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The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting. To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines. [ABSTRACT FROM AUTHOR]

This paper presents two stochastic programming models for the allocation of time slots over a network of airports. The proposed models address three key issues. First, they provide an optimization tool to allocate time slots, which takes several operational aspects and airline preferences into account; second, they execute the process on a network of airports; and third they explicitly include uncertainty. To the best of our knowledge, these are the first models for time slot allocation to consider both the stochastic nature of capacity reductions and the problem’s network structure. From a practical viewpoint, the proposed models provide important insights for the allocation of time slots. Specifically, they highlight the tradeoff between the schedule/request discrepancies, i.e., the time difference between allocated time slots and airline requests, and operational delays. Increasing schedule/request discrepancies enables a reduction in operational delays. Moreover, the models are computationally viable. A set of realistic test instances that consider the scheduling of four calendar days on different European airport networks has been solved within reasonable – for the application’s context – computation times. In one of our test instances, we were able to reduce the sum of schedule/request discrepancies and operational delays by up to 58%. This work provides slot coordinators with a valuable decision making tool, and it indicates that the proposed approach is very promising and may lead to relevant monetary savings for airlines and aircraft operators. [ABSTRACT FROM AUTHOR]