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

Area of Science:

• Logistics and Supply Chain Management
• Operations Research
• Computational Science

Background:

• Static models inadequately represent dynamic global logistics, leading to underutilization (e.g., <70% capacity) and imbalance (0.57 node load).
• Existing algorithms struggle with sudden demand shifts, resulting in significant prediction errors (7.8%) and convergence delays (38%).

Purpose of the Study:

• To develop a novel framework for real-time optimization of large-scale logistics networks.
• To address the limitations of static models and improve the efficiency and responsiveness of logistics operations.

Main Methods:

• A gradient-driven framework integrating sparse gradient, tensor decomposition, and constrained multi-objective optimization.
• Real-time data processing and adaptive control mechanisms for dynamic adjustments.

Main Results:

• Achieved significant improvements: 28.3% cost reduction, 37.3% shorter transit times, 41.4% increased container turnover, and 27.7% lower CO₂ emissions.
• Demonstrated superior performance in a 15-node network with 89.3% capacity matching and a root mean square error of 0.145.
• Outperformed traditional methods and reinforcement learning approaches in benchmark tests.

Conclusions:

• The proposed framework offers a scalable, real-time optimization paradigm for intelligent, low-carbon, and resilient logistics.
• Enables sub-second equilibrium convergence and rapid recovery from disturbances in complex logistics networks.
• Establishes a foundation for future advancements in smart and sustainable global logistics ecosystems.