Research on Production Decision Optimization Based on Dynamic Programming

The paper investigates the optimization of production decisions in the manufacturing process of electronic products. Initially, the production process is analyzed, and the House of Quality method is utilized to preliminarily determine the direction of decision-making. Subsequently, a dynamic programming model is introduced, defining state variables, decision variables, and the state transition equation, and establishing the objective function. The model takes into account decisions at various stages, including part testing, finished product inspection, and handling of non-conforming products, aiming to balance cost, quality, and efficiency. The paper proposes an improved value iteration algorithm to enhance the convergence speed of the algorithm and its adaptability to environmental changes. Furthermore, in response to complex production environments with multiple processes and components, the paper also explores the application of the iterative dynamic programming model. Through continuous iterative approximation and grid refinement, the model significantly reduces computational and storage requirements, improving operational efficiency and robustness. The model is capable of handling a vast state space and is suitable for complex industrial production decision-making problems.