Analyzing Simulation Dynamics in AGV Systems for Improved Traffic Prediction Using Graph Neural Networks

Abstract

Automated Guided Vehicle (AGV) systems play a critical role in modern industrial automation, but designing efficient AGV layouts is time-consuming due to the reliance on extensive simulations. This thesis explores the use of Graph Neural Networks (GNNs) to predict congestion and waiting times within AGV layouts, with the goal of accelerating the design process. Building on previous research, we use a hierarchical GNN model that combines classification and regression to estimate segment-level waiting times. To overcome data scarcity and simulation inconsistency, we construct a graph-based dataset from real AGV systems and apply targeted data augmentation focused on traverse time. Simulation consistency and appropriate simulation durations are carefully analysed to ensure data reliability. Our results show that while the classifier performs robustly, surpassing variation baselines set by the augmentation average, the regressor faces challenges in accurately modelling continuous waiting times. The study highlights key limitations, including the absence of AGV count and fleet management logic in the input features. Nonetheless, the findings demonstrate the potential of GNNs for layout evaluation and provide a foundation for more generalizable, traffic prediction tools in AGV system design.