Airport congestion is one of the main causes of costly aircraft delays. Sometimes costs may be reduced by imposing on some aircraft a delay at take off time in order to later avoid a more expensive airborne delay. The objective of the Flow Management Problem (F.M.P.) is to find an optimal delay strategy so that the total expected delay cost is minimized. In this paper an idealized and greatly simplified version of F.M.P. is investigated. In particular the airways network considered is star-shaped and congestion is allowed only in the central (arrival) airport. For this particular version a model is presented and a polynomial solution algorithm is derived. Landing priorities among aircraft can also influence the total expected delay cost: the optimal priority rule for our version of the F.M.P. is derived. Another algorithm is presented for the case where the number of aircraft not to be delayed on the ground is given a priori. Possible extensions of the proposed model to more realistic situations are mentioned
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In recent years air traffic has dramatically increased without a corresponding development of airports. Therefore, airports' limited capacity causes air traffic congestion and consequent expensive delays. The only strategy that can be applied in the short term with low investments aims at the optimal management of present resources; its principal device is Ground Holding, which consists of delaying an aircraft take off whenever it is foreseen it will not land in time because of congestion. We consider a traffic situation with “multiple connections” or “banking,” i.e., the situation where some flights are assigned a set of “preceding” flights; no “successive” flight can start until all its preceding flights have landed. The problem consists of distributing delays to flights, so as to minimize the total delay cost, by respecting airport capacity, connections, and time constraints imposed by airlines. We construct an integer linear programming model and we solve it to optimality with CPLEX. Because the computation time is too high (hours) for real-world instances, we propose an alternative heuristic algorithm, which shows a very low computation time (seconds) and acceptable errors when tested on 30 realistic instances with strongly diversified data.
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