Quasi-stochastic optimization model for time-based arrival scheduling considering Standard Terminal Arrival (STAR) track time and a new delay-conflict relationship

Chandra, Aitichya; Choubey, Nipun; Verma, Ashish; Sooraj, K.P.

doi:10.1016/j.jairtraman.2023.102527

Journal of Air Transport Management

2024

DOI: 10.1016/j.jairtraman.2023.102527

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Quasi-stochastic optimization model for time-based arrival scheduling considering Standard Terminal Arrival (STAR) track time and a new delay-conflict relationship

Aitichya Chandra,

Nipun Choubey,

Ashish Verma

et al.

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Cited by 3 publications

References 44 publications

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To delay or not to delay? A hybrid relationship between departure delay, en-route conflict probability, and number of conflicts

Chandra,

Verma

2025

Journal of the Air Transport Research Society

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To delay or not to delay? A hybrid relationship between departure delay, en-route conflict probability, and number of conflicts

Chandra,

Verma

2025

Journal of the Air Transport Research Society

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On the Possibilities of Efficient Air Traffic Monitoring through Complex Network Clustering Based Airspace Sub-Sectorization: A Multi-Objective Discrete Particle Swarm Optimization Approach

Chandra,

Hazra,

Verma

et al. 2024

Transportation Research Record: Journal of the Transportation R

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This study models the airspace sub-sectorization problem as a multi-objective complex network clustering problem. A decomposition-based discrete particle swarm optimization (DPSO) algorithm is then used to solve the problem, followed by applying the minimum bounding geometry method to design convex and compact boundaries. An Indian airspace sector was considered to validate the proposed framework. The waypoints and routes within the sector were represented as a network graph, and discretized traffic loads were randomly allotted to the vertices to guide the DPSO. The maximum number of generations or iterations was set as the termination criteria. The proposed approach generates clusters that result in all sub-sectors having a medium traffic load, ensuring equity that is difficult to achieve. This framework offers enough flexibility to avoid several strict constraints, thereby reducing the problem’s complexity. Moreover, the proposed framework improves the adaptability of sub-sectors to network evolution and traffic conditions, recognizing the hierarchical characteristics of air transport networks. The present research also motivates several research opportunities and possibilities for future air traffic management systems.

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Flight Arrival Scheduling via Large Language Model

Zhou,

Wang,

Zhu

et al. 2024

Aerospace

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The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabilities and generalization potential of large language models (LLMs). Our method conceptualizes the dynamic scheduling problem for training flight as a language modeling problem, a perspective not previously explored. Specifically, we represent the allocator’s inputs and outputs as language tokens, utilizing LLMs to generate conflict-free results based on a language description of requested landing information and assigned training flight information. Additionally, we employ a reset strategy to create a small dataset for scenario-specific samples, enabling LLMs to quickly learn allocation schemes from the dataset. We demonstrated the capability of LLMs in addressing time conflicts by evaluating metrics such as answer accuracy, conflict rate, and total delay time (without the wrong answer). These findings underscore the feasibility of employing LLMs in the field of air traffic control.

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Quasi-stochastic optimization model for time-based arrival scheduling considering Standard Terminal Arrival (STAR) track time and a new delay-conflict relationship

Cited by 3 publications

References 44 publications

To delay or not to delay? A hybrid relationship between departure delay, en-route conflict probability, and number of conflicts

To delay or not to delay? A hybrid relationship between departure delay, en-route conflict probability, and number of conflicts

On the Possibilities of Efficient Air Traffic Monitoring through Complex Network Clustering Based Airspace Sub-Sectorization: A Multi-Objective Discrete Particle Swarm Optimization Approach

Flight Arrival Scheduling via Large Language Model

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