This study explores how to optimize production decisions through mathematical and stochastic simulation methods to address the uncertainties and risks present in the production process. A production decision model considering the fluctuation of defect rate was constructed based on the production of electronic products. First of all, the impact of sampling testing methods on the defect rate was analyzed, and a confidence interval was constructed to quantify the fluctuation range of the defect rate. What’s more, random variables are introduced to simulate the fluctuation of defect rate, and Monte Carlo simulation is used for optimization to obtain a more realistic optimal decision solution. Finally, the effectiveness of the random simulation method is verified through comparative analysis. This study provides new ideas and methods for optimizing production decisions, which has important reference value for improving the economic benefits and reducing risks of enterprises.

Yue Wu, Xiaoqin Ma, Zishuo Qin. Research on Production Decision Optimization Based on Random Simulation and Confidence Interval Analysis. Academic Journal of Mathematical Sciences (2025), Vol. 6, Issue 2: 11-16. https://doi.org/10.25236/AJMS.2025.060202.

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