FLEXIBLE JOB SHOP SCHEDULING BASED ON DIGITAL TWIN AND IMPROVED BACTERIAL FORAGING

To realize the dynamic scheduling of complex workpiece processing in complex workpiece job shop, a hybrid dynamic scheduling method with Digital Twin and improved bacterial foraging algorithm (IBFOA) is proposed to minimize the maximum completion time and machine load. During the actual workshop processing, the flexible job shop scheduling problem (FJSP) is divided into two sub-problems: machine assignment and process sequencing. The initial scheduling scheme is completed using an IBFOA to construct a Digital Twin flexible job shop scheduling model. Digital Twin model is used to solve the impact of workshop emergencies. Based on typical benchmark cases and real data from a machine company's mould shop, the machining shop production scheduling experiments are conducted. The results show that the scheduling scheme using the IBFOA combined with the Digital Twin can optimize the system performance as a whole and effectively deal with the problem of extended production time caused by disruption. The algorithm can obtain the most satisfactory scheduling solution and the effectiveness of solving the multi-objective FJSP are verified.

Automated summary

This study proposes a hybrid dynamic scheduling method with Digital Twin and improved bacterial foraging algorithm (IBFOA) to optimize the complex workpiece processing in a job shop. The results show that this method can effectively minimize the maximum completion time and machine load, and address the issue of extended production time caused by disruptions.