A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

Area of Science:

• Operations Research
• Transportation Science
• Aerospace Engineering

Background:

• Network-wide air traffic flow management (ATFM) aims to reduce congestion and delays.
• Conventional ATFM models often assume fixed capacities, which are impractical due to weather uncertainties.
• Convective weather dynamics introduce significant capacity uncertainties in air traffic networks.

Purpose of the Study:

• To investigate the stochastic air traffic network flow optimization (SATNFO) problem.
• To incorporate operational risk from capacity uncertainties into ATFM models.
• To develop a more computationally efficient model for handling dynamic air traffic conditions.

Main Methods:

• Formulated the SATNFO problem as a weighted biobjective 0-1 integer programming model.
• Utilized probabilistic risk assessment to model operational risk due to capacity uncertainties.
• Introduced operational risk as an additional objective within the SATNFO framework.

Main Results:

• The proposed SATNFO model demonstrated significantly fewer constraints compared to traditional stochastic models with nonanticipative constraints.
• Computational experiments confirmed a reduction in computational complexity.
• The model effectively evaluates the impact of capacity uncertainties on air traffic flow management.

Conclusions:

• The developed weighted biobjective 0-1 integer programming model offers a more efficient approach to stochastic air traffic network flow optimization.
• This method provides a practical way to manage air traffic under uncertain conditions caused by weather.
• The reduction in computational complexity makes the model more viable for real-world air traffic control applications.