Research on Enterprise Production Decision Optimization Based on Dynamic Optimization Technology

Abstract

Enterprise production decision-making planning is very important to the development of enterprises, and scientific and reasonable decision-making planning can effectively optimize resource allocation, control costs and improve economic benefits. In order to cope with the complex decision-making challenges in enterprise production, this study constructs a complete production decision-making planning system based on dynamic optimization strategies and the integration of a variety of advanced mathematical models and methods. In practice, the binary inspection combined with the normal approximation model is used for the incoming inspection of spare parts to accurately determine the sampling scale and testing standards, so as to ensure product quality and reduce testing costs. In the multi-stage decision-making of the whole production process, the cost decision-making model of spare parts testing, finished product testing and exchange, and dismantling of unqualified finished products is constructed, and the cost and loss are carefully weighed to help enterprises make optimal decisions. The MIL model is used to deal with the production decisions of multi-process and multi-spare parts, and the optimal production plan is screened out by comprehensively considering the cost and defective rate. Utilize DBN and MDP to cope with the dynamic production environment, optimize decision-making in real time based on the dynamic defect rate of sampling and detection, ensure that enterprises maximize cost-effectiveness in the midst of uncertainty, and promote the sustainable and stable development of enterprises.