Integrated Arrival- and Departure-Schedule Optimization Under Uncertainty

Xue, Min; Zelinski, Shannon

doi:10.2514/1.c032957

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…Including uncertainty in future work will be important and necessary in order to handle departure time and weather impacts precisely. Since GA-type optimization can be parallelized and combined with Graphics Processing Unites (GPU) implementation [8], the proposed approach will enable the introduction of uncertainties in future research, where uncertainties can be injected to route options in terms of their corresponding look-ahead times.…”

Section: Discussionmentioning

confidence: 99%

Route optimization for offloading congested meter fixes

Xue

Zelinski

2016

2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

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The Optimized Route Capability (ORC) concept proposed by the FAA facilitates traffic managers to identify and resolve arrival flight delays caused by bottlenecks formed at arrival meter fixes when there exists imbalance between arrival fixes and runways. ORC makes use of the prediction capability of existing automation tools, monitors the traffic delays based on these predictions, and searches the best reroutes upstream of the meter fixes based on the predictions and estimated arrival schedules when delays are over a predefined threshold. Initial implementation and evaluation of the ORC concept considered only reroutes available at the time arrival congestion was first predicted. This work extends previous work by introducing an additional dimension in reroute options such that ORC can find the best time to reroute and overcome the 'firstcome-first-reroute' phenomenon. To deal with the enlarged reroute solution space, a genetic algorithm was developed to solve this problem. Experiments were conducted using the same traffic scenario used in previous work, when an arrival rush was created for one of the four arrival meter fixes at George Bush Intercontinental Houston Airport. Results showed the new approach further improved delay savings. The suggested route changes from the new approach were on average 30 minutes later than those using other approaches, and fewer numbers of reroutes were required. Fewer numbers of reroutes reduce operational complexity and later reroutes help decision makers deal with uncertain situations.

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Section: Discussionmentioning

confidence: 99%

Route optimization for offloading congested meter fixes

Xue

Zelinski

2016

2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

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“…The details of the parameterization can be found in Hartjes and Visser [19]. Two noise-exposed regions of 66 km × 59 km and 36.5 km × 20 km with a population grid cell size of 500 m × 500 m [33] are used for the SPIJKERBOOR2K and ARNEM2N SIDs, respectively. A Boeing 737-800 with two engines is used as the aircraft model, based on the Base of Aircraft Data (BADA), with an initial mass of 68 tons (85% of the maximum take-off weight) as a representative take-off mass.…”

Section: Numerical Examplementioning

confidence: 99%

An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

et al. 2017

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Abstract:In an effort to allow to increase the number of aircraft and airport operations while mitigating their negative impacts (e.g., noise and pollutant emission) on near-airport communities, the optimal design of new departure routes with less noise and fuel consumption becomes more important. In this paper, a multi-objective evolutionary algorithm based on decomposition (MOEA/D), which recently emerged as a potential method for solving multi-objective optimization problems (MOPs), is developed for this kind of problem. First, to minimize aircraft noise for departure routes while taking into account the interests of various stakeholders, bi-objective optimization problems involving noise and fuel consumption are formulated where both the ground track and vertical profile of a departure route are optimized simultaneously. Second, in order to make the design space of vertical profiles feasible during the optimization process, a trajectory parameterization technique recently proposed is employed. Furthermore, some modifications to MOEA/D that are aimed at significantly reducing the computational cost are also introduced. Two different examples of departure routes at Schiphol Airport in the Netherlands are shown to demonstrate the applicability and reliability of the proposed method. The simulation results reveal that the proposed method is an effective and efficient approach for solving this kind of problem.

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“…The stochastic scheduler combines NSGA and Monte Carlo simulation. 13,16,17,28 The decision variables including speeds, routes, delays, and runway assignment are coded as "genes", and each solution with a set of decision variables is marked as an "individual". In NSGA, a population with hundreds of "individuals" evolves at each generation in terms of their costs through operations of "crossover", "mutation", "ranking", and "selection".…”

Section: G Algorithms and Implementationmentioning

confidence: 99%

“…In a Linux platform with 18x2.5 GHz Xeon, 32 GB memory, and two GeForce GTX690 GPUs, a 15-flight scenario scheduling problem takes around 30 seconds to be solved . 28,29…”

Section: G Algorithms and Implementationmentioning

confidence: 99%

A Stochastic Scheduler for Integrated Arrival, Departure, and Surface Operations in Los Angeles

Xue

Zelinski

2015

15th AIAA Aviation Technology, Integration, and Operations Conference

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In terminal airspace, integrating arrivals, departures, and surface operations with competing resources provides the potential of improving operational efficiency by removing barriers between different operations. This work develops a centralized stochastic scheduler for operations in a terminal area including airborne and surface operations using a non-dominated sorting genetic algorithm and Monte Carlo simulations. The scheduler handles competing resources between different flows, such as runway allocations, runway crossings, merges at departure fixes, and other interaction waypoints between arrivals and departures. The scheduler takes time-varied uncertainties into account in optimization as well. The scheduler is run sequentially to identify the best robust schedule for the next planning window. Resulting schedules determine routes, speeds or delays, and runway assignments subject to separation constraints at merging/diverging waypoints in the air and at runways (including runway crossings) on the surface. The Los Angeles terminal area was used as an example in experiments with a four-hour traffic scenario. The results showed that using stochastic schedulers can reduce flight time delay (airborne and ground) anywhere from 28% to 40% statistically compared to deterministic schedulers. Sensitivity studies on various planning horizons presented that trade-offs exist between planning horizons and achievable minimum delays. A twenty-minute planning horizon was found to be a bad choice because uncertainties increased with the look-ahead time. Eight minutes was promising for planning as it achieved the lowest delay compared to others. However, the results demonstrated that any duration from two minutes to eight minutes could be a good candidate as well. The results on runway usage showed that using the stochastic scheduler, runway makespans and occupancy were usually slightly lower than applying deterministic schedulers.

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Integrated Arrival- and Departure-Schedule Optimization Under Uncertainty

Cited by 12 publications

References 24 publications

Route optimization for offloading congested meter fixes

Route optimization for offloading congested meter fixes

An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

A Stochastic Scheduler for Integrated Arrival, Departure, and Surface Operations in Los Angeles

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