Abstract: Intelligent manufacturing is a common trend in the development of the world′s manufacturing industry,and the digital twin,as an important enabling technology to promote the implementation of intelligent manufacturing,is a key link to intelligent manufacturing. To deal with the problems of uncertain events and low transparency of production information,a flexible job-shop scheduling optimization method based on digital twin simulation was proposed. Firstly,the digital twin shop scheduling framework was designed,and the AnyLogic software flexible job-shop digital twin simulation model was constructed. Secondly,considering the maximum completion time,energy consumption and total load of equipment,a flexible job-shop scheduling model was established,and an improved NSGA-Ⅱ algorithm was proposed to solve the problem.Multi-strategy mixed population initialization method was adopted,and different cross-mutation strategies were adopted for process and machine coding. Finally,the effectiveness of the proposed method was verified based on the standard calculation examples and a manufacturing example of engine cylinder head.

Key words: flexible job-shop scheduling, multi-objective optimization, digital twin simulation, AnyLogic software