Virtual Reconfiguration Method of Robotic Mixed-Model Assembly Line Using Bees Algorithm Based on Digital Twin

As an important part of intelligent manufacturing, robotic mixed-model assembly line needs to cost-effectively adjust its configuration to meet the dynamical manufacturing requirements. However, the existing reconfiguration method always considers the configurations of equipment in cyber space and ignores the interconnections between the physical and cyber spaces. Digital twin provides alternative ways to realize the interconnections between the physical and cyber spaces. In this paper, digital twin model of robotic mixed-model assembly line and the interconnections between physical and cyber spaces are realized by means of digital twin model. In addition, the mathematical model for minimizing the reconfiguration cost and load balancing is built, then the virtual reconfiguration problem is proposed. Afterwards, adaptive neighborhood search Bees algorithm, of which the adaptive neighborhood search strategy is included, is utilized to solve the proposed problem. Finally, the effectiveness of the proposed method is verified and the results show adaptive neighborhood search Bees algorithm generates better solutions compared with the other optimization algorithms. Note to Practitioners—The existing reconfiguration method is always realized in the cyber space and rarely integrates the physical industrial robot. Motivated by this, this paper combines the physical industrial robot with digital twin model of robotic mixed-model assembly line, and uses adaptive neighborhood search Bees algorithm to solve the reconfiguration problem. Based on the digital twin model of robotic mixed-model assembly line, the optimal solution of virtual reconfiguration method could be obtained considering the dynamic manufacturing requirements. Afterwards, the optimal solution can be sent to the physical robotic assembly line through the interconnection between the physical and cyber spaces. In addition, experiments under different cases show that the proposed method could generate the optimal solution when the manufacturing requirements change. In the future, the physical manufacturing process based on robotic mixed-model assembly line will be studied to make the proposed method more applicable.