Author(s)

Keyi An, Yuxi Pu, Jiayu Zhang, Jiaxin Li

Corresponding Author

Keyi An

Abstract

In this paper, we develop a multi-stage decision-making model aimed at minimizing costs in a production process involving multiple stages and components. The model is formulated using integer programming and solved with a Monte Carlo algorithm. We further enhance the optimization process by employing a genetic algorithm. Additionally, we assume that the defect rates of components, semi-finished, and finished products are determined through random sampling inspection. Using theoretical formulas for sampling estimation, we calculate the upper defect rate limit at 15.9% with 95% confidence and the lower limit at 3.64% with 90% confidence. These defect rates are then used to adjust the model parameters, allowing for a reevaluation of production phase decisions and average production costs.

Keywords

Multi-stage decision-making model, Integer programming, Monte Carlo algorithm, Genetic algorithm