Two-Stage Stochastic Mixed-Integer Programming with Chance Constraints for Extended Aircraft Arrival Management

Khassiba, Ahmed; Bastin, Fabian; Cafieri, Sonia; Gendron, Bernard; Mongeau, Marcel

doi:10.1287/trsc.2020.0991

Cited by 23 publications

(20 citation statements)

References 33 publications

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“…The first source of uncertainty in our model is related to the earliest time that a flight would be able to land in the absence of congestion effects or adverse weather conditions at the destination airport. Following previous studies (Bennell et al (2017), Khassiba et al (2020)), we refer to this as the unconstrained landing time and denote it by A i for flight i ∈ F. Noting that A i may depend on many unpredictable factors and control interventions at various different stages of flight i's progress (including the pre-departure stage), we propose to make a distinction between pre-tactical uncertainty and tactical uncertainty and write…”

Section: Unconstrained Landing Timesmentioning

confidence: 99%

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A New Simheuristic Approach for Stochastic Runway Scheduling

Shone,

Glazebrook,

Zografos

2024

Transportation Science

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We consider a stochastic, dynamic runway scheduling problem involving aircraft landings on a single runway. Sequencing decisions are made with knowledge of the estimated arrival times (ETAs) of all aircraft due to arrive at the airport, and these ETAs vary according to continuous-time stochastic processes. Time separations between consecutive runway landings are modeled via sequence-dependent Erlang distributions and are affected by weather conditions, which also evolve continuously over time. The resulting multistage optimization problem is intractable using exact methods, and we propose a novel simheuristic approach based on the application of methods analogous to variable neighborhood search in a high-dimensional stochastic environment. Our model is calibrated using flight tracking data for over 98,000 arrivals at Heathrow Airport. Results from numerical experiments indicate that our proposed simheuristic algorithm outperforms an alternative based on deterministic forecasts under a wide range of parameter values, with the largest benefits seen when the underlying stochastic processes become more volatile and also when the on-time requirements of individual flights are given greater weight in the objective function. Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/M020258/1]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/trsc.2022.0400 .

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Section: Unconstrained Landing Timesmentioning

confidence: 99%

“…Liu et al (2020) used a similar formulation, but allowed for limited or ambiguous information about the model parameters. Khassiba et al (2020) considered the optimization of a sequence of aircraft arriving at an initial approach fix (IAF) and used chance constraints to mitigate the risk of separation time violations.…”

Section: Introductionmentioning

confidence: 99%

A New Simheuristic Approach for Stochastic Runway Scheduling

Shone,

Glazebrook,

Zografos

2024

Transportation Science

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“…For the uncertainty model, there is no restriction in selecting the distribution for the random variable. A natural choice is a Gaussian distribution, which has been widely used [37,38,36,46]. We can also use more complex models such as a mixture of two distributions, where one of the distributions can be used to model the uncertainty for regular flights and the other to model flights with heavy delays.…”

Section: Objective Function For the Probabilistic-based Model Under The Presence Of Uncertaintymentioning

confidence: 99%

A probabilistic model based optimization for aircraft scheduling in terminal area under uncertainty

Huo

Delahaye

Sbihi

2021

Transportation Research Part C: Emerging Technologies

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Air traffic management relies strongly on the accuracy of prediction, while uncertainties such as weather, navigation accuracy, pilot operations, etc. may weaken the performance of predictive tools and cause potential safety issues or reduced capacity. Among all controlled airspace, the Terminal Maneuvering Area (TMA) is one of the most complex areas in which flight safety can be easily affected by unpredictable disturbances. This paper addresses an aircraft scheduling problem under uncertainty with the aim of providing a robust schedule for arrival flights. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models -the deterministic model and a model that incorporates separation buffers -are presented as benchmarks. A meta-heuristic simulated annealing algorithm combined with a time decomposition sliding window is proposed for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented so as to evaluate the optimized solutions obtained from the three models. Statistical comparison among the results shows instability of the model that incorporates the separation buffers, in contrast, the proposed model has absolute advantages in both stability and the conflict absorbing ability when uncertainty arises.

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“…On the one hand, only the required time of arrival (RTA) on the runway is optimized, assuming the aircraft can arrive on time [19], in which an model-based [20] online 4D trajectory prediction or datadriven methods [21,22] can be applied to obtain the estimated time of arrival (ETA). Using more advanced modeling techniques, Khassiba et al [23][24][25] took flight time uncertainty under consideration, formulating a two-stage stochastic programming model to obtain robust landing sequences. On the other hand, arrival trajectories can also be included for scheduling a conflict-free solution [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Delay in the Air or Detour on the Ground?—A Case Study in Guangzhou Baiyun International Airport

Du,

Zhang,

et al. 2023

Aerospace

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Collaboration between terminal airspace and airport surface operation shows an increasing significance for the best efficiency of both parts of the air traffic management domain. Runways play a critical role in connecting the two parts for departure and arrival aircraft. Suppose the gate and the entry fix of an aircraft are predetermined according to the flight plan, and they are on the opposite side of the airport terminal. The aircraft will either spend more time (i.e., delay in the air) landing on a runway close to its gate or take a longer distance (i.e., detour on the ground) taxiing to its gate if a runway close to its entry fix is assigned. This paper proposes a runway assignment model considering terminal airspace operation and airport surface movement simultaneously to discover how runway assignments can affect integrated operations. Four different runway assignment schemes are applied in this model. Subsequently, a metaheuristic method is proposed to solve the model. Furthermore, the historical taxiing and flight time data are analyzed to demonstrate the potential benefits of runway reassignment. Finally, the results show that the free assignment of the runway stands out among the four schemes, not only in the performance of terminal airspace operation (lower flight time) but also in airport surface movement (lower pushback delay, taxi time).

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Two-Stage Stochastic Mixed-Integer Programming with Chance Constraints for Extended Aircraft Arrival Management

Cited by 23 publications

References 33 publications

A New Simheuristic Approach for Stochastic Runway Scheduling

A New Simheuristic Approach for Stochastic Runway Scheduling

A probabilistic model based optimization for aircraft scheduling in terminal area under uncertainty

Delay in the Air or Detour on the Ground?—A Case Study in Guangzhou Baiyun International Airport

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