Modeling and optimizing the delay propagation in Chinese aviation networks

Area of Science:

• Aviation Operations Research
• Complex Systems Modeling
• Transportation Science

Background:

• Aviation network delays significantly disrupt airline operations and passenger travel.
• Understanding and mitigating delay propagation is crucial for system efficiency.

Purpose of the Study:

• To develop metrics and a data-driven model for quantifying and simulating aviation delay propagation.
• To identify key factors influencing delay propagation and their temporal characteristics.
• To create an improved genetic algorithm (GA) for flight rescheduling to minimize delays.

Main Methods:

• Defined metrics to quantify overall delay levels.
• Proposed an agent-based, data-driven model incorporating aircraft rotation, flight connectivity, scheduling, and disturbance.
• Developed an improved genetic algorithm (GA) for flight rescheduling.
• Analyzed delay impact and temporal characteristics at individual airports.

Main Results:

• Aircraft rotation was identified as the most significant internal factor in delay propagation.
• A priority-based strategy proved more effective than First-Come-First-Serve in minimizing delays during congestion.
• The GA-rescheduled flights demonstrated a more substantial reduction in delay propagation than the agent-based model.

Conclusions:

• Aircraft rotation is a critical element in aviation delay propagation.
• Priority-based scheduling strategies are superior to FIFO for managing congestion-induced delays.
• The developed GA offers a more effective approach to flight rescheduling for delay mitigation.