Knowledge fusion and spatio-temporal modeling for dynamic bottleneck prediction in sustainable production processes

Sustainable production is for the manufacturing industry to improve production efficiency and reduce energy consumption. Production system complexities, which cause bottlenecks, greatly impact process efficiency. However, the challenge lies in effectively utilizing sustainable production process knowledge, which is often under-exploited, particularly when integrating this knowledge with knowledge graphs for production bottleneck prediction. To solve the problem, this study presents a method for dynamic bottleneck prediction by integrating knowledge graphs and spatio-temporal models, leveraging underused production process knowledge. Initially, developing a sustainable production process knowledge graph effectively captures correlations between workstation states and external factors at production bottlenecks. Subsequently, a knowledge cross-fertilization module merges this correlation knowledge with spatio-temporal features, enhancing bottleneck prediction accuracy and reliability. Therefore, it efficiently predicts each workstation's blockage and starvation, which has significance in identifying accurately future bottleneck workstations. A case study demonstrates that this method significantly reduces root-mean-square error by 7% to 42% in predicting overall system blockage and starvation metrics. Moreover, the mean-absolute-error of this method is within 4.5% over various time scales of 30,60,90, and 120 minutes. Furthermore, these results confirm that the method introduced holds considerable practical significance in precisely identifying future workstation bottlenecks, offering novel tools and insights for enhancing production efficiency.