基于狮群算法的数字孪生车间调度问题优化

针对柔性作业车间调度问题,提出一种基于狮群算法的数字孪生柔性作业车间调度方法。基于实际生产过程的需求,使用狮群算法生成柔性作业车间调度初始方案,建立物理车间与虚拟车间实时交互的数字孪生柔性作业车间调度模型,在搭建的虚拟车间中对初始调度方案根据设备利用率进行方案优化。采用数字孪生模型解决设备故障等车间突发事件对生产进程的影响问题。通过使用真实车间数据对机加工车间生产调度过程试验,结果表明,采用狮群算法求解柔性作业车间调度问题,搜寻能力强,搜索速度快,可以在不同规模的问题中找到更优的解决方案;狮群算法结合数字孪生的柔性作业车间调度方案能够整体优化系统性能,有效处理扰动带来的延长生产时间问题。

Optimization of digital twin job scheduling problem based on lion swarm algorithm

A digital twin job shop scheduling method based on lion swarm optimization algorithm was proposed aiming at the flexible job shop scheduling problem, which could effectively improve the utilization of equipment and solve the production delay caused by dynamic factors such as equipment failure in discrete manufacturing. The lion swarm optimization algorithm was used to generate the initial scheme based on the requirements of the actual production process. A digital twin flexible job shop scheduling model with real-time interaction between physical shop floor and virtual shop floor was established. The initial scheduling scheme was optimized according to the equipment utilization in the virtual shop floor. The digital twin model was used to solve the delay of production process caused by shop floor emergency such as equipment failure. By using real workshop data to test the machine tool production scheduling process in machining workshop, the results showed that the flexible job shop scheduling problem based on lion swarm optimization algorithm had strong search ability and fast search speed, and could find better solutions in different scale problems. The flexible job shop scheduling scheme with digital twin could optimize the system performance as a whole, and effectively deal with the problem of prolonging production time caused by disturbance.