Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Tayan Benson, Junior Jessica Buskey Gabriel Camacho Caitlyn Gabrinowitz
Lead Presenter's Name
Tayan Benson
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Mihhail Berezovski
Abstract
Private aviation scheduling is complex and dynamic, requiring frequent aircraft repositioning based on demand and operational constraints, unlike fixed commercial airline schedules. As fleets grow beyond 300 aircraft, traditional deterministic methods become too slow, leading to the use of approaches such as genetic algorithms, but neural network-based methods have not seen in-depth exploration. This project models aircraft scheduling as a network, where airports and flights form a graph. It explores advanced AI methods, including graph neural networks and spatio-temporal graph neural networks (STGNNs), to capture both network structure and time constraints. The goal is to generate efficient daily schedules from given aircraft locations and customer flight requests, providing a foundation for AI-driven optimization in business aviation. These flights form a complex network of aircraft movements connecting a large number of airports, making the dataset well‑suited for network‑based analysis. Graph-based representations allow the relationships between airports and aircraft movements to be modeled in a structured manner, making them suitable for machine learning approaches designed for network data. Methods combine graph construction from short‑horizon operational records, spatial visualization of flight paths, and baseline algorithmic evaluation using a greedy scheduler; preliminary evaluation on the provided records indicates a moderate reduction in required aircraft when using graph‑aware assignment heuristics. The study demonstrates that GNN and STGNN approaches can capture structural and temporal regularities from operational flight records and provides an empirical foundation for further development of data‑driven scheduling and decision‑support tools in business aviation.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
AI-Driven Scheduling Algorithms for Private Aviation
Private aviation scheduling is complex and dynamic, requiring frequent aircraft repositioning based on demand and operational constraints, unlike fixed commercial airline schedules. As fleets grow beyond 300 aircraft, traditional deterministic methods become too slow, leading to the use of approaches such as genetic algorithms, but neural network-based methods have not seen in-depth exploration. This project models aircraft scheduling as a network, where airports and flights form a graph. It explores advanced AI methods, including graph neural networks and spatio-temporal graph neural networks (STGNNs), to capture both network structure and time constraints. The goal is to generate efficient daily schedules from given aircraft locations and customer flight requests, providing a foundation for AI-driven optimization in business aviation. These flights form a complex network of aircraft movements connecting a large number of airports, making the dataset well‑suited for network‑based analysis. Graph-based representations allow the relationships between airports and aircraft movements to be modeled in a structured manner, making them suitable for machine learning approaches designed for network data. Methods combine graph construction from short‑horizon operational records, spatial visualization of flight paths, and baseline algorithmic evaluation using a greedy scheduler; preliminary evaluation on the provided records indicates a moderate reduction in required aircraft when using graph‑aware assignment heuristics. The study demonstrates that GNN and STGNN approaches can capture structural and temporal regularities from operational flight records and provides an empirical foundation for further development of data‑driven scheduling and decision‑support tools in business aviation.