A dual spatial temporal neural network for bottleneck prediction in manufacturing systems

Abstract

In manufacturing systems, bottlenecks act as constraints that limit system throughput. Extensive efforts have been made to detect and predict bottlenecks. Traditional bottleneck prediction methods predominantly utilize time-series feature analysis, which is limited in capturing the dynamic spatial dependencies introduced by production material flow. To address these limitations, we proposed a dual spatial temporal neural network for dynamic bottlenecks (Dual-BDSTN) to learn the dependencies of temporal and spatial features dynamically. In the temporal module, a gated recurrent unit combined with a self-attention mechanism is employed to capture the time-evolving dynamics of temporal features related to machine status. In the spatial module, a dynamic graph neural network is employed to learn spatial information affected by dynamic production material flow and a cross-attention mechanism captures the effect of temporal features on spatial features. Finally, gated recurrent neural networks are applied to capture the temporal trends of the temporal and spatial features to predict future starvation and blockage for identifying bottleneck locations. Experimental results demonstrate that the proposed model outperforms the best benchmark, achieving a 5.95 and 2.95 reduction in root mean square error for predicting starvation and blockage times of overall machines in the production system respectively (over 10%), with a 2.85% improvement in bottleneck prediction accuracy.