Multi-Objective Optimization and Federated Learning for Agri-Food Supply Chains via Dynamic Heterogeneous Graph Neural Networks

The intricate and dynamic nature of agricultural supply chains imposes stringent demands on optimization methodologies, necessitating multi-objective considerations, privacy safeguards, and decision transparency to address pivotal challenges in ensuring food security and sustainable development. This study introduces a Dynamic Heterogeneous Multi-Objective Graph Neural Network (DHMO-GNN) model, meticulously tailored for optimizing agricultural supply chains. It integrates five core modules: data preprocessing and heterogeneous graph construction, dynamic graph neural networks, multi-objective optimization, interpretability enhancement, and federated learning collaboration. The model adeptly captures temporal dynamics through sequential attention mechanisms and incremental updates, harmonizes cost, delivery time, and carbon emissions via multi-task learning and Pareto optimization, augments decision transparency with GNNExplainer and SHAP, and surmounts data silos by leveraging federated learning alongside differential privacy. Empirical evaluations on the Chengdu Hongguang Town Farmers’ Market dataset demonstrate that the centralized DHMO-GNN variant achieves a hypervolume indicator (HV) of 0.849, surpassing baseline models; the federated variant exhibits only a 2.6% decline under privacy constraints, underscoring its robustness. Ablation studies further corroborate the synergistic contributions of each module. This research furnishes an efficacious and trustworthy framework for the intelligent management of agricultural supply chains, holding profound implications for advancing digital transformation and green development.