An Edge-Computing Empowered Multi-Agent Reinforcement Learning Framework for Decarbonized Cross-Border Logistics and Fleet Management

Abstract

Integrating regional supply chains while minimizing environmental footprints presents unprecedented operational challenges, particularly when scaling the highly volatile operations of micro and small logistics enterprises across borders. Existing centralized optimization frameworks often struggle with non-linear latency and scalability constraints, revealing inherent limitations when adapting to real-time, stochastic fleet rebalancing demands under fluctuating multi-national regulatory environments. To address these systemic bottlenecks, this paper proposes an edge-computing empowered multi-agent reinforcement learning framework. During our iterative modeling phase, significant algorithmic discrepancies in cross-border communication stability forced a crucial design adjustment toward decentralized, asynchronous network topologies to prevent localized node failures from cascading through the infrastructure. Computational simulations suggest that distributing heavy inference tasks to edge devices may, to some extent, alleviate core processing bottlenecks and reduce fuel consumption anomalies, though potential data-logging biases across heterogeneous edge architectures necessitate a cautious interpretation of absolute decarbonization metrics. Considering the above factors, this leads us to further thinking regarding how decentralized algorithmic governance might ultimately reshape global supply chain paradigms, though further empirical research is explicitly needed to stress-test the framework under extreme multi-core task disruptions.