Research on Intelligent Decision Making Based on Dynamic Optimization Model Combined with Random Forest Algorithm

The purpose of this paper is to explore the application of dynamic optimization model combined with machine learning algorithms in intelligent production decision-making [1]. This paper establishes a dynamic optimization model and a sampling inspection model based on the quality control requirements in the actual production process to provide enterprises with scientific and reasonable inspection and assembly strategies. First, a two-stage sampling inspection method is used to derive the critical values of small and large samples [2]. Secondly, the dynamic optimization model for minimizing the cost function is established by comprehensively considering the cost and loss factors, so as to derive the optimal decision-making strategy. In addition, the number of spare parts and production processes was expanded so that the total cost of inspection and reuse could be quantitatively analyzed, and an iterative algorithm was designed to find the optimal solution [3]. In addition, the decision-making process is optimized by dynamically estimating the defect rate, taking into account sampling errors and different confidence levels. In this paper, the Random Forest algorithm is also integrated into the dynamic optimization framework to enable data-driven defect rate prediction [4]. This enables more accurate and flexible inspection decisions to be made, thus optimizing productivity and reducing costs while maintaining high quality control [5].