What question did this study set out to answer?

The research aims to develop a framework for efficient resource allocation in transportation hubs using deep reinforcement learning.

January 25, 2026Open Access

Cross-Domain Deep Reinforcement Learning for Real-Time Resource Allocation in Transportation Hubs: From Airport Gates to Seaport Berths

Key Points

The research aims to develop a framework for efficient resource allocation in transportation hubs using deep reinforcement learning.
Developed a domain-adaptive deep reinforcement learning framework for dynamic resource allocation.
Implemented dual-level heterogeneous graph attention networks to separate constraint topology from domain-specific features.
Used hypergraph-based constraint modeling to capture high-order dependencies.
Employed hierarchical policy decomposition to reduce computational complexity.
Achieved 87.3% resource utilization in airport operations and 86.3% in port operations.
Outperformed commercial solvers, operating 270 times faster (1.1 s vs. 298 s per instance).
Maintained 92.4% performance when policies trained on airports were applied to ports.
Scaled to problems three times larger than training instances with only 7% performance degradation.

Abstract

Efficient resource allocation is critical for transportation hub operations, yet current scheduling systems require substantial domain-specific customization when deployed across different facilities. This paper presents a domain-adaptive deep reinforcement learning (DADRL) framework that learns transferable optimization policies for dynamic resource allocation across structurally similar transportation scheduling problems. The framework integrates dual-level heterogeneous graph attention networks for separating constraint topology from domain-specific features, hypergraph-based constraint modeling for capturing high-order dependencies, and hierarchical policy decomposition that reduces computational complexity from O(mnT) to O(m+n+T). Evaluated on realistic simulators modeling airport gate assignment (Singapore Changi: 50 gates, 300–400 daily flights) and seaport berth allocation (Singapore Port: 40 berths, 80–120 daily vessels), DADRL achieves 87.3% resource utilization in airport operations and 86.3% in port operations, outperforming commercial solvers under strict real-time constraints (Gurobi-MIP with 300 s time limit: 85.1%) while operating 270 times faster (1.1 s versus 298 s per instance). Given unlimited time, Gurobi achieves provably optimal solutions, but DADRL reaches 98.7% of this optimum in 1.1 s, making it suitable for time-critical operational scenarios where exact solvers are computationally infeasible. Critically, policies trained exclusively on airport scenarios retain 92.4% performance when applied to ports without retraining, requiring only 800 adaptation steps compared to 13,200 for domain-specific training. The framework maintains 86.2% performance under operational disruptions and scales to problems three times larger than training instances with only 7% degradation. These results demonstrate that learned optimization principles can generalize across transportation scheduling problems sharing common constraint structures, enabling rapid deployment of AI-based scheduling systems across multi-modal transportation networks with minimal customization and reduced implementation costs.

Cross-Domain Deep Reinforcement Learning for Real-Time Resource Allocation in Transportation Hubs: From Airport Gates to Seaport Berths

Key Points

Abstract

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