A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

Abstract

Abstract Assembly stations are important hubs that connect massive material, information, human labor, etc. The fixed-position assembly systems for complex products may deal with hundreds of thousands of processes, making them vulnerable to manufacturing exceptions. Many scheduling problems were described and solved in the past decades, however, the gap between theoretical models and industrial practices still exist. To achieve a practical method for the dynamic scheduling in case of exceptions while reducing the impact brought by the exceptions, an Intelligent Collaborative Mechanism (ICM) was proposed where negotiations on resource configuration may happen among tasks (i.e. assembly processes). The intercommunication among resources was guaranteed by the data-driven ICM framework. The Petri-net-based workflow analysis and the constraint matrix can pick out the tasks that are currently not bound by other ones. The dynamic priority of the processes was defined and obtained using grey relational analysis. The matching strategy among the selected tasks and operators can provide a scheduling plan that is close to the initial plan, so the assembly systems may remain effective even when exceptions occur. The proposed models were analyzed in a case scenario, where the impact brought by exceptions can decrease by 44.3% in terms of the operators’ utilization rate, and by 60.26% in terms of the assembly time. This research has provided a practical strategy to improve the flexibility and effectiveness of assembly systems for complex products.