Author(s)

HongLei Ye^*, SiYi Jiang, Li Zhang, DanYao Yan

Affiliation(s)

School of Mathematics and Statistics, Huaibei Normal University, Huaibei 235065, Anhui, China.

Corresponding Author

HongLei Ye

ABSTRACT

This paper aims to solve the problem of quality and cost control in the process of electronic product assembly. Through the cost-benefit analysis of each link in the assembly process, the mathematical model is constructed and the optimal decision-making scheme is proposed. The model comprehensively considers the defective rate of parts and finished products, detection cost, assembly cost, market price and other factors, aiming to achieve the best balance between product quality assurance and cost saving. Firstly, two sampling schemes are designed and compared, and a more economic binomial distribution scheme is recommended. Then according to the actual situation encountered in production, the cost and benefit of detecting spare parts, finished products and disassembling unqualified finished products are analyzed, and the optimal decision scheme is obtained after comparing the experimental data. In addition, taking two processes and eight spare parts as examples, three decision-making schemes are proposed to meet different needs. The experimental results can provide decision support for product assembly, help reduce costs, improve product quality and enhance market competitiveness.

KEYWORDS

Simple random sampling; Cost-benefit theory; Decision tree algorithm; Central limit theorem; Product assembly decisions

CITE THIS PAPER

HongLei Ye, SiYi Jiang, Li Zhang, DanYao Yan. Electronic product assembly decis-ion based on the dual perspectives of quality and cost. Eurasia Journal of Science and Technology. 2025, 7(1): 55-62. DOI: https://doi.org/10.61784/ejst3067.

REFERENCES

[1] Zhu Haihua, Fu Tairan, Li Fei, et al. Prediction of complete set time of complex product assembly material process based on digital twin. Journal of Mechanical Engineering, 2024: 1-15.

[2] Zhang Jin, Liu Jianhua, Zhao Wenhao, et al. Risk early warning method for complex product assembly technology problems based on pre-trained model and similarity algorithm. Journal of Mechanical Engineering, 2024(24): 317-329.

[3] Shen Jitong, Wang Chuangjian, Xu Xianhao. Industrial Engineering and Management, 2023, 28(01):110-119.

[4] 2024 National College Student Mathematical Contest in Modeling. 2024-09-05.

[5] Lin Yaohua, Liang Zhong, Hu Huaping. Cuckoo search algorithm based on beta distribution. Journal of Nanjing University(Natural Science), 2016, 52(04): 638-646.

[6] Wang Jieting, Li Feijiang, Li Jue, et al. Gini index and decision tree method for mitigating stochastic consistency.Science China:Information Science, 2024, 54(01): 159-190.

[7] Wen Hufeng, Shang Tianshuai, Li Jian, et al. Ground state spin prediction of odd A nuclei based on decision tree method. Acta Physica Sinica, 2023, 72(15): 33-41.

[8] Wu Bei. Research and application of performance prediction model based on decision tree algorithm. Xi'an: Xi'an University of Technology, 2019.

[9] Chen Jiajun, Miao Duoqian. An adaptive decision tree algorithm based on the fusion of decision risk cost and attribute preference. Journal of Chinese Computer Systems, 2018, 39(06): 1208-1212.

[10] Li Xinjie. Cost-benefit analysis of enterprises from the perspective of budget, business and financial integration. Modern Business Research, 2024(08): 104-106.

[11] Jin Guomin. Enterprise cost management strategy based on cost-effectiveness. Investment and Cooperation, 2021(10): 38-39.

[12] Jianxiang W, Lu C, Pu H, et al. Decision Tree-Based Data Stratification Method for the Minimization of the Masking Effect in Adverse Drug Reaction Signal Detection. Applied Sciences, 2021, 11(23): 11380-11380.

[13] Lv Xinrong. Reflections on the cost-effectiveness of enterprise internal control. Finance and Economics, 2021(19): 125-126.

[14] Hu Weixiang. On the internal control strategy of start-up SMEs based on the principle of cost-effectiveness. Finance and Economics, 2020(20): 100-101.

[15] Ding Ying Cost benefit balance point analysis of enterprise risk management. Contemporary Accounting, 2018(09): 32-33.