Robustness of two air traffic scheduling approaches to departure uncertainty

Agogino, Adrian; Rios, Joseph

doi:10.1109/dasc.2011.6095996

Cited by 2 publications

References 16 publications

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Research on Performance Analysis of Air Traffic Flow Management Combined Strategy Impacted by uncertainty

Ying¹,

Xu²,

Guoye³

et al. 2019

Proceedings of the 4th International Conference on Humanities Science, Management and Education Technology (HSMET 2019)

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Since the implementation performance of the composite air traffic flow management strategy is impacted by the departure time uncertainty and/or the estimated time over the enroute point, it is needed to quantitatively analyze the uncertainty impact on the composite strategy. Focused on the initial composite strategy which combines ground delay strategy and miles-in-trail restriction, the practical operation data of Sanya Control Area in the South China Sea is utilized to analyze uncertainty characterization of the departure time and the en-route flying time. Algorithm for dynamically adjusting the initial management strategy is designed and the assessment performance metrics for the composited strategy impacted under uncertainty are advanced and calculated. Then the performance is analyzed with the Monte-Carlo method.

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Research on Performance Analysis of Air Traffic Flow Management Combined Strategy Impacted by uncertainty

Ying¹,

Xu²,

Guoye³

et al. 2019

Proceedings of the 4th International Conference on Humanities Science, Management and Education Technology (HSMET 2019)

View full text Add to dashboard Cite

show abstract

Robust Optimization Model of Airport Group Coordinated Timetable with Uncertain Flight Time

Yan,

2024

Aerospace

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This study develops a robust 0–1 linear optimization programming model for airport group timetable coordination, aiming at assigning each flight at an airport to a unique time slot to avoid conflicts between multiple flights from different airports at the same shared waypoint in an uncertain environment. Flight times between airports and shared waypoints are assumed to have an arbitrary distribution in the interval. Furthermore, some practical constraints, such as the time-varying capacity of each airport, waypoints affected by factors such as weather and traffic control, and maximum delay times for each flight, are considered in this study. The objective is to minimize the total delay time for all flights. The solution is obtained using the RSOME solver. Finally, a real-world case of the Beijing–Tianjin–Hebei airport group, China, is used to optimize the schedules of four airports to prove the accuracy and effectiveness of the method developed in this study. The influence of the budget of uncertainty parameters on model performance is also analyzed.

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Robustness of two air traffic scheduling approaches to departure uncertainty

Cited by 2 publications

References 16 publications

Research on Performance Analysis of Air Traffic Flow Management Combined Strategy Impacted by uncertainty

Research on Performance Analysis of Air Traffic Flow Management Combined Strategy Impacted by uncertainty

Robust Optimization Model of Airport Group Coordinated Timetable with Uncertain Flight Time

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