DECISION OPTIMIZATION MODEL FOR ELECTRONIC PRODUCT PRODUCTION BASED ON BINOMIAL DISTRIBUTION

This paper aims to solve the key balance between quality control and cost optimization in the multi-stage electronics manufacturing process. By combining statistical hypothesis testing with mixed integer linear programming (MILP), we propose a novel decision-making framework that can dynamically adapt to different defect rate, inspection cost, and risk scenarios. Firstly, a one-sided hypothesis testing method was proposed to calculate the minimum sampling size in order to solve the problem of supplier defect rate verification. Secondly, for the multi-stage production decision-making problem, a mixed integer linear programming model is constructed, and the total cost is optimized by the combination of enumeration strategies. This study provides a theoretical basis for enterprises to formulate flexible production strategies, promotes the development of production decision science by combining statistical quality control with operational optimization, and provides a data-driven tool for manufacturers to cope with the dynamic supply chain environment. This approach can be extended to the context of sustainable manufacturing, especially for recycling-oriented production systems with material uncertainty.