Aircraft Flow Management under Congestion

Abstract

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

Disruption management in production planning

“…There have also been models dealing with aircraft reassignment (Jarrah et al [18], Teodorovic and Guberinic [28], and Teodorovic and Stojkovic [29]) and crew recoveries (Arguello, Bard, and Yu [6]) under disruptions, papers specifically studying DM under ground delay programs imposed by the FAA (Andreatta and Romanin-Jacur [5], Luo and Yu [21], and Vranas, Bertsimas, and Odoni [31]), and reviews on the advancement of the DM research in the airline industry (Yu [35] and Yu and Yang [36]). The complexity of the problems forced people to develop special-purpose heuristic algorithms.…”

Estimating One-Parameter Airport Arrival Capacity Distributions for Air Traffic Flow Management

“…Constraint set (1) states that all flights that are predicted to arrive in time period t (demand or D t ) or were delayed on the ground from the previous time period (G t−1 ) should arrive in the current time period (A t ) or be delayed on the ground until a subsequent time period (G t ). Constraint set (2) states that under scenario q, all flights scheduled to arrive in the current time period or that are air delayed from a previous time period (W q,t−1 ) must be air delayed until a subsequent time period or must arrive in the current time period. The inputs into the H-R model are: the number of predicted arrivals or demand for each time period t (D t ), the cost of ground delaying one flight for one time period (c g ), the cost of one period of airborne delay of a single flight (c a ), and Q capacity scenarios (ACDs) with associated probabilities, p q .…”

“…Determining the number of arrival slots or the amount of ground delay to assign to flights is known as the ground holding problem (GHP ). Previous work have been done on the deterministic GHP ( [5], [18]) and the stochastic GHP ( [2], [3], [5], [10], [11], [13], [19], and [20], [21]). For the deterministic GHP, an airport's capacity is generated by the ATCSCC at the beginning of the day and is assumed to remain constant.…”

Optimization Applications in the Airline Industry

“…Andreatta and Romanin-Jacur [5] tried to find the best strategy to make ground holding assignments in the presence of airport congestions under some greatly simplified assumptions. The simplified problem instance being considered is: A group of flights V = {v 1 , ..., v n } are all scheduled to land at the same destination airport at t = 0.…”