A new approach to flight planning surfaced on Hacker News that pairs scikit-decide with OpenAP to generate fuel-efficient routes. The method treats trajectory decisions as a Markov Decision Process and solves them with reinforcement learning algorithms.
What It Is / How It Works
Scikit-decide provides a unified interface for planning and reinforcement learning solvers. OpenAP supplies open aircraft performance models that calculate fuel burn, drag, and thrust for specific airframes. The combination lets planners model climb, cruise, and descent phases as sequential decisions that minimize total fuel while respecting altitude and speed constraints.
The workflow converts a flight from origin to destination into a state space of position, altitude, and weight. Solvers then search for the lowest-cost policy under wind and temperature forecasts.
Benchmarks and Reported Results
The original post shows fuel savings of 2-4% on short-haul routes compared with standard great-circle planning. On a 1,200 km flight the optimized profile burned 180 kg less fuel in one documented case. Computation time stayed under 30 seconds on a standard laptop when using the built-in Monte Carlo tree search solver.
How to Try It
Install the packages with:
pip install scikit-decide openap
Load an aircraft model from OpenAP, define origin and destination waypoints, then call the scikit-decide solver with a fuel-cost reward function. Example notebooks appear in the scikit-decide repository under the aviation examples folder.
Pros and Cons
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Pros
- Works with existing flight management system data formats
- Runs locally without cloud API calls
- Supports multiple solvers (A*, MCTS, PPO) through one interface
-
Cons
- Requires accurate wind and temperature grids
- Limited to single-aircraft optimization today
- No built-in handling of air traffic control constraints
Alternatives and Comparisons
Traditional tools such as Jeppesen and Lido rely on deterministic cost-index tables. The scikit-decide + OpenAP pipeline adds stochastic wind modeling and can re-optimize mid-flight.
| Feature | scikit-decide + OpenAP | Jeppesen | Lido FMS |
|---|---|---|---|
| Fuel modeling | Physics-based | Table | Table |
| Replanning speed | <30 s | Minutes | Minutes |
| Wind uncertainty | Yes | Limited | Limited |
| License | Open source | Commercial | Commercial |
Who Should Use This
Airlines running their own flight planning systems gain the most. Researchers testing new trajectory algorithms can prototype quickly. Operators bound by strict ATC slot times or heavy traffic flows should continue with certified commercial tools until the open-source stack adds those constraints.
Bottom Line / Verdict
The scikit-decide and OpenAP pairing delivers measurable fuel reductions on representative routes while remaining fully local and open source. Early adopters in research and smaller carriers can integrate it today; larger fleets will wait for ATC and multi-aircraft extensions.
The same optimization pattern applies to drone delivery and urban air mobility once performance models for those vehicles mature.
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