Distributed Collaborative Intelligence for Autonomous Port Operations: A Hierarchical Game-Theoretic Resource Allocation Framework Based on Cloud-Edge Synergy

Abstract In addressing the inherent inefficiencies of centralized scheduling systems in automated container terminals, this study proposes a Cloud-Edge-Fog Distributed Collaborative Intelligence (CEF-DCI) framework integrated with a hierarchical game-theoretic model to optimize resource allocation and coordination among heterogeneous equipment clusters. The framework combines cooperative game theory at the upper layer for global benefit allocation using Shapley values, and non-cooperative game theory at the lower layer for autonomous equipment decision-making under quota constraints. Through high-fidelity digital twin simulations based on Yangshan Port’s 2023 operational data, the model demonstrates significant performance improvements: an 18.7% increase in daily throughput, a 31.7% reduction in AGV idle time, a 22.3% shortening of container handling cycles, and a 28.5% enhancement in equipment utilization, compared to traditional centralized approaches. The study further validates the system’s robustness under throughput fluctuations and equipment failures, highlighting its practical viability for real-world port operations. These findings provide a scalable, efficient, and resilient solution for next-generation intelligent port systems.