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Related Experiment Video

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways.

Ming Wei^1,2, Bo Sun¹, Wei Wu¹

¹School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China.

Mathematical Biosciences and Engineering : MBE

|October 30, 2020

Summary

This study introduces a multi-objective model for optimizing flight assignments and schedules to minimize delays and maximize runway use. A genetic algorithm efficiently solves this complex problem, improving air traffic management.

Keywords:

multi-objective mixed integer linear programming arrival and departure flight scheduling heuristic algorithm

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Area of Science:

Operations Research
Aviation Management
Computer Science

Background:

Air traffic management faces challenges in optimizing runway assignments and flight schedules.
Minimizing flight delays and maximizing runway utilization are critical objectives.
The complexity of flight scheduling problems (NP-hard) requires advanced optimization techniques.

Purpose of the Study:

To develop a multi-objective mixed integer linear programming (MOMILP) model for flight-to-runway assignment and time determination.
To simultaneously minimize flight delays and maximize runway utilization.
To account for unique runway operational modes and inter-runway interference.

Main Methods:

Formulation of a multi-objective mixed integer linear programming (MOMILP) model.
Development of a heuristic-based non-dominated sorting genetic algorithm II (NSGA-II) to solve the NP-hard problem.
Definition of coding structure and heuristic algorithms for initial population generation in NSGA-II.

Main Results:

The proposed MOMILP model effectively balances flight delays and runway utilization.
The NSGA-II algorithm provides Pareto-optimal solutions for complex scheduling scenarios.
A real-world example validated the model's correctness and demonstrated trends between flight delays and runway idle times.

Conclusions:

The developed MOMILP model and NSGA-II algorithm offer an effective approach to air traffic flow management.
Optimizing runway operations and flight scheduling can significantly improve airport efficiency.
The study provides valuable insights for enhancing air traffic control systems.