Optimization of Air Traffic Management Strategies at Airports with Uncertainty in Airport Capacity

Gilbo, Eugene

doi:10.1016/s1474-6670(17)43797-9

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…However, their main focus is on increasing the accuracy of the trajectory predictions considering the global perspective for a better optimized ATN. In the existing literature, there are works which estimate the airport and airspace capacity under uncertainty conditions as well as their consideration in flow management, while the framework proposed in [55], [58], [59], [60], [61], [62] are for airport and [63], [64], [65], [66], [67], [68], [28] are for en-route airspace.…”

Section: Consideration Of Uncertaintymentioning

confidence: 99%

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Real-time flight scheduling for optimal air traffic flow management

Guruge¹,

Sandamali²

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The contributions of the co-authors are as follows: • I reviewed the literature, came up with the new model formulation, prepared the manuscript, and performed simulations. • A/Prof. Su Rong provided the initial research direction, guided me through the process, and contributed with suggestions. • Yicheng Zhang participated in the initial brainstorming discussions, revised the manuscript, and provided several technical directions.

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Section: Consideration Of Uncertaintymentioning

confidence: 99%

“…A strategic level stochastic programming model is proposed in [59], by considering the effect of airport capacity uncertainty in the arrival and departure traffic. The uncertainty due to weather conditions is scrutinized and used as a set of scenarios with their associated probabilities.…”

Section: Consideration Of Uncertaintymentioning

confidence: 99%

Real-time flight scheduling for optimal air traffic flow management

Guruge¹,

Sandamali²

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“…Prominent among indirect strategies are control measures such as gate holding policies, metering aircraft through a control point, and vectoring. These strategies reflect control decisions that are typically made at the individual flight level, often just hours in advance of execution, and based on projected traffic flows (Gilbo 1997a).…”

Section: Introductionmentioning

confidence: 99%

Simulation optimization of airline delay with constraints

Hutchison

Hill²

Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304)

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Air traffic delay is a growing and expensive problem. We investigated ways to reduce the cost and magnitude of such delays by trading gate delays against more expensive air delays. Air management and planning at this level can be facilitated by simulation, especially for strategies that alter controls on the system. We used the SIMMOD air traffic simulation to model the system. The objective was to determine a set of control measures that achieve the best system performance subject to restrictions on the decision parameters and selected system output measurements. Because observed system performance is "noisy," the problem is a constrained stochastic optimization problem with nonlinear objective fiinction and nonlinear, stochastic constraints, which requires eficient stochastic optimization methods for its solution. Our approach used the simultaneous perturbation stochastic approximation (SPSA) algorithm with a penalty h c t i o n to handle the dificult constraints.

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“…These strategies reflect control decisions that are typically made at the individual flight level, often just hours in advance of execution, and based on projected flows [11]. This is the level at which we have placed our focus.…”

Section: Introductionmentioning

confidence: 99%

Simulation optimization of airline delay with constraints and multiple objectives

Hutchison

Hill

Fourth International Symposium on Uncertainty Modeling and Analysis, 2003. ISUMA 2003.

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Air traffic delay is a growing and expensive problem. We investigated ways to reduce the cost and magnitude of such delays by means of several control strategies. Air management and planning at this level can be facilitated by simulation, especially for strategies that alter controls on the system. We used the SIMMOD air traffic simulation to model the system. The goal was to determine a set of control measures that achieve the best system performance subject to restrictions on the decision parameters and selected output measurements. Because observed system performance is "noisy," the problem is a constrained stochastic optimization problem with multiple nonlinear objective functions and nonlinear, stochastic constraints, which requires efficient stochastic optimization methods for its solution. Our approach used simultaneous perturbation stochastic approximation (SPSA) with a penalty function to handle the difficult constraints. The results are illustrated in simulation experiments.

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Optimization of Air Traffic Management Strategies at Airports with Uncertainty in Airport Capacity

Cited by 7 publications

References 4 publications

Real-time flight scheduling for optimal air traffic flow management

Real-time flight scheduling for optimal air traffic flow management

Simulation optimization of airline delay with constraints

Simulation optimization of airline delay with constraints and multiple objectives

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