (TFIM) incorporating airline flight scheduling preferences and multiple objectives is introduced.•Dynamic and asynchronous total airport capacity constraints are modelled.•Valid inequalities are introduced to reduce the required solution times.•Computational results demonstrating the capabilities of the proposed methodology are presented. The lack of airport capacity hinders the growth potential of global air travel, inflicting delays and economic losses to passengers, airlines, and airports. Airport Slot Allocation (ASA) is used in congested airports as a short-term measure for providing access to air-carriers to scarce airport resources. A rich literature has been developed during the last decade around the formulation and optimisation of the ASA problem using the IATA World Scheduling Guidelines (IATA WSG). However, existing models do not address airlines’ flight scheduling flexibility preferences which are expressed through the Timing Flexibility Indicator (TFI). Additionally, in considering the airport's capacity, existing ASA literature does not treat endogenously the allocation of the available airport capacity to match the demand's characteristics. This paper addresses these issues by proposing a novel modelling and solution framework that considers airlines’ flexibility preferences and its seamless integration with constraints that enable the dynamic allocation of the airport's resources. Our approach takes into account the number of rejected requests, the total, and maximum displacement objectives and addresses several primary and additional policy criteria of IATA WSG. The preferences of the airlines are introduced through the Timing Flexibility Indicator (TFI) which is incorporated in a weighted objective function considering the number of slot requests which fall within their specified TFI. The proposed framework benefits from valid tightening inequalities which reduce the required computational times. Our computational study using data from a coordinated airport suggests that the joint consideration of the TFI and the endogenous and dynamic capacity constraints, improves airport capacity utilisation, thus leading to improved airport slot schedules with reduced total and maximum displacement and significant improvements in terms of displaced slot requests and passengers.