Multi-Criteria Attention-Based Graph Neural Network: A Heterogeneous Representation Learning Framework Abstract. for Logistics System Optimization

Modeling the intricate relationships within complex logistics systems is essential for optimizing various operations—such as routing, scheduling, and distribution—in modern supply chains. These systems often exhibit significant diversity in their facilities, transportation modes, and capacity constraints, introducing a phenomenon known as “heterogeneity,” which complicates the modeling process. To simplify calculations, some researchers assume homogeneous systems, overlooking critical variability in nodes (e.g., warehouses, distribution centers) and edges (e.g., transportation routes, capacities). However, ignoring this heterogeneity can lead to a marked decrease in model accuracy. In this paper, a representation learning method specifically tailored for heterogeneous logistics systems is proposed, in which the multifaceted relationships among components are preserved and model performance in real-world scenarios is enhanced. Two novel extensions refine the underlying graph-based deep learning architecture by incorporating techniques from deep learning, graph probability models, and machine learning. The approach is evaluated on two popular Vehicle Routing Problem with Time Windows (VRPTW) datasets, using precision, F1 score, and recall as performance metrics. Experimental results indicate that this method outperforms existing approaches by providing higher precision and F1 scores, enabling more accurate classification of system components and better extraction of relationships within complex logistics networks.