Optimal Air Traffic Flow Management Regulations Scheme with Adaptive Large Neighbourhood Search

In the European air transportation network, overloads (i.e., critical imbalances between traffic demand and capacity) are generally resolved by activating air traffic flow management regulations, which delay flights on ground using a – more or less – first-planned, first-served principle. According to a recent research study, some of the regulations that are requested by the flow managers across Europe may not be strictly necessary due to the complex interactions between them. Results using an adaptive tabu search algorithm revealed that some regulations could be safely removed, thereby reducing the delay without causing problems elsewhere in the network. Such an algorithm, however, was designed to suggest which regulations among those requested by the flow managers could be cancelled, not to change the parameters of existing regulations or to propose new ones. This paper addresses this limitation by proposing a simple yet effective optimisation algorithm based on adaptive large neighbourhood search, which is able to determine the best set of regulations minimising air traffic flow management delay from scratch. The potential of the proposed method is assessed for various challenging scenarios using historical traffic data.