An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework.

Area of Science:

• Operations Research
• Computer Science
• Aerospace Engineering

Background:

• Airspace congestion and flight delays pose significant challenges in air traffic management.
• Existing optimization models often struggle to address these issues simultaneously and efficiently.

Purpose of the Study:

• To develop a multiobjective optimization framework for the Airway Network Flow Assignment (ANFA) problem.
• To simultaneously reduce airspace congestion and flight delays.

Main Methods:

• Formulation of the ANFA problem as a multiobjective optimization model.
• Application of a multi-island parallel evolution algorithm with nondominated sorting genetic algorithm II.
• Adaptation of a cooperative coevolution algorithm to decompose the problem.
• Design of a dynamic adjustment operator for solution diversity and prematurity avoidance.

Main Results:

• The proposed framework effectively improves solution quality for the ANFA problem.
• Simulation results using real-world data from China's air route network validate the approach.
• The method shows superiority over existing multiobjective optimization algorithms.

Conclusions:

• The developed multiobjective optimization framework offers an effective solution for reducing air traffic congestion and flight delays.
• The novel combination of parallel evolution, cooperative coevolution, and dynamic adjustment operators enhances optimization capabilities.
• This research provides a valuable contribution to efficient air traffic management systems.