Methods for Solving of the Aircraft Landing Problem. II. Approximate Solution Methods

Veresnikov, Georgy; Egorov, Nikolay A.; Kulida, Elena; Lebedev, Valentin

doi:10.1134/s0005117919080101

Cited by 14 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraints (3) and (4) restrict the maximum possible advance and delay respectively. Constraints ( 5)- (7) represent the relation between the continuous variables. Constraints (8) express the precedence relationship between aircraft.…”

Section: Complete Formulationmentioning

confidence: 99%

See 1 more Smart Citation

The aircraft runway scheduling problem: A survey

Ikli

Mancel

Mongeau

et al. 2021

Computers & Operations Research

View full text Add to dashboard Cite

The aircraft scheduling problem consists in sequencing aircraft on airport runways and in scheduling their times of operations taking into consideration several operational constraints. It is known to be an NP-hard problem, an ongoing challenge for both researchers and air traffic controllers.The aim of this paper is to present a focused review on the most relevant techniques in the recent literature (since 2010) on the aircraft runway scheduling problem, including exact approaches such as mixed-integer programming and dynamic programming, metaheuristics, and novel approaches based on reinforcement learning. Since the benchmark instances used in the literature are easily solved by high-performance computers and current versions of solvers, we propose a new data set with challenging realistic problems constructed from real-world air traffic.

show abstract

Section: Complete Formulationmentioning

confidence: 99%

“…Bennell et al [2] proposed in 2010 a comprehensive overview of the approaches from the literature. Recently, Veresnikov et al published two surveys on the ALP [6,7]. The first one focuses on some exact approaches to the ALP (mainly mixed-integer programming), and the second one overviews mainly genetic and memetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

The aircraft runway scheduling problem: A survey

Ikli

Mancel

Mongeau

et al. 2021

Computers & Operations Research

View full text Add to dashboard Cite

show abstract

“…During nearly three decades of development, the research on ASSP attracted considerable attention from many researchers. Some research treated the ASSP as a static case [3] and others as a dynamic case [4][5][6][7][8][9][10]; some research tackled the ASSP from a deterministic perspective and others from a stochastic perspective [11][12][13][14]; some research concerned the appeals of one single stakeholder (i.e., single-objective optimization) [15][16][17][18] and others multiple stakeholders (i.e., multiple-objectives optimization) [19][20][21][22][23]; some research solved the ASSP by exact solution methods [3,19,[24][25][26][27][28] (e.g., Beasley used solvers such as CPLEX and Briskorn used model language such as GAMS) and others by approximate solution methods [29], including the simulated annealing algorithm [18,30], genetic algorithm [31,32], ant colony optimization algorithm [33], imperialist competitive algorithm [34], local search algorithm [10], and so on; and some research only provided the optimized landing runway, sequence, and time, while others also proposed the advisories for air traffic controllers [22,[35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method for Arrival Sequencing and Scheduling Problem

Zhang

Kang

2023

Aerospace

View full text Add to dashboard Cite

Decision support tools for arrival sequencing and scheduling could assist air traffic controllers in managing the arrival aircraft in terminal areas. However, one critical issue is that the current method for dealing with the arrival sequencing and scheduling problem does not consider the dynamic traffic situation and the human working experience, which results in a deviation between the scheduled and actual landing sequences. This paper develops a data-driven method to address this issue. Firstly, the random forest model is applied to predict the estimated time of arrival (ETA). During the ETA prediction, the trajectory, operation, and airport-related factors that could increase the prediction accuracy are considered. Secondly, the landing sequence is obtained by sorting the predicted ETAs. Thirdly, two optimization methods are proposed to generate the scheduled time of arrival (STA). The former uses the predicted ETAs as inputs and then directly optimizes the landing sequence and the STA. The latter uses both the predicted ETA and the landing sequence as inputs for further optimization. Finally, these proposed methods are evaluated with three sets of historical data on arrival operations at Changsha Huanghua International Airport (ZGHA). The results show that the RF-based ETA prediction method could improve scheduling performance. Moreover, the proposed optimization methods could provide controllers with a more appropriate decision advisory. Such advisories could simultaneously reduce the operation efficiency indicators (average/maximum delay or dwell time) and the operation complexity indicators (Kendall rank correlation or position shift).

show abstract

“…This problem has been studied in various mathematical formulations since the late 1970s. To date, a variety of formulations have been proposed, representing different sets of requirements of pilots and air traffic controllers (ATC) to the resulting flow (see, for example, [1,2,3,4,5,6,7] and references within).…”

Section: Introductionmentioning

confidence: 99%

Air-traffic controller and autopilot joint control of aircraft as mixed integer quadratic problem

Mikhailov

Kumkov

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

One of the traditional problems in air-traffic management is optimal safe scheduling aircraft at a point of air-routes join. However, this problem implies another one, namely, the problem of guiding the aircraft to the prescribed point of its route at the necessary instant. The latter problem is studied much less attentively. Change of the arrival instant can be provided by alternating the aircraft motion trajectory on the basis of near-airport air-routes scheme or by alternating its velocity. Such a control can be worked out as automatically by the aircraft autopilot as manually by an air-traffic controller. But it can be difficult to an air-traffic controller to construct such a control especially during rush hours in loaded airports. In this paper, the authors suggest an optimizational formalization of the problem of automatic directions generation for such a guiding. The criterion to be minimized in this formalization is the number of controller-pilot interactions, because each such an interaction is a long, difficult process liable to linguistic, technical, psychological, and other errors. Since the criterion and other processes are of discrete kind, the formalization is done in the framework of mixed integer quadratic programming. The obtained problems are solved numerically by means of MIQP-solver provided by the Gurobi library. Each subproblem is solved quite fast within 0.1-0.5 seconds. So, solution of the entire problem (which includes enumeration of trajectories and choice of the optimal velocity regime for each of them) takes one-two seconds. Thus, the problem for the entire group of aircraft can be solved during a time acceptable for a real time application.

show abstract

Methods for Solving of the Aircraft Landing Problem. II. Approximate Solution Methods

Cited by 14 publications

References 12 publications

The aircraft runway scheduling problem: A survey

The aircraft runway scheduling problem: A survey

A Data-Driven Method for Arrival Sequencing and Scheduling Problem

Air-traffic controller and autopilot joint control of aircraft as mixed integer quadratic problem

Contact Info

Product

Resources

About