Supply Chain Management (SCM) is an essential component of any successful enterprise’s supply chain. For a business’s continued operation and positive reputation, its well-organized product production and shipping processes are significant. With all the progress deep reinforcement learning has made in logistics, there are still models that cannot handle relational data in supply chain graphs or adapt quickly enough to new sustainability regulations. To bridge the gap, a Sustainability-aware Hierarchical Graph-driven Meta Reinforcement Learning Optimizer (SHG-MRLO) is proposed, integrating hierarchical policy architectures, Graph Neural Network (GNN), and meta learning for scalable adaptation and improved performance. The optimizer model supplies a supply chain as a dynamic graph, where a meta controller dynamically orchestrates decision strategies across multilevel graph-embedded states, ensuring robust adaptation to unexpected disruptions and demanding sustainability constraints. An experimental evaluation on benchmark datasets simulating a realistic supply chain scenario demonstrates that the proposed model achieves a 7.5% increase in on-time delivery, a 28.6% reduction in carbon emissions, and a path coverage rate of 93.3%, outperforming the baseline models. The presented SHG-MRLO technique can independently achieve SCM policies in the face of a complex, adaptive environment. This work contributes a scalable and interpretable framework for enhancing sustainable supply chain performance, merging the robustness of meta-learning with the expressiveness of graph-based modelling, and paves the way for the next generation of eco-efficient supply chain solutions.
Feifei Sun (2026) studied this question.