Optimizing Electronic Manufacturing Based on Sampling Inspection and Multi-Objective Optimization Algorithms

As competition in the market intensifies, electronics manufacturers are faced with the dual challenge of improving product quality and reducing production costs. The purpose of this study is to construct a decision-making model for electronic product production based on sampling detection and multi-objective optimization algorithm to achieve the dual goals of quality control and cost optimization. This paper first reviews the relevant research on sampling detection theory and multiobjective optimization algorithm, and then describes the quality control problems in the production process of electronic products in detail, and puts forward three specific problem scenarios: spare parts quality control, production cost minimization and multi-process decision optimization. In order to solve these problems, this paper constructs a corresponding mathematical model, uses the normal distribution approximation and Z-test method to determine the sampling scheme, and evaluates the economy of different decisions through expected cost analysis. Furthermore, multi-objective optimization algorithms such as jellyfish search algorithm, linear weighting method and whale algorithm are used to optimize various decision-making variables in the production process to minimize the total cost and optimize the quality. Through the algorithm implementation and case analysis, the effectiveness and practicability of the model are verified.