Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2024, 7(6); doi: 10.25236/AJETS.2024.070606.

Research on Process Optimisation Based on Dynamic Planning Models

Author(s)

Weize Fan1, Zishuo Zheng2

Corresponding Author:
Weize Fan
Affiliation(s)

1International Microelectronics College, Dongguan University of Technology, Dongguan, China

2College of Telecommunications Engineering and Intelligence, Dongguan University of Technology, Dongguan, China

Abstract

With the rapid development of electronic manufacturing and intelligent technology, the market requirements for product quality have become increasingly strict. In this paper, we study the quality control and cost optimization problem in the production process of electronic products by combining dynamic planning and decision analysis methods. We construct a multi-stage quality control optimization model based on key indicators such as defective rate, inspection cost, assembly cost and rework cost, aiming to maximize profit and minimize production cost. By applying integer programming and optimization algorithms, we developed an efficient optimization model that identifies the best inspection strategies across multiple production stages for quality control optimization. Focusing on inspection strategies for individual components and whole assemblies, this paper analyzes the role of multiple inspection nodes in reducing production losses and improving profitability.

Keywords

Normal Distribution, Dynamic Programming, Integer Programming, Optimal Policy, Quality Control

Cite This Paper

Weize Fan, Zishuo Zheng. Research on Process Optimisation Based on Dynamic Planning Models. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 6: 36-43. https://doi.org/10.25236/AJETS.2024.070606.

References

[1] Yang Yichen, Bai Xu, Li Houpiao. 'Application of Optimization Models in Mathematical Modeling', Experimental Science and Technology, 2017(03:20-24.

[2] Si Shoukuai, Sun Xijing. 'Mathematical Modeling Algorithms and Applications (3rd Edition)', National Defense Industry Press, 2023.

[3] Chen Rongjun, Liu Yongcai, Huang He, Tang Guochun. 'Dynamic Programming Algorithm for Single Machine Supply Chain Sequencing Problems', Journal of Operations Research (Chinese English), 9[2024-09-07].

[4] Zou Wei, Ge Ling, Liu Yubiao. 'Reinforcement Learning', Beijing: Tsinghua University Press, 2020. isbn 9787302538295.

[5] Cao Hanqi, Su Peng, Liu Fei. 'A Linear Approximation Iterative Dynamic Programming Algorithm for Chemical Processes', Proceedings of the 35th China Process Control Conference, Jiangnan University Advanced Control of Light Industry Process Key Laboratory, 2024. DOI: 10.26914/c. cnkihy. 2024. 020037.