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Analysis of production cycle-time distribution with a big-data approach

Author

Listed:
  • Xu Tan

    (Central South University of Forestry and Technology
    Shenzhen Institute of Information Technology)

  • Lining Xing

    (Central South University of Forestry and Technology
    Shenzhen Institute of Information Technology)

  • Zhaoquan Cai

    (Huizhou University)

  • Gaige Wang

    (Ocean University of China)

Abstract

In production planning, one of the most crucial issues involves cycle time forecasting and distribution. Particularly, the parameter aids in realizing high delivery reliability. In the production planning process that involves computer component manufacturing, an estimation of the tasks’ cycle time offers an important basis for dispatching control, material purchase, and due date assignment. In this study, a big-data approach was proposed and examined to determine how it could be used to predict cycle time distribution. Also, the research context involved computer components manufacturing systems. Indeed, the motivation was to determine how the proposed mechanism could improve delivery reliability in manufacturing systems.Regarding the implementation and design of the CT forecasting system, with the proposed DP-RBFN framework being a model to be implemented in computer components manufacturing, components of the system constituted three major parts. The first part, being the basic platform, played the role of Hadoop series software installation. This installation had its role lie in enabling the parallel computing of big data. Another part of the framework design and implementation involved data preprocessing. In this case, the role of the data preprocessing procedure lay in the extraction, transformation, and loading of data to CTF. The third part that followed the basic platform design and data preprocessing procedure involved CT forecasting. Results demonstrated that the proposed model performs superiorly than the contrast or other comparative methods on both the computer components manufacturing system dataset and benchmark datasets. From the findings, the proposed framework (DP-RBFN) exhibited superior performance compared to previous performance outcomes that had been reported relative to the use of the RBFN algorithm. These findings held for both MAD and SD—relative to the selected datasets.

Suggested Citation

  • Xu Tan & Lining Xing & Zhaoquan Cai & Gaige Wang, 2020. "Analysis of production cycle-time distribution with a big-data approach," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1889-1897, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01544-7
    DOI: 10.1007/s10845-020-01544-7
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
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    4. Teng Ren & Helu Xiao & Zhongbao Zhou & Xinguang Zhang & Lining Xing & Zhongwei Wang & Yujun Cui, 2019. "The Iterative Scheme and the Convergence Analysis of Unique Solution for a Singular Fractional Differential Equation from the Eco-Economic Complex System’s Co-Evolution Process," Complexity, Hindawi, vol. 2019, pages 1-15, September.
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    Cited by:

    1. Zilong Zhuang & Liangxun Guo & Zizhao Huang & Yanning Sun & Wei Qin & Zhao-Hui Sun, 2021. "DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2197-2207, December.
    2. Yu-Cheng Wang & Horng-Ren Tsai & Toly Chen, 2021. "A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication," Mathematics, MDPI, vol. 9(12), pages 1-18, June.

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