MetaGradient driven strategy decomposition for accelerated equilibrium in large scale logistics networks

Abstract

Static models fail to track the fast-changing supply-demand balance in global logistics. For instance, the high-speed rail express corridor exhibits a transport capacity utilisation rate of less than 70% during peak periods, along with a node load imbalance of 0.57. Existing algorithms have been shown to exhibit a 7.8% prediction error and 38% convergence time overruns during sudden demand changes. This study proposes a gradient-driven framework that combines sparse gradient, tensor decomposition, and constrained multi-objective optimization. Cost drops 28.3%, transit time shrinks 37.3%, container turnover rises 41.4%, and CO₂ falls 27.7%. In the 15-node network, the framework achieves a capacity matching degree of 89.3% with a root mean square error of 0.145, which is better than the benchmark performance of traditional methods and reinforcement learning methods. This research innovates a scalable real-time optimization paradigm, realizes sub-second equilibrium convergence and anti-disturbance recovery of large-scale logistics networks, and lays a foundation for intelligent, low-carbon and resilient logistics ecology.