Research on Assembly and Detection Optimization Based on Multi-objective Decision Model and Genetic Algorithm
DOI:
https://doi.org/10.54097/8xe2za57Keywords:
Bayesian Updating, Sampling Detection, Multi-Objective Optimization Model, Pareto FrontierAbstract
Firstly, this paper uses the Bayesian updating sampling detection scheme to adjust and optimize the decision-making process to ensure that the defective rate of spare parts is accurately evaluated while minimizing the number of inspections. Then, the corresponding multi-objective optimization model was constructed by integrating various factors in the assembly and testing process, such as cost control and product quality. Finally, combined with the pro-duction process, the detection scheme of each step of parts, semi-finished products and costs is considered, and the genetic algorithm is used to solve the optimal strategy of production cost minimization. This research not only provides scientific tools and methods for enterprise production inspection quality control, but also provides a reference for similar multi-objective planning problems.
Downloads
References
[1] Hao Huibing. Research on reliability modelling and assessment method of performance degradation based on Bayesian updating and Copula theory [D]. Southeast University,2016.
[2] Feng Shiyong, Ni, Jiaxun, Zou, Guohua , Sample Survey Theory and Methods(2nd Edition) , China Statistical Press, 2012-09-01.
[3] H. Wen, T. Zhang, R. Wang, L. Wang, Progress of Research on Knee Frontier Search Methods for Multi-Objective Optimisation, Control Theory and Applications, Science Press 2021-03-04.
[4] X. Lin, H.-L. Zhen, Z. Li, Q.-F. Zhang, and S. Kwong, ‘Pareto multi-task learning,’ Advances in neural information processing systems, vol. 32, 2019.
[5] M. Crawshaw, ‘Multi-task learning with deep neural networks: a survey,’ arXiv preprint arXiv:2009.09796, 2020.
[6] M. Momma, C. Dong, and J. Liu, ‘A multi-objective/multi-task learning framework induced by pareto stationarity,’ in International Conference on Machine Learning. 2022, pp. 15 895-15 907.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Computing and Intelligent Systems
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
