Assembly processes for complex products primarily involve manual assembly and often encounter various disruptive events, such as the insertion of new orders, order cancellations, task adjustments, workers absences, and job rotations. The dynamic scheduling problem for complex product assembly workshops requires consideration of trigger events and time nodes for rescheduling, as well as the allocations of multi-skilled and multi-level workers. The application of digital twin technology in smart manufacturing enables managers to more effectively monitor and control disruptive events and production factors on the production site. Therefore, a dynamic scheduling strategy based on digital twin technology is proposed to enable real-time monitoring of dynamic events in the assembly workshop, triggering rescheduling when necessary, adjusting task processing sequences and team composition accordingly, and establishing a corresponding dynamic scheduling integer programming model. Additionally, based on NSGA-II, an improved multi-objective evolutionary algorithm (IMOEA) is proposed, which utilizes the maximum completion time as the production efficiency indicator and the time deviation before and after rescheduling as the production stability indicator. Three new population initialization rules are designed, and the optimal parameter combination for these rules is determined. Finally, the effectiveness of the scheduling strategy is verified through the construction of a workshop digital twin system.

中文翻译：

---

数字孪生驱动的复杂产品装配车间动态调度与人员分配


复杂产品的装配流程主要涉及手工装配，经常会遇到各种干扰事件，例如新订单的插入、订单取消、任务调整、工人缺勤和工作轮换等。复杂产品装配车间的动态调度问题需要考虑触发事件和重新调度的时间节点，以及多技能、多层次工人的分配。数字孪生技术在智能制造中的应用使管理人员能够更有效地监视和控制生产现场的破坏性事件和生产因素。因此，提出基于数字孪生技术的动态调度策略，实现对装配车间动态事件的实时监控，必要时触发重新调度，相应调整任务处理顺序和团队组成，建立相应的动态调度整数规划模型。此外，基于NSGA-II，提出了一种改进的多目标进化算法（IMOEA），以最大完成时间作为生产效率指标，以重新调度前后的时间偏差作为生产稳定性指标。设计了三种新的种群初始化规则，并确定了这些规则的最佳参数组合。最后通过车间数字孪生系统的构建验证了调度策略的有效性。