An inter-airline ATFM slot trading mechanism for delay cost reduction

When unexpected temporary reductions in airspace capacity occur, flights may need to be rescheduled, often resulting in delays and associated costs. Reallocating limited airspace resources in a way that equitably mitigates delay costs across airlines, while directly involving them in the decision-making process, remains a significant operational challenge. Although several approaches have been proposed in the literature, the complexity of tactical air traffic flow management and the diverse requirements of stakeholders have so far limited their practical applicability. In this work, we introduce a novel inter-airline slot trading framework that provides a fair mechanism aligned with airlines’ operational needs, while remaining sufficiently simple for real-world deployment. The framework is based on an integer programming formulation, and we show that the resulting trade selection problem can be expressed as a Maximum Weight Independent Set (MWIS) and solved efficiently. To cope with the growing computational burden associated with large-scale instances, we develop a three-step machine-learning-based pre-processing heuristic. This heuristic uses neural networks to discard flight combinations unlikely to generate beneficial trades and to estimate the value of the remaining ones, thereby restricting the search space to the most promising candidates. Computational experiments demonstrate that the proposed framework effectively reduces total delay costs while ensuring an equitable distribution of benefits among airlines. Moreover, the neural-network-based pre-processing component enables the efficient solution of instances that would otherwise be computationally intractable, substantially enhancing the practical applicability of the approach.