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A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem.

Cong Ding1,2, Jun Bi1, Yongxing Wang1

  • 1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Entropy (Basel, Switzerland)
|May 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an imitation learning and genetic algorithm (IL-GA) for airport gate assignment. The IL-GA method improves contact gate allocation efficiency and aligns with airport preferences, outperforming traditional approaches.

Keywords:
deep neural networkgate assignmentgenetic algorithmimitation learning

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Transportation Science

Background:

  • Airport gate assignment is complex due to limited resources and increasing flights.
  • Traditional methods lack real-time decision-making and learning capabilities.
  • Efficient gate assignment is crucial for optimizing ground services and passenger experience.

Purpose of the Study:

  • To propose a novel two-stage hybrid algorithm (IL-GA) for the airport gate assignment problem.
  • To enhance the efficiency of assigning flights to contact gates and maximize gate preferences.
  • To develop a real-time decision-making solution that learns from expert data.

Main Methods:

  • Formulating the gate assignment problem as a Markov decision process (MDP).
  • Utilizing imitation learning (IL) with a deep policy network trained on expert data for offline learning.
  • Employing a genetic algorithm (GA) with an initial population generated by the IL policy network for online optimization.

Main Results:

  • The IL-GA algorithm significantly reduces iterations and improves convergence speed compared to traditional GAs.
  • Achieved a 14.9% higher contact gate allocation rate than manual assignment and 4% higher than traditional GA.
  • The IL-GA allocation scheme demonstrates greater consistency with airport preferences.

Conclusions:

  • The hybrid IL-GA approach offers a powerful and efficient solution for the complex airport gate assignment problem.
  • Imitation learning enhances genetic algorithms by providing informed initial populations, leading to faster and more optimal solutions.
  • This method provides a practical, learning-based approach for real-time gate assignment, improving operational efficiency and airport satisfaction.