Research on Production Process Decision Making Based on Dynamic Programming and Simulated Annealing Algorithm

Runqian Li; Weiyu Zhang

doi:10.54097/bvgdp107

Authors

Runqian Li
Weiyu Zhang

DOI:

https://doi.org/10.54097/bvgdp107

Keywords:

Dynamic programming, simulated annealing, Monte Carlo method.

Abstract

In this paper, a comprehensive decision model based on dynamic programming and simulated annealing algorithm is constructed to solve the decision optimization problem of an enterprise in the production of popular electronic products. By analyzing each stage of the production process in detail, including parts purchase, inspection, assembly and finished product inspection, and combining the defective rate, inspection cost, market price and return loss and other key parameters of each stage, a scheme for optimizing production decision is formed. In this paper, the dynamic programming method is used to optimize the production path, and the simulated annealing algorithm is introduced to solve the larger scale production planning problem, and the detection and treatment strategy in the production process of enterprises is optimized. Finally, it provides an effective decision-making basis for enterprises to obtain the maximum production profit under uncertain conditions.

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References

[1] A. B, J.P. S, J.C. M G, et al. Modified dynamic programming for asteroids belt exploration [J]. Acta Astronautica, 2024, 215142-155.

[2] Alexandru V, Constantin I C, Mihai D C, et al. Simulated annealing algorithms used for microstructural design of composites [J]. Materials Today: Proceedings, 2023 combine (P4): 680-684. DOI: https://doi.org/10.1016/j.matpr.2023.05.109

[3] Ahmed E K, Hossan M M C, Pradip R, et al. A decision model for sustainable informal entrepreneurship in cities [J]. International Journal of Climate Change Strategies and Management, 2019, 16 (3): 276-297. DOI: https://doi.org/10.1108/IJCCSM-02-2022-0026

[4] Tartero G, Krauth W. Concepts in Monte Carlo sampling [J]. American Journal of Physics, 2024, 92 (1): 65-77. DOI: https://doi.org/10.1119/5.0176853

[5] Wang Q, Yu D, Zhou J, et al. Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm [J]. Sustainability, 2023, 15 (9). DOI: https://doi.org/10.3390/su15097388

[6] Claudio C, Romualdo S P. Relevance of backtracking paths in recurrent-state epidemic spreading on networks [J]. Physical Review E, 2018, 98 (5). DOI: https://doi.org/10.1103/PhysRevE.98.052313

[7] Cui S, Zhao J, Shi B. The Fitness Landscapes Exploration Based on the Parallel Simulated Annealing Algorithm [J]. Journal Journal of Big Data and Computing, 2024, 2 (1). DOI: https://doi.org/10.62517/jbdc.202401114

[8] H A C, Ankur G, Guang C, et al. Locally optimal geometry for surface-enhanced diffusion [J]. Physical review. E, 2023, 108 (4-2): 045101-045101. DOI: https://doi.org/10.1103/PhysRevE.108.045101