A Study on Optimization Methods Based on Generative Flow Networks for Airport Resource Allocation Problems 


Vol. 14,  No. 8, pp. 633-642, Aug.  2025
https://doi.org/10.3745/TKIPS.2025.14.8.633


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  Abstract

With the continuous growth in global air traffic demand, the operational efficiency of airports has become a critical factor in the competitiveness of the aviation industry. Among various operational challenges, the Gate Assignment Problem (GAP) is a combinatorial optimization problem characterized by complex resource constraints and real-time requirements. Existing approaches have shown limitations in terms of solution diversity and computational efficiency. In this study, GAP is mathematically modeled as a Mixed Integer Linear Programming (MILP) formulation, incorporating practical operational constraints such as time windows, gate capacity, and airline preferences. A novel optimization method based on Generative Flow Networks (GFlowNet) is proposed to learn reward-proportional probability distributions and stochastically generate structurally diverse, high-reward solutions. Using real-world data from Incheon International Airport, the proposed GFlowNet-based approach was compared under the same experimental conditions with an A3C (Asynchronous Advantage Actor-Critic)-based reinforcement learning method. The results showed that the proposed method achieved a 100% gate utilization rate and an average execution time of 3.16 seconds, improving resource utilization by 5.4% and reducing runtime by 41% compared to A3C. Furthermore, it achieved over 95% constraint satisfaction and a diversity score of 1.0, demonstrating its effectiveness in exploring non-redundant, high-quality solutions. This study empirically demonstrates that combining MILP-based mathematical modeling with generative reinforcement learning can effectively contribute to real-time airport operations and intelligent resource allocation.

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[IEEE Style]

K. J. Kyu and A. K. Mo, "A Study on Optimization Methods Based on Generative Flow Networks for Airport Resource Allocation Problems," The Transactions of the Korea Information Processing Society, vol. 14, no. 8, pp. 633-642, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.8.633.

[ACM Style]

Kim Jun Kyu and Ahn Kwang Mo. 2025. A Study on Optimization Methods Based on Generative Flow Networks for Airport Resource Allocation Problems. The Transactions of the Korea Information Processing Society, 14, 8, (2025), 633-642. DOI: https://doi.org/10.3745/TKIPS.2025.14.8.633.

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