Research on Dynamic Decision-making in the Production Process

This paper addresses the decision-making problems in the production process of enterprises, and builds decision models for maximizing the profit rate of production, minimizing the decision, and minimizing the number of inspection times based on dynamic programming models. Finally, the paper implements the cyclic decision-making process in the production process using the iterative traversal method, and solves the production decision Pareto optimal solution using the 0-1 programming model. Six decision schemes are obtained under six scenarios.A dynamic programming model based on greedy strategy is established, and a production decision scheme for multi-process and multi-component production is designed. Based on the dynamic programming model, the global optimization is carried out using the greedy strategy to obtain the corresponding Pareto optimal solution.The Bayesian sampling method is adopted as the sampling inspection method, and the mathematical expectation value of the defect rate of each component in the production process is dynamically calculated. The Bayesian sampling method calculates the corresponding posterior expectation value based on different prior distribution parameters, dynamically calculates the mathematical expectation value under different production scale, and analyzes the influence of prior distribution parameters on the number of decision changes and expected profit rate in the production process.