Dynamic scheduling strategy of intelligent RGV

Abstract

In order to improve the working efficiency of RGV-CNC intelligent processing system, this paper constructs a model with predictive control and rolling optimization function from shallow to deep. The model can be applied to one process and two processes, the rolling optimization model also has excellent stability in the face of possible failures. For the establishment of the scheduling model of RGV cars, this paper first conducted a comprehensive analogy between the abstract problem and the actual disk scheduling, and found that the SPSS algorithm in the disk scheduling algorithm can adapt to this environment well. On this basis, this paper conducts an in-depth study on the "request-response" mechanism between RGV and CNC and summarizes the general rules. It is found that the working mechanism of the system is very similar to the "autocorrelation/cross-correlation influence" that is common in signal analysis. Based on this, this paper constructed the self-owned and mutual influence complementary RVG scheduling strategy, and used genetic algorithm to optimize the overall situation. The results showed that under the scheduling strategy constructed in this paper, the average utilization rate of CNC was as high as 95%, which strongly proved the scientific and practical nature of this model.