A robust optimization approach for airport departure metering under uncertain taxi-out time predictions

Murça, Mayara Condé Rocha

doi:10.1016/j.ast.2017.05.020

Cited by 37 publications

(15 citation statements)

References 14 publications

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“…Accordingly, multi-objective route planning received great attention from some scholars. Several studies constructed a hybrid integral linear programming model [1,[33][34][35][36], robust optimization model [37], real-time route planning targeting at the minimum pollutant emission [38], and pushback control and derated takeoff model [39] by targeting at the minimum taxiing time, minimum fuel consumption, and minimum pollutant emission or minimum delay of aircraft. Chen and Stewart [18] discussed the balance between taxiing time and fuel consumption during the taxiing of aircraft by using the immunity-inspired multi-objective optimization method.…”

Section: A Route Planning For Surface Taxiingmentioning

confidence: 99%

Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

Zhang

Liu

2022

Journal of Aerospace Information Systems

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Section: A Route Planning For Surface Taxiingmentioning

confidence: 99%

Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

Zhang

Liu

2022

Journal of Aerospace Information Systems

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“…The ASRSP belongs to the latter class of trajectory-based approaches (as e.g. [12,13,16,21]), which make use of a directed graph to represent the airport surface network and to build the full trajectory for each airplane, from gate to take-off point (and from landing point to gate). Rather than looking only at the time when a departing airplane leaves its gate, these methods actually "follow" the airplane movement from the start (at the gate) to the take-off point, by selecting the spatial route and the time at which the vehicle should be at any point on its route.…”

Section: Problem Description and Literature Reviewmentioning

confidence: 99%

Scheduling Vehicles with Spatial Conflicts

Kloster

Mannino

Riise

et al. 2022

Transportation Science

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When scheduling the movement of individual vehicles on a traffic network, one must ensure that they never get too close to one another. This is normally modelled by segmenting the network and forbidding two vehicles to occupy the same segment at the same time. This approximation is often insufficient or too restraining. This study develops and systematises the use of conflict regions to model spatial proximity constraints. By extending the classical disjunctive programming approach to job-shop scheduling problems, we demonstrate how conflict regions can be exploited to efficiently schedule the collective movements of a set of vehicles, in this case aircraft moving on an airport ground network. We also show how conflict regions can be used in the short-term control of vehicle speeds to avoid collisions and deadlocks. The overall approach was implemented in a software system for air traffic management at airports and successfully evaluated for scheduling and guiding airplanes during an extensive human in the loop simulation exercise for the Budapest airport. Through simulations, we also provide numerical results to assess the computational efficiency of our scheduling algorithm.

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“…The authors used curated data from previous years, to estimate and calibrate the parameters in their statistical model and used that knowledge to determine optimal manoeuvres. In a more generic uncertainty context, Murça (2017) presented a robust approach for optimizing runway usage and taxi-out time and Radmanesh et al (2018) solved the problem of path planning for unmanned air vehicles under random circumstances. Different sources of uncertainty were explored by Kim et al (2009) who discretized flight speed uncertainty using a white Gaussian function and removing the crosswind effect to assess the efficiency of traffic flow.…”

Section: Aircraft Trajectory Prediction Under Uncertaintymentioning

confidence: 99%

Robust aircraft conflict resolution under trajectory prediction uncertainty

Dias¹,

Rey²

2020

Preprint

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We address the aircraft conflict resolution problem under trajectory prediction uncertainty. We consider that aircraft velocity vectors may be perturbed due to weather effects, such as wind, or measurement errors. Such perturbations may affect aircraft trajectory prediction which plays a key role in ensuring flight safety in air traffic control. Our goal is to solve the aircraft conflict resolution problem in the presence of such perturbations and guarantee that aircraft are separated for any realization of the uncertain data. We propose an uncertainty model wherein aircraft velocities are represented as random variables and the uncertainty set is assumed to be polyhedral. We consider a robust optimization approach and embed the proposed uncertainty model within state-of-the-art mathematical programming formulations for aircraft conflict resolution. We then adopt the approach of Bertsimas and Sim (2004) to formulate the robust counterpart. We use the complex number reformulation and the constraint generation algorithm proposed by Dias et al. (2020a) to solve the resulting nonconvex optimization problem on benchmarking instances of the literature. Our numerical experiments reveal that perturbations of the order of ±5% on aircraft velocities can be accounted for without significantly impacting the objective function compared to the deterministic case. Our tests also show that for greater levels of uncertainty, several instances fail to admit conflict-free solutions, thus highlighting existing risk factors in aircraft conflict resolution. We attempt to explain this behavior by further analyzing pre-and post-optimization aircraft trajectories. Our findings show that most infeasible instances have both a relatively low total aircraft pairwise minimal distance and a high number of conflicts.

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A robust optimization approach for airport departure metering under uncertain taxi-out time predictions

Cited by 37 publications

References 14 publications

Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

Scheduling Vehicles with Spatial Conflicts

Robust aircraft conflict resolution under trajectory prediction uncertainty

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