Research on Product Quality Inspection Based on Sequential Testing and Genetic Algorithms

This paper investigates the decision-making solutions for enterprise product quality inspection based on sequential testing and genetic algorithms. Firstly, using the binomial distribution and its normal approximation model, combined with the sequential testing model, the optimal sampling plans for specific confidence requirements are determined. Subsequently, an evaluation index of extra cost (EC) is introduced to select the optimal decision model. Through Matlab calculations and solutions, the performance of the greedy algorithm and the genetic algorithm under different scenarios is compared, and it is found that the genetic algorithm is superior in global optimization. Finally, a method for selecting the optimal production stage decision model based on extra cost is proposed, and the effectiveness of the model is verified through examples. The research results help enterprises to optimize inspection costs while ensuring product quality, enhancing the scientific and economic nature of production decisions.