Research on Production Decision Problems Based on 0-1 Integer Programming

Jiale Li; Chenxi Liu

doi:10.54097/tkfgh960

Authors

Jiale Li
Chenxi Liu

DOI:

https://doi.org/10.54097/tkfgh960

Keywords:

Nonlinear Integer Programming, Genetic Algorithms, Production Decisions, Yield Analysis, Detection and Disassembly Strategies.

Abstract

Whether enterprises conduct inspections on components and finished products during the production of electronic products is of great significance for significantly improving product quality, effectively controlling costs, and enhancing market competitiveness. Thus, this paper focuses on decision optimisation problem in the production process of electronic products. In response to the issue of potential defective parts in components and finished products, this paper proposes to use 0-1 integer programming to solve the decision problem of whether to conduct inspection and disassembly during production. With the constraints of inspection cost, defective rate, etc., and maximising profit as the objective, the decision-making models for inspection and disassembly are constructed for one process and multiple processes respectively. In order to solve the above model, this paper adopts the exhaustive search algorithm and genetic algorithm respectively to find the optimal solution under the given conditions. Finally, the validity of the model is fully demonstrated through verification with actual production data, providing robust theoretical support and practical guidance for production decision-making in electronic product enterprises.

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References

[1] Dai Jiping, Zhang Binbin. Testing and quality control of electronic products [J]. Electronic Quality, 2020, (12):74-77.

[2] Guo Jiulei. Research on testing and quality control of electronic products [J]. Science and Technology Innovation and Application, 2020, (24):64-65

[3] Ma Jun, Chen Huimin, Zhang Yu, et al. A digital twin-driven production management system for production workshop [J]. The International Journal of Advanced Manufacturing Technology, Volume 110, Issue 5-6. 2020. PP 1385-1397.

[4] Guo Hongfei, Chen Zhibin, Ren Yaping, et al. Research on integrated decision-making of dismantling sequence and dismantling depth of used products based on comprehensive evaluation of parts recycling [J]. Journal of Mechanical Engineering, 2022, 58(04):258-268.

[5] Zhao Wei. Research on optimal deployment method of oilfield capacity project based on 0-1 integer planning [J]. Contemporary petroleum and petrochemical, 2017, 25(07):14-19.

[6] Zhang Faping, Yan Xuebin. Multi-process production decision of parts based on genetic algorithm [J]. Combined machine tools and automated machining technology, 2007, (12):73-76.

[7] Hu Xiaogang. Research on the optimal design of automation intelligent technology in machinery manufacturing production line [J]. China Equipment Engineering, 2024, (17):43-45.

[8] Wang Tongtong. Research on scheduling problem of complex heavy equipment production system based on multi-objective optimization [D]. Xi'an University of Technology, 2024.

[9] REN Yaping, REN Ying, GUO Hongfei, et al. Decision-making study on the disassembly process route of decommissioned electromechanical products under the conditions of random failure of work processes and their repair [J/OL]. Journal of Mechanical Engineering, 1-12[2024-10-22].

[10] Hu Sigui, Wang Honglei. Group sequential optimisation test and application based on GB/T2828.1 (2012) [J]. Journal of Systems Engineering, 2020, 35(05):700-710.

[11] Li Jiarong, Mei Huaping, Luo Danyan, et al. Ordering and transporting of raw materials in production enterprises based on mathematical planning model [J]. Heilongjiang Science, 2024, 15(16):77-81.