Towards Optimal Routing and Scheduling of Metroplex Operations

Capozzi, Brian; Atkins, Stephen; Choi, Seongim

doi:10.2514/6.2009-7037

Cited by 37 publications

(24 citation statements)

References 8 publications

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“…The distance for both W 1 to OAK (route R 4 ) and W 0 to OAK (route R 3 ) is 45 miles; The distance for both W 1 to SJC (route R 2 ) and W 0 to SJC (route R 1 ) is 56 miles. The flight departure schedule is described in Table 1, which is the same as the "phased peak demand" mentioned in Capozzi's paper [7]. Aircraft were assumed to be of the same type.…”

Section: A Test Problemmentioning

confidence: 99%

“…To test the formulation using the NSGA algorithm, a test problem was rebuilt based on Capozzi's papers [7,8]. In this problem, two departure flows are assumed to come from two neighboring airports OAK and SJC, respectively.…”

Section: A Test Problemmentioning

confidence: 99%

“…6). The problem with 20 flights was modelled and solved without multiple windows that were used in MILP formulations [7]. The formulations in Eqn.…”

Section: A Test Problemmentioning

confidence: 99%

“…In 2009, Capozzi et. al [7] introduced a MILP formulation and applied it to coordinate SFO arrivals and SJC departures. The MILP formulation was found suitable for solving small scale problems but it required significant computational time if the number of flights was greater than 10.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Integration of Departures and Arrivals in Terminal Airspace

Xue

Zelinski

2014

Journal of Guidance, Control, and Dynamics

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Coordination of operations with spatially and temporally shared resources, such as route segments, fixes, and runways, improves the efficiency of terminal airspace management. Problems in this category are, in general, computationally difficult compared to conventional scheduling problems. This paper presents a fast time algorithm formulation using a non-dominated sorting genetic algorithm (NSGA). It was first applied to a test problem introduced in existing literature. An experiment with a test problem showed that new methods can solve the 20 aircraft problem in fast time with a 65% or 440 second delay reduction using shared departure fixes. In order to test its application in a more realistic and complicated problem, the NSGA algorithm was applied to a problem in Los Angeles (LAX) terminal airspace, where interactions between 28% of LAX arrivals and 10% of LAX departures are resolved by spatial separation in current operations, which may introduce unnecessary delays. In this work, three types of separations -spatial, temporal, and hybrid separations -were formulated using the new algorithm. The hybrid separation combines both temporal and spatial separations. Results showed that although temporal separation achieved less delay than spatial separation with a small uncertainty buffer, spatial separation outperformed temporal separation when the uncertainty buffer was increased. Hybrid separation introduced much less delay than both spatial and temporal approaches. For a total of

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Section: A Test Problemmentioning

confidence: 99%

Section: A Test Problemmentioning

confidence: 99%

“…6). The problem with 20 flights was modelled and solved without multiple windows that were used in MILP formulations [7]. The formulations in Eqn.…”

Section: A Test Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Integration of Departures and Arrivals in Terminal Airspace

Xue

Zelinski

2014

Journal of Guidance, Control, and Dynamics

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“…These interactions are expected to happen more often when continuous descent approaches are deployed in the future. Recent studies [10][11][12][13] showed that optimized integrated arrivals and/or departures in major airports or metroplex areas have promise for improving operation efficiency. However, the benefits from above optimal schedules are calculated under deterministic scenarios and they are usually sensitive to flight time uncertainties, which can be caused by many sources, such as inaccurate wind prediction, error in aircraft dynamics, or human factors.…”

Section: Introductionmentioning

confidence: 99%

Integrated Arrival- and Departure-Schedule Optimization Under Uncertainty

Xue

Zelinski

2015

Journal of Aircraft

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In terminal airspace, integrating arrivals and departures with shared waypoints provides the potential of improving operational efficiency by allowing direct routes when possible. Incorporating stochastic evaluation as a post-analysis process of deterministic optimization, and imposing a safety buffer in deterministic optimization, are two ways to learn and alleviate the impact of uncertainty and to avoid unexpected outcomes. This work presents a third and direct way to take uncertainty into consideration during the optimization. The impact of uncertainty was incorporated into cost evaluations when searching for the optimal solutions. The controller intervention count was computed using a heuristic model and served as another stochastic cost besides total delay. Costs under uncertainty were evaluated

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Optimal Scheduling Algorithm for Air Traffic Point Merge System Using MILP

Hong

Lee

et al. 2017

Advances in Aerospace Guidance, Navigation and Control

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Towards Optimal Routing and Scheduling of Metroplex Operations

Cited by 37 publications

References 8 publications

Optimal Integration of Departures and Arrivals in Terminal Airspace

Optimal Integration of Departures and Arrivals in Terminal Airspace

Integrated Arrival- and Departure-Schedule Optimization Under Uncertainty

Optimal Scheduling Algorithm for Air Traffic Point Merge System Using MILP

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