Identification of flow constraint and control points in departure operations at airport systems

Idris, Husni; Delcaire, Bertrand; Anagnostakis, Ioannis; Hall, Warren A.; Pujet, Nicolas; Féron, Éric; Hansman, R.; Clarke, John‐Paul; Odoni, Amedeo R.

doi:10.2514/6.1998-4291

Cited by 70 publications

(55 citation statements)

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“…For example, the authors in [1] show that take off surface delays, measured as the excess time over the scheduled take-off time, account for over 50% of the National Airspace System delays. Additionally, an analysis using queuing models indicated that the cause of these delays is an imbalance between runway capacity and runway demand [2]. To alleviate this problem, many airports have attempted to expand their capacity by building new runways, taxiways, and gates.…”

Section: Introductionmentioning

confidence: 99%

Runway Scheduling Using Generalized Dynamic Programming

Montoya

Wood

Rathinam

2011

AIAA Guidance, Navigation, and Control Conference

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A generalized dynamic programming method for finding a set of pareto optimal solutions for a runway scheduling problem is introduced. The algorithm generates a set of runway flight sequences that are optimal for both runway throughput and delay. Realistic timebased operational constraints are considered, including miles-in-trail separation, runway crossings, and wake vortex separation. The authors also model divergent runway takeoff operations to allow for reduced wake vortex separation. A modeled Dallas/Fort Worth International airport and three baseline heuristics are used to illustrate preliminary benefits of using the generalized dynamic programming method. Simulated traffic levels ranged from 10 aircraft to 30 aircraft with each test case spanning 15 minutes. The optimal solution shows a 40-70 percent decrease in the expected delay per aircraft over the baseline schedulers. Computational results suggest that the algorithm is promising for real-time application with an average computation time of 4.5 seconds. For even faster computation times, two heuristics are developed. As compared to the optimal, the heuristics are within 5% of the expected delay per aircraft and 1% of the expected number of runway operations per hour and can be 1000x faster.

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Section: Introductionmentioning

confidence: 99%

Runway Scheduling Using Generalized Dynamic Programming

Montoya

Wood

Rathinam

2011

AIAA Guidance, Navigation, and Control Conference

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“…Idris examined the departure system at Boston Logan airport in [14] and [15], concluding that the runway is the primary bottleneck for the departure system. This is also the case at Heathrow.…”

Section: Previous Researchmentioning

confidence: 99%

An examination of take-off scheduling constraints at London Heathrow airport

Atkin

Burke

Greenwood³

et al. 2009

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Abstract:In this paper, we focus upon the departure system for London Heathrow airport, one of the busiest airports in the world. Decreasing the delay for aircraft awaiting take-off with their engines running would decrease fuel usage and have consequent cost and pollution benefits. We explain how the departure system at Heathrow currently works and we describe the various constraints that apply to take-off schedules. A model for the take-off order problem is presented from the point of view of the runway controller, the person who is responsible for the take-off scheduling. We investigate the effects of each constraint and combination of constraints, using a simulation of the Heathrow departure system. The role of the runway controller in the simulation is performed by a search which was designed to form the basis of an online decision support system. Both the simulation and the decision support system are fully described. We use the results to evaluate the effect upon delay that we would expect from various changes that could be made to the departure system. We end the paper by drawing conclusions about the predicted effectiveness of different changes that could be made to the departure system and focus upon a further opportunity for decision support research.

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“…The proposed protocols range from pure gate-holding [3], [4], [5], [6] to explicit control of surface movement [7], [8], [9]. In contrast to aircraft in the departure queue at a runway, aircraft waiting at the gate have their engines turned off.…”

Section: Introductionmentioning

confidence: 99%

Optimal control of airport operations with gate capacity constraints

Khadilkar

Balakrishnan

2013

2013 European Control Conference (ECC)

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Abstract-The mitigation of airport surface congestion is an important step towards increasing the efficiency of the air transportation system, and decreasing flight delays. This paper proposes a strategy to control the release of departing flights from their gates with the specific objective of reducing their taxi times and fuel consumption, while limiting the impact on airport throughput. The proposed strategy also explicitly accounts for the practical constraints that arise due to limited gate resources at the airport. A stochastic network abstraction of the airport surface is used to model aircraft movement, and the optimal release time for each aircraft is calculated using dynamic programming. Simulations of operations at Boston's Logan International Airport in the US are used to illustrate the advantages of the proposed policies.

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Identification of flow constraint and control points in departure operations at airport systems

Cited by 70 publications

References 0 publications

Runway Scheduling Using Generalized Dynamic Programming

Runway Scheduling Using Generalized Dynamic Programming

An examination of take-off scheduling constraints at London Heathrow airport

Optimal control of airport operations with gate capacity constraints

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