A Study on the Decision-making of Product Production Process Based on Dynamic Programming Model

Authors

DOI:

https://doi.org/10.61383/ejam.20253193

Keywords:

Dynamic programming, binomial distribution, Bayesian updating, quality-controlled management

Abstract

In view of the sales situation of enterprise products, especially for electronic products, the equipment and assembly of internal parts of products and the quality of parts themselves, the impact on the performance of finished products is obvious. This paper analyzes the finished product production of an enterprise producing electronic products and makes the best decision on how to optimize the production process through quality control management. Firstly, using the approximation principle of binomial distribution and normal distribution, the minimum detection times of 98 and 59 are calculated at 95% and 90% confidence levels, respectively, to verify the premise that the nominal value of the merchant does not exceed 10%. Then, by constructing a dynamic programming model, the detection process is subdivided into the detection of parts and finished products and the disassembly decision of unqualified products, and the optimal decision-making schemes in six cases are obtained. Furthermore, the dynamic programming model is extended to 5 stages and 16 steps, and the optimal solution of each step is comprehensively considered to obtain the optimal decision of the whole process. Finally, the Bayesian updating method is introduced to dynamically adjust the defective rate according to the number of defective products detected in real time, and the previous decision-making scheme is updated accordingly to realize the dynamic optimization of decision-making.

References

[1] HuS. Jack, et al., Assembly system design and operations for product variety, CIRP Annals, 60(2011), no.2, pp.715–733. DOI: 10.1016/j.cirp.2011.05.004. DOI: https://doi.org/10.1016/j.cirp.2011.05.004

[2] Nunes Eusébio, Sérgio Sousa, A dynamic programming model for designing a quality control plan in a manufacturing process, Procedia Manufacturing, 38(2019), pp.581–588. DOI: 10.1016/j.promfg.2020.01.073. DOI: https://doi.org/10.1016/j.promfg.2020.01.073

[3] Arinç A., Çetinyokuş T., İfraz M., Determination of sample size and design of final product control plan based on product type, Neural Computing and Applications, 37(2025), pp.283–302. DOI: 10.1007/s00521-024-10459-w. DOI: https://doi.org/10.1007/s00521-024-10459-w

[4] William J. Welch, Tat-Kwan Yu, Sung Mo Kang, Jerome Sacks, Computer experiments for quality control by parameter design, Journal of Quality Technology, 22(1990), no.1, pp.15–22. DOI: 10.1080/00224065.1990.11979201. DOI: https://doi.org/10.1080/00224065.1990.11979201

[5] Yi-Ting Huang, Fan-Tien Cheng, Min-Hsiung Hung, Developing a product quality fault detection scheme, 2009 IEEE Int. Conf. on Robotics and Automation, Kobe, Japan, (2009), pp.927–932. DOI: 10.1109/ROBOT.2009.5152474. DOI: https://doi.org/10.1109/ROBOT.2009.5152474

[6] Yingda Chen, Improvement of the Digital Engineering Quality Control System by Dynamic Programming Method, 2024 Second Int. Conf. on Data Science and Information System (ICDSIS), Hassan, India, (2024), pp.1–5. DOI: 10.1109/ICDSIS61070.2024.10594501. DOI: https://doi.org/10.1109/ICDSIS61070.2024.10594501

[7] Pravin P. Tambe, Makarand S. Kulkarni, A reliability based integrated model of maintenance planning with quality control and production decision for improving operational performance, Reliability Engineering & System Safety, 226(2022). DOI: 10.1016/j.ress.2022.108681. DOI: https://doi.org/10.1016/j.ress.2022.108681

[8] Rodrigo Polo-Mendoza, Gilberto Martinez-Arguelles, Rita Peñabaena-Niebles, A multi-objective optimization based on genetic algorithms for the sustainable design of Warm Mix Asphalt (WMA), International Journal of Pavement Engineering, 24(2022), no.2. DOI: 10.1080/10298436.2022.2074417. DOI: https://doi.org/10.1080/10298436.2022.2074417

[9] Chien Chen-Fu, Runliang Dou, Wenhan Fu, Strategic capacity planning for smart production: Decision modeling under demand uncertainty, Applied Soft Computing, 68(2018), pp.900–909. DOI: 10.1016/j.asoc.2017.06.001. DOI: https://doi.org/10.1016/j.asoc.2017.06.001

[10] Zhao W., Wang Y., Coordination of joint pricing-production decisions in a supply chain, IIE Transactions, 34(2002), pp.701–715. DOI: 10.1023/A:1014972527002. DOI: https://doi.org/10.1080/07408170208928906

[11] Onur Kaya, Incentive and production decisions for remanufacturing operations, European Journal of Operational Research, 201(2010), no.2, pp.442–453. DOI: 10.1016/j.ejor.2009.03.007. DOI: https://doi.org/10.1016/j.ejor.2009.03.007

[12] Marfuah U., Panudju A. T., Mansyuri U., Dynamic Programming Approach in Aggregate Production Planning Model under Uncertainty, International Journal of Advanced Computer Science and Applications, 14(2023), no.3. DOI: 10.14569/IJACSA.2023.0140321. DOI: https://doi.org/10.14569/IJACSA.2023.0140321

[13] Xiaoqin Niu, Serhat Yüksel, Hasan Dinçer, Emission strategy selection for the circular economy-based production investments with the enhanced decision support system, Energy, 274(2023). DOI: 10.1016/j.energy.2023.127446. DOI: https://doi.org/10.1016/j.energy.2023.127446

[14] Wang T., Chen Y., Qiao M. et al., A fast and robust convolutional neural network-based defect detection model in product quality control, International Journal of Advanced Manufacturing Technology, 94(2018), pp.3465–3471. DOI: 10.1007/s00170-017-0882-0. DOI: https://doi.org/10.1007/s00170-017-0882-0

[15] Martin Schader, Friedrich Schmid, Two Rules of Thumb for the Approximation of the Binomial Distribution by the Normal Distribution, The American Statistician, 43(1989), no.1, pp.23–24. DOI: 10.1080/00031305.1989.10475601. DOI: https://doi.org/10.1080/00031305.1989.10475601

[16] Wolfgang Viechtbauer et al., A simple formula for the calculation of sample size in pilot studies, Journal of Clinical Epidemiology, 68(2015), no.11, pp.1375–1379. DOI: 10.1016/j.jclinepi.2015.04.014. DOI: https://doi.org/10.1016/j.jclinepi.2015.04.014

[17] Kumar M., Gupta S.K., Multicriteria decision-making based on the confidence level Q-rung orthopair normal fuzzy aggregation operator, Granular Computing, 8(2023), no.1, pp.77–96. DOI: 10.1007/s41066-022-00314-5. DOI: https://doi.org/10.1007/s41066-022-00314-5

[18] Basma Ahmed et al., A Novel G Family for Single Acceptance Sampling Plan with Application in Quality and Risk Decisions, Annals of Data Science, 11(2024), no.1, pp.181–199. DOI: 10.1007/s40745-022-00451-3. DOI: https://doi.org/10.1007/s40745-022-00451-3

[19] Jeehyoung Kim, Bong Soo Seo, How to calculate sample size and why, Clinics in Orthopedic Surgery, 5(2013), no.3, pp.235–242. DOI: 10.4055/cios.2013.5.3.235. DOI: https://doi.org/10.4055/cios.2013.5.3.235

[20] Mohmmad Hanafy, Hoda ElMaraghy, Modular product platform configuration and co-planning of assembly lines using assembly and disassembly, Journal of Manufacturing Systems, 42(2017), pp.289–305. DOI: 10.1016/j.jmsy.2016.12.002. DOI: https://doi.org/10.1016/j.jmsy.2016.12.002

[21] Qiqige Wulan, Order scheduling optimization in manufacturing enterprises based on MDP and dynamic programming, Scientific Reports, 13(2023), no.1, pp.9783. DOI: 10.1038/s41598-023-36976-7. DOI: https://doi.org/10.1038/s41598-023-36976-7

[22] Donald Ballou et al., Modeling information manufacturing systems to determine information product quality, Management Science, 44(1998), no.4, pp.462–484. DOI: 10.1287/mnsc.44.4.462. DOI: https://doi.org/10.1287/mnsc.44.4.462

[23] John M. Antle, Sequential decision making in production models, American Journal of Agricultural Economics, 65(1983), no.2, pp.282–290. DOI: 10.2307/1240874. DOI: https://doi.org/10.2307/1240874

[24] M. A. Girshick, Herman Rubin, A Bayes approach to a quality control model, Annals of Mathematical Statistics, 23(1952), no.1, pp.114–125. DOI: 10.1214/aoms/1177729489. DOI: https://doi.org/10.1214/aoms/1177729489

[25] Vishwas Dohale et al., An integrated Delphi-MCDM-Bayesian Network framework for production system selection, International Journal of Production Economics, 242(2021). DOI: 10.1016/j.ijpe.2021.108296. DOI: https://doi.org/10.1016/j.ijpe.2021.108296

[26] Abbas Mamudu et al., Dynamic risk modeling of complex hydrocarbon production systems, Process Safety and Environmental Protection, 151(2021), pp.71–84. DOI: 10.1016/j.psep.2021.04.046. DOI: https://doi.org/10.1016/j.psep.2021.04.046

[27] Seyedmohsen Hosseini, Dmitry Ivanov, A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic, International Journal of Production Research, 60(2021), no.17, pp.5258–5276. DOI: 10.1080/00207543.2021.1953180. DOI: https://doi.org/10.1080/00207543.2021.1953180

[28] Xiaochen Hao et al., Prediction of f-CaO content in cement clinker: A novel prediction method based on LightGBM and Bayesian optimization, Chemometrics and Intelligent Laboratory Systems, 220(2022). DOI: 10.1016/j.chemolab.2021.104461. DOI: https://doi.org/10.1016/j.chemolab.2021.104461

[29] Tihomir Asparouhov, Bengt Muthén, Expanding the Bayesian structural equation, multilevel and mixture models to logit, negative-binomial, and nominal variables, Structural Equation Modeling, 28(2021), no.4, pp.622–637. DOI: 10.1080/10705511.2021.1878896. DOI: https://doi.org/10.1080/10705511.2021.1878896

[30] Adolphus Lye, Alice Cicirello, Edoardo Patelli, Sampling methods for solving Bayesian model updating problems: A tutorial, Mechanical Systems and Signal Processing, 159(2021). DOI: 10.1016/j.ymssp.2021.107760. DOI: https://doi.org/10.1016/j.ymssp.2021.107760

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Published

2025 Mar 18

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Research Article

How to Cite

[1]
“A Study on the Decision-making of Product Production Process Based on Dynamic Programming Model”, Electron. J. Appl. Math., vol. 3, no. 1, pp. 9–23, Mar. 2025, doi: 10.61383/ejam.20253193.