Research on sampling inspection and process optimization model in process production

In this paper, the decision problem in the production process of the company is studied, and the defective rate is evaluated by using the hypothesis testing model and Monte Carlo method. Assuming that the real defective rate and nominal value error are 0.05, the minimum sampling times are 50, 66, and 75 when the total capacity is 100, 200, and 300 with 95% confidence. The minimum number of samplings in the infinite population case is 98. The multi-stage decision-making model based on dynamic optimization is constructed, the value function and dynamic recursion are added to Markov decision, the production decision of each stage is analyzed and solved, the objective function of maximum profit is obtained, the corresponding production decision is determined, and the dynamic optimization is evaluated and analyzed by sensitivity analysis. Taking parts 1 as an example, comparing situation 2 and situation 4, it is found that the testing cost of parts and finished products has changed its maximum profit and corresponding decision, which is mainly reflected in the decision of whether to inspect parts and finished products. It is concluded that the detection cost has a great influence on the optimal decision. In this paper, the decision problem in the production process of the company is studied, and the defective rate is evaluated by using the hypothesis testing model and Monte Carlo method. Assuming that the real defective rate and nominal value error are 0.05, the minimum sampling times are 50, 66, and 75 when the total capacity is 100, 200, and 300 with 95% confidence. The minimum number of samplings in the infinite population case is 98. The multi-stage decision-making model based on dynamic optimization is constructed, the value function and dynamic recursion are added to Markov decision, the production decision of each stage is analyzed and solved, the objective function of maximum profit is obtained, the corresponding production decision is determined, and the dynamic optimization is evaluated and analyzed by sensitivity analysis. Taking parts 1 as an example, comparing situation 2 and situation 4, it is found that the testing cost of parts and finished products has changed its maximum profit and corresponding decision, which is mainly reflected in the decision of whether to inspect parts and finished products. It is concluded that the detection cost has a great influence on the optimal decision.