Digital Twin-Driven Dynamic Scheduling for Multi-Disturbance Manufacturing Workshops

Production scheduling efficiency exerts a profound influence on overall workshop productivity. Discrete manufacturing workshops represent complex, multi-functional systems marked by frequent variability. Previous research on workshop scheduling frequently grapples with sluggish rescheduling responses and an overly narrow focus on influencing factors. To address these issues, this study integrated multiple critical disturbances, including employee absenteeism, tool wear, and non-conforming product quality, to construct a dynamic scheduling service system driven by multiple disturbances within the workshop. By incorporating Digital Twin theory, the system monitored dynamic events in the assembly workshop. A workshop scheduling planning model was developed based on these dynamic disturbances. An enhanced genetic algorithm subsequently assessed scheduling schemes directed toward minimizing both completion time and energy consumption. The system selected the most appropriate scheduling strategy based on the severity of disturbance impact, which thus augmented the timeliness and predictability of workshop scheduling. This approach ultimately optimizes workshop efficiency and reduces energy consumption. The development and subsequent validation of a workshop Digital Twin scheduling system confirmed the efficacy of the proposed scheduling strategy.