Scheduling Aircraft Take-Offs and Landings on Interdependent and Heterogeneous Runways

Lieder, Alexander; Stolletz, Raik

doi:10.2139/ssrn.2657577

Cited by 8 publications

(15 citation statements)

References 38 publications

(42 reference statements)

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“…In the latter case, several configuration (i.e., layout or design) are possible, such as the parallel (two or more) runways, whose centerlines are parallel. A comprehensive list of articles regrouped by the number of runways and their configuration is reported in [26].…”

Section: Mathematical Modelsmentioning

confidence: 99%

The aircraft runway scheduling problem: A survey

Ikli

Mancel

Mongeau

et al. 2021

Computers & Operations Research

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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.

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Section: Mathematical Modelsmentioning

confidence: 99%

The aircraft runway scheduling problem: A survey

Ikli

Mancel

Mongeau

et al. 2021

Computers & Operations Research

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“…Some studies have also supported the uncertainty and fuzzy states, incorporating the latter into other techniques (39)(40)(41). Such solutions have been proposed for AT problems and are based on the Internet of Things (IoT) (42)(43)(44), optimization methods (45)(46)(47)(48), multi-objective optimization techniques (49)(50)(51), and intelligent agent-based methods (52).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

Aghdam

Tabbakh

Chabok

et al. 2020

Preprint

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Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.

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“…As for the aviation industry, extant research has focused on route scheduling (Murca and Muller, 2015;Lieder and Stolletz, 2016) and aircraft selection (Bazargan and Hartman, 2012;Sama et al, 2013Sama et al, , 2014. Bazargan and Hartman (2012) proposed an aircraft replacement strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Vancroonenburg et al (2014) proposed an MILP model for air cargo selection and weight balancing. Lieder and Stolletz (2016) proposed a dynamic programming approach to deal with aircraft take-off and landing to achieve the optimal solutions in realistic problem instances. Mirkovic et al (2016) developed a new approach to deal with resource allocation in airports.…”

Section: Introductionmentioning

confidence: 99%

A genetic algorithm for optimizing space utilization in aircraft hangar shop

Wang

Chan

et al. 2019

Int Trans Operational Res

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This study considers the aircraft placement problem in aircraft hangar shops (AHS) encountered by aircraft service companies. AHSs usually have irregular shapes, and aircraft, too, have special shapes. Moreover, frequent operations involving moving aircraft in and out are complicated. For these reasons, aircraft placement is difficult. The present study deals with operations management in AHS to optimize space utilization by placing a greater number of aircraft, which would greatly benefit aircraft services companies. Herein, a novel genetic algorithm (GA) based approach is applied to optimize space utilization. To exactly express the problem, practical and operational principles, including both in AHS and in outdoor areas, are abstracted based on interviews with the staff of an aircraft service company. Then, the placement space is modeled in an x–y coordinate system. In addition, a two‐dimensional geometry model for aircraft, consisting of seven parameters, is developed. Based on these works, a novel GA for solving the aircraft placement problem is developed. Finally, a practical instance with eight aircraft serviced by a company is tested. All eight aircraft are placed well by using the proposed approach. Compared to the previous scenario, where at most seven aircraft could be placed well, the proposed approach will greatly benefit aircraft service companies.

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Scheduling Aircraft Take-Offs and Landings on Interdependent and Heterogeneous Runways

Cited by 8 publications

References 38 publications

The aircraft runway scheduling problem: A survey

The aircraft runway scheduling problem: A survey

Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

A genetic algorithm for optimizing space utilization in aircraft hangar shop

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