Optimization scheme design in production process based on multi-stage decision-making

Authors

  • Mingkang Xu
  • Hongsen Dai
  • Lei Zuo
  • Haoli Xu
  • Bin Qu
  • Jun Lu
  • Haoqi Gao
  • Zhiyang Hu
  • Mengjiang Wu
  • Yong Huang
  • Jun Chen

DOI:

https://doi.org/10.54097/tmeer346

Keywords:

Sampling detection, Binomial distribution, Hypothesis testing, Optimization problem, Multi-stage decision making

Abstract

This paper studies the control of defective rate in the electronic industry chain, designs a small sample binomial distribution sampling detection scheme to verify the supplier 's defective rate statement and optimize the detection cost. Based on the defective rate, the linear programming and dynamic programming / decision tree model are used to optimize the multi-stage production process, including parts inspection, finished product inspection and unqualified product treatment, so as to maximize the profit of the enterprise. A phased optimization strategy is proposed for complex multi-process situations. Finally, according to the sampling test results, the defect rate estimation is adjusted, the problem model is re-optimized, and the cost control reference scheme is provided.

References

The authors deeply appreciate the great support from the Dreams Foundation of Jianghuai Advance Technology Center (No.2023-ZM01D006), the National Natural Science Foundation of China (No.62305389), the Scientific Research Project of National University of Defense Technology under Grant (22-ZZCX-07) and Hefei Comprehensive National Science Center. In addition, the authors thank Prof. Zhengdong Deng and Prof. Zhibin Ding from the Army Engineering University of PLA for their valuable knowledge of groundwater remote sensing assessment and water environment analysis.

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Published

28-09-2024

Issue

Section

Articles

How to Cite

Xu, M., Dai, H., Zuo, L., Xu, H., Qu, B., Lu, J., Gao, H., Hu, Z., Wu, M., Huang, Y., & Chen, J. (2024). Optimization scheme design in production process based on multi-stage decision-making. Journal of Computing and Electronic Information Management, 14(2), 114-116. https://doi.org/10.54097/tmeer346