Application of Big Data Technology Combined with Clustering Algorithm in Manufacturing Production Analysis System

Yu Liu; Zhengchao Zhang; Shicao Jiang; Yunfei Ding

doi:10.31181/dmame712024897

Authors

Yu Liu School of Economics & Management, Liaoning University of Technology, Jinzhou, 121000, China https://orcid.org/0009-0009-9402-5412
Zhengchao Zhang College of Economic, Bohai University, Jinzhou, 121000, China https://orcid.org/0009-0005-7009-0763
Shicao Jiang School of Economics & Management, Liaoning University of Technology, Jinzhou, 121000, China https://orcid.org/0009-0007-2601-1313
Yunfei Ding School of Economics & Management, Liaoning University of Technology, Jinzhou, 121000, China https://orcid.org/0009-0003-4069-2961

DOI:

https://doi.org/10.31181/dmame712024897

Keywords:

Clustering Algorithm, Big Data, Apriori, Production Analysis, Genetic Algorithm

Abstract

Production data analysis is crucial for production planning in the manufacturing industry, and accurate and a comprehensive and accurate analysis can improve production planning. To analyze the production of manufacturing industry, the research proposes the big data technology research method of the set clustering algorithm. In the process of this research method, the K-means clustering algorithm is first used to build the production measurement data system, and the Apache Hadoop Big data framework is applied. Then it introduces the Apriori algorithm for data association mining, and finally uses the genetic algorithm for production scheduling in view of big data analysis. The experiment showcases that the research method achieved a support level of 0.002 with 729 association rules when testing the number of association rules. When conducting data throughput testing, the research method achieved a data throughput of 7789 threads/s when the number of threads reached 8 in the Sleep scenario. In the analysis error testing, the error rate of the research method in the retained data fluctuates around 3.1%. When testing the number of processing operations in the process, the maximum error in the analysis results of the processing operations in the research method is 2. The results indicate that the research method possesses exceptional computational performance, carrying out manufacturing production analysis effectively and efficiently. The manufacturing production analysis system designed by the research institute offers valuable reference solutions for the informationization and intelligent development of the manufacturing industry.

Downloads

Download data is not yet available.

References

Jamwal, A., Agrawal, R., Sharma, M., & Kumar, V. (2021). Review on multi-criteria decision analysis in sustainable manufacturing decision making. International Journal of Sustainable Engineering, 14(3), 202-225. https://doi.org/10.1080/19397038.2020.1866708.

Calzavara, M., Battini, D., Bogataj, D., Sgarbossa, F., & Zennaro, I. (2020). Ageing workforce management in manufacturing systems: state of the art and future research agenda. International Journal of Production Research, 58(3), 729-747. https://doi.org/10.1080/00207543.2019.1600759.

Babubudjnauth, A., & Seetanah, B. (2021). An empirical analysis of the impacts of real exchange rate on GDP, manufacturing output, and services sector in Mauritius. International Journal of Finance & Economics, 26(2), 1657-1669. https://doi.org/10.1002/ijfe.1869.

Chen, D. J. I. Z. (2021). Automatic vehicle license plate detection using K-means clustering algorithm and CNN. Journal of Electrical Engineering and Automation, 3(1), 15-23. https://doi.org/10.36548/jeea.2021.1.002.

Meena, N., & Singh, B. (2021). Firefly optimization based hierarchical clustering algorithm in wireless sensor network. Journal of Discrete Mathematical Sciences and Cryptography, 24(6), 1717-1725. https://doi.org/10.1080/09720529.2021.1880146.

Silva, A. F., Marins, F. A. S., Dias, E. X., & Ushizima, C. A. (2021). Improving manufacturing cycle efficiency through new multiple criteria data envelopment analysis models: An application in green and lean manufacturing processes. Production Planning & Control, 32(2), 104-120. https://doi.org/10.1080/09537287.2020.1713413.

Wang, W., Zhang, Y., Gu, J., & Wang, J. (2021). A proactive manufacturing resources assignment method based on production performance prediction for the smart factory. IEEE Transactions on Industrial Informatics, 18(1), 46-55. https://doi.org/10.1109/TII.2021.3073404.

Paul, S. K., & Chowdhury, P. (2021). A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19. International Journal of Physical Distribution & Logistics Management, 51(2), 104-125. https://doi.org/10.1108/IJPDLM-04-2020-0127.

Zhou, G., Zhang, C., Li, Z., Ding, K., & Wang, C. (2020). Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. International Journal of Production Research, 58(4), 1034-1051. https://doi.org/10.1080/00207543.2019.1607978.

Oliveira, E., Miguéis, V. L., & Borges, J. L. (2022). On the influence of overlap in automatic root cause analysis in manufacturing. International Journal of Production Research, 60(21), 6491-6507. https://doi.org/10.1080/00207543.2021.1992680.

Wang, H. Y., Wang, J. S., & Zhu, L. F. (2021). A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation. Journal of Intelligent & Fuzzy Systems, 40(6), 12411-12432. https://doi.org/10.3233/JIFS-210555.

Tang, C., Wang, H., Wang, Z., Zeng, X., Yan, H., & Xiao, Y. (2021). An improved OPTICS clustering algorithm for discovering clusters with uneven densities. Intelligent Data Analysis, 25(6), 1453-1471. https://doi.org/10.3233/IDA-205497.

Dalmaijer, E. S., Nord, C. L., & Astle, D. E. (2022). Statistical power for cluster analysis. BMC Bioinformatics, 23(1), 1-28. https://doi.org/10.1186/s12859-022-04675-1.

Zhang, Y., Zhang, Y., & Zhang, R. (2020). Text information classification method based on secondly fuzzy clustering algorithm. Journal of Intelligent & Fuzzy Systems, 38(6), 7743-7754. https://doi.org/10.3233/JIFS-179844.

Yang, Z., Xu, P., Yang, Y., & Kang, B. (2021). Noise robust intuitionistic fuzzy c-means clustering algorithm incorporating local information. IET Image Processing, 15(3), 805-817. https://doi.org/10.1049/ipr2.12064.

Calzavara, M., Battini, D., Bogataj, D., Sgarbossa, F., & Zennaro, I. (2020). Ageing workforce management in manufacturing systems: state of the art and future research agenda. International Journal of Production Research, 58(3), 729-747. https://doi.org/10.1080/00207543.2019.1600759.

Barma, M., & Modibbo, U. M. (2022). Multiobjective mathematical optimization model for municipal solid waste management with economic analysis of reuse/recycling recovered waste materials. Journal of Computational and Cognitive Engineering, 1(3), 122-137. https://doi.org/10.47852/bonviewJCCE149145.

Ghazal, T. M. (2021). Performances of K-means clustering algorithm with different distance metrics. Intelligent Automation & Soft Computing, 30(2), 735-742. https://doi.org/10.32604/iasc.2021.019067.

Kaur, A., & Kumar, Y. (2022). A multi-objective vibrating particle system algorithm for data clustering. Pattern Analysis and Applications, 25(1), 209-239. https://doi.org/10.1007/s10044-021-01052-1.

Arun, R., & Balamurugan, R. (2020). Distributed Entropy energy-efficient clustering algorithm for cluster head selection (DEEEC). Journal of Intelligent & Fuzzy Systems, 39(6), 8139-8147. https://doi.org/10.3233/JIFS-189135.

Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023). Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artificial Intelligence Review, 56(8), 8219-8264. https://doi.org/10.1007/s10462-022-10366-3.

Gao, X., Zhang, Y., Wang, H., Sun, Y., Zhao, F., & Zhang, X. (2023). A modified fuzzy clustering algorithm based on dynamic relatedness model for image segmentation. The Visual Computer, 39(4), 1583-1596. https://doi.org/10.1007/s00371-022-02430-4.

Sabir, Z., Ali, M. R., & Sadat, R. (2023). Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8913-8922. https://doi.org/10.1007/s00371-022-02430-4.

Gülmez, B. (2023). A novel deep neural network model based on Xception and genetic algorithm for detection of COVID-19 from X-ray images. Annals of Operations Research, 328(1), 617-641. https://doi.org/10.1007/s10479-022-05151-y.

Pajak, M., Brus, G., & Szmyd, J. S. (2023). Genetic algorithm-based strategy for the steam reformer optimization. International Journal of Hydrogen Energy, 48(31), 11652-11665. https://doi.org/10.1016/j.ijhydene.2021.10.046.

Chen, X., Ding, Y., Cory, C. A., Hu, Y., Wu, K. J., & Feng, X. (2021). A decision support model for subcontractor selection using a hybrid approach of QFD and AHP-improved grey correlation analysis. Engineering, Construction and Architectural Management, 28(6), 1780-1806. https://doi.org/10.1108/ECAM-12-2019-0715.