Optimization Model for Production Decisions Based on Dynamic Programming

Modern enterprises face key challenges in supply chain refinement management and high-efficiency production quality control problems of spare parts and cost optimization pressure, this study focuses on this, relying on accurate quality inspection technology and advanced cost management system, to construct a series of mathematical models and decisionmaking strategies, which are expected to achieve double enhancement: on the one hand, to On the one hand, strengthening product quality control to ensure product quality excellence; on the other hand, optimizing the production cost structure, maximizing production efficiency, and winning the competitive advantage for enterprises in the market. In this paper, we design a sampling and testing scheme to determine whether a manufacturer should accept the spare parts provided by a supplier. This scheme, given the binomial distribution principle, focuses precisely on the assessment as well as optimization of the defective rate by constructing a statistical hypothesis testing model. Dynamic programming techniques are then applied to design a decision-making framework, with the fundamental aim of accurately planning the inspection time points of components and finished products on the production line and the treatment of non-conforming products. The dynamic planning model is then broadened to cope with the complex production process that includes multiple processes and more diversified parts, and the linkage between the cost composition of each process and part and the defective rate is analyzed in depth, so as to optimize and upgrade the decisionmaking process of the entire production chain, and finally, based on the data obtained from the field sampling and testing, the existing production strategy is comprehensively reassessed and adjusted.