A smart predict-then-optimize framework for the Aircraft Arrival Scheduling Problem (ASP) in terminal airspace. This project combines machine learning prediction with optimization techniques to improve arrival sequencing and scheduling of aircraft.
This repository is currently under active development.
The project uses a combination of:
- PyEPO (End-to-End Predict-then-Optimize framework)
- Gurobi optimization solver
- Deep learning models for transit time prediction
- Weather impact scoring system
More documentation and features coming soon!