AeroSched is an intelligence-driven operations platform designed for DAA (Dublin Airport Authority) to manage ground operations staffing. It provides a three-tiered approach to resource management: Strategic (Long-Term), Tactical (Short-Term), and Operational (Real-Time/Intraday).
AeroSched transforms passenger forecast data and staff availability into actionable schedules. It helps operations managers predict staffing gaps, optimize rosters, and respond to real-time operational changes.
- 12-Month Forecast: Visualize seasonal trends and annual passenger footfall for 2026.
- Gap Analysis: Automatically identify weeks where staff demand exceeds available capacity.
- Annual Allocation: Detailed breakdown of FTE requirements by role, terminal, and airport pier.
- Scenario Planning: Perform Monte Carlo simulations to stress-test workforce plans against demand surges, punctuality drops, and staff absence rates.
- 3-Day Rolling Window: Manage detailed scheduling for the upcoming 3 days.
- Task Generation: Convert passenger demand profiles into staffing tasks for passenger-processing roles.
- Staff Roster: View assignments, break schedules, and individual utilization rates.
- Demand Timeline: Review passenger-driven staffing demand by touchpoint and time block.
- Real-Time Optimization: Monitor today's operations with current time indicators.
- Disruption Management: Adjust operational assumptions and recalculate task timings and staff assignments.
- Manual Overrides: Reallocate staff onto tasks to cover last-minute gaps.
- Backend: Python / Flask
- Frontend: Vanilla JavaScript (ES6+), modern CSS
- Charting: Chart.js for high-performance data visualization
- Optimization: MIP and CP-SAT workforce allocation workflows
- Simulation: Custom Monte Carlo engine for risk modeling
The application is powered by a set of CSV and Excel files located in the data/ directory:
| File | Description |
|---|---|
forecast_pax_results_2026.csv |
Weekly P10/P50/P90 passenger forecast used for the long-term view. |
historical_pax_data.csv |
Historical weekly passenger footfall used for actuals in the long-term trend chart. |
Staff_schedule.csv |
Daily roster showing which staff members are on duty. |
Staff_absence_schedule.csv |
Tracks approved leave and sickness. |
short term PAX.xlsx |
Short-term passenger demand profile used for tactical and intraday staffing. |
PAX Config.xlsx |
Passenger-handling productivity rates used to convert footfall into staffing demand. |
Note
The platform includes self-healing data automation. On startup, the app automatically updates operational schedule dates to the current day.
- Python 3.8+
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Clone the repository.
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Install dependencies:
pip install -r requirements.txt
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Run the application:
python app.py
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Open your browser and navigate to
http://127.0.0.1:5000.
Staffing requirements are calculated using a calibrated FTE model. For long-term forecasting, the system uses weekly P50_Pax footfall from forecast_pax_results_2026.csv, applies passenger-handling rates from PAX Config.xlsx, and converts those passenger slots into FTEs.
Created for DAA Ground Operations: Operational Excellence through Intelligence.