Research on Assembly and Detection Optimization Based on Multi-objective Decision Model and Genetic Algorithm

Firstly, this paper uses the Bayesian updating sampling detection scheme to adjust and optimize the decision-making process to ensure that the defective rate of spare parts is accurately evaluated while minimizing the number of inspections. Then, the corresponding multi-objective optimization model was constructed by integrating various factors in the assembly and testing process, such as cost control and product quality. Finally, combined with the pro-duction process, the detection scheme of each step of parts, semi-finished products and costs is considered, and the genetic algorithm is used to solve the optimal strategy of production cost minimization. This research not only provides scientific tools and methods for enterprise production inspection quality control, but also provides a reference for similar multi-objective planning problems.