一种基于博弈进化算法的柔性作业车间调度方法、装置、电子设备及介质
By optimizing the scheduling of flexible workshops using a game-theoretic evolutionary algorithm, the problems of blindness and premature convergence in traditional evolutionary algorithms in large-scale complex scheduling problems are solved, achieving efficient resource allocation and improved production efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANJING UNIV OF POSTS & TELECOMM
- Filing Date
- 2023-06-15
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional evolutionary algorithms suffer from blindness and premature convergence in flexible job shop scheduling problems, making it difficult to effectively solve large-scale complex scheduling problems, resulting in low resource utilization and low production efficiency.
A game-theoretic evolutionary algorithm is adopted. By establishing a production scheduling mathematical model, the game-theoretic evolutionary algorithm is used to iteratively solve the population. The selection probability is updated by combining repeated game crossover operator and Bayes' theorem, thereby optimizing equipment allocation and process sequencing and generating the optimal scheduling scheme.
It improves the optimization effect and adaptability of the algorithm, enabling it to quickly and effectively generate optimized scheduling schemes, improve production efficiency and resource utilization, and reduce production costs.
Smart Images
Figure CN116523266B_ABST