一种可重入装配柔性作业车间调度问题的优化方法和系统
By combining a goal-oriented local search multi-objective evolutionary algorithm with an energy consumption adjustment strategy, the scheduling of reentrant assembly flexible workshops is optimized, solving the high energy consumption and high difficulty scheduling problems, improving production efficiency and reducing energy consumption, and achieving green and efficient production.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- UNIV OF SCI & TECH BEIJING
- Filing Date
- 2023-08-08
- Publication Date
- 2026-07-17
AI Technical Summary
The scheduling problem of reentrant assembly flexible workshop is highly difficult to solve, especially in the wafer manufacturing of microelectromechanical systems. The workpiece enters the same equipment multiple times to wait for processing, which increases resource competition and has high energy consumption characteristics. Existing technologies are difficult to effectively optimize production efficiency and energy consumption.
We employ the MOEA/D-OOLS decomposition-based multi-objective evolutionary algorithm, which combines goal-oriented local search, to design a rule-based population initialization method. This method generates initial individuals that satisfy assembly constraints. Furthermore, we optimize the scheduling scheme through a goal-oriented local search strategy and combine it with an energy consumption adjustment strategy to reduce machine idle time, thereby optimizing completion time and total energy consumption.
It effectively solved the scheduling problem of reentrant assembly flexible workshop, improved production management efficiency, reduced energy consumption, and achieved green and efficient production.
Smart Images
Figure CN117055481B_ABST