Author(s)

XiaoYan Liu

Affiliation(s)

School of Information Engineering, Lanzhou Petrochemical University of Vocational Technology, Lanzhou 730060, Gansu, China.

Corresponding Author

XiaoYan Liu

ABSTRACT

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.

KEYWORDS

Quality control; Mixed integer programming; Hypothesis testing; Sampling testing; Production decisions

CITE THIS PAPER

XiaoYan Liu. Decision optimization model for electronic product production based on binomial distribution. World Journal of Management Science. 2025, 3(2): 63-68. DOI: https://doi.org/10.61784/wms3068.

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