Intelligent Workshop Bottleneck Prediction Based on Complex Network

Production bottleneck is one of the key factors affecting production efficiency. In this paper, based on the production network, the bottleneck is dynamically identified based on the production data. A complex network is established by abstracting the actual production process steps and connecting the actual influences between process steps. With degree centrality, closeness centrality, betweenness centrality and clustering coefficient, these four indicators are used as the evaluation criteria for the global importance of the nodes, and the network efficiency matrix is used as the evaluation criterion for the local importance. Combined with the node manufacturing capability, the dynamic indicator of bottleneck is calculated. In this way, the bottleneck can be identified in a clear state even there are multiple bottlenecks.