Joint multi-objective dynamic scheduling of machine tools and vehicles in a workshop based on digital twin

Abstract

Dynamic job-shop scheduling is vital for improving job-shop efficiency and reducing energy consumption. A workshop is a complex combination of multiple functions. Previous studies regarding workshop scheduling showed several problems, such as delayed rescheduling response, single influencing factors, and the separation of machine tools and vehicles. Therefore, to address these issues, this paper constructs a multi-factor scheduling service system with various factors, such as machine failure, tool wear, and product quality. Combined with the international background of rising energy prices, the scheduling scheme is evaluated while having two goals to achieve: minimizing the completion time and achieving energy consumption flexibly by controlling the different parameters’ values. Introducing digital twin theory, machine fault prediction, tool wear prediction, and product quality monitoring can achieve the timeliness and predictability of the workshop scheduling, the joint scheduling of machine tools, and the vehicle transport tasks. Finally, the superiority of this method is verified by processing some critical parts in a marine diesel engine workshop.