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  4. Vol 9, Issue 1, 2025

A Study on Production Decision Making Problem Based on Multi-Stage Stochastic Dynamic Programming

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DOI: 10.23977/acss.2025.090110 | Downloads: 28 | Views: 618

Author(s)

Xinyue Tong 1, Jiayi Deng 2, Liang Yan 1, Zhengzheng Ning 1, Jiayi Zhou 1, Zhaoyang Wang 1

Affiliation(s)

1 School of Information Engineering, Xi'an Mingde Institute of Technology, Xi'an, China
2 School of Economics and Management, Xi'an Mingde Institute of Technology, Xi'an, China

Corresponding Author

Jiayi Zhou

ABSTRACT

This study aims to explore the production decision-making problem based on multi-stage stochastic dynamic programming to cope with the many uncertainties faced in modern production management. Firstly, the Bayesian sequential probability ratio test model is built to solve the problem of sampling and testing when purchasing spare parts, which effectively reduces the testing cost and improves the reliability of decision-making. Then, a multi-stage stochastic dynamic planning decision-making model is constructed, which integrally considers multiple stages and various cost factors in the production process to maximise the profit of the enterprise. The results show that the model can effectively deal with the stochastic demand and uncertainty in the production process and provide an optimal production decision-making solution for the enterprise. However, the solving efficiency of the model and its ability to handle large-scale data still need to be improved. Future research will be devoted to optimising the algorithm and expanding the application scope of the model to better adapt to the complex and changing production environment.

KEYWORDS

Multi-stage Stochastic Dynamic Programming, Production Decision Making, Bayesian Sequential Probability Ratio Test, Uncertainty, Optimisation Models

CITE THIS PAPER

Xinyue Tong, Jiayi Deng, Liang Yan, Zhengzheng Ning, Jiayi Zhou, Zhaoyang Wang, A Study on Production Decision Making Problem Based on Multi-Stage Stochastic Dynamic Programming. Advances in Computer, Signals and Systems (2025) Vol. 9: 64-73. DOI: http://dx.doi.org/10.23977/acss.2025.090110.

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