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The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems

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  • Lin, Yao-San
  • Li, Der-Chiang

Abstract

The statistical theories are not expected to generate significant conclusions, when applied to very small data sets. Knowledge derived from limited data gathered in the early stages is considered too fragile for long term production decisions. Unfortunately, this work is necessary in the competitive industry and business environments. Our previous researches have been aimed at learning from small data sets for scheduling flexible manufacturing systems, and this article will focus development of an incremental learning procedure for small sequential data sets. The main consideration concentrates on two properties of data: that the data size is very small and the data are time-dependent. For this reason, we propose an extended algorithm named the Generalized-Trend-Diffusion (GTD) method, based on fuzzy theories, developing a unique backward tracking process for exploring predictive information through the strategy of shadow data generation. The extra information extracted from the shadow data has proven useful in accelerating the learning task and dynamically correcting the derived knowledge in a concurrent fashion.

Suggested Citation

  • Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:1:p:121-130
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    References listed on IDEAS

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    Cited by:

    1. Che-Jung Chang & Liping Yu & Peng Jin, 2016. "A mega-trend-diffusion grey forecasting model for short-term manufacturing demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1439-1445, December.
    2. Li, Der-Chiang & Lin, Liang-Sian, 2013. "A new approach to assess product lifetime performance for small data sets," European Journal of Operational Research, Elsevier, vol. 230(2), pages 290-298.
    3. Der-Chiang Li & Chun-Wu Yeh & Chieh-Chih Chen & Hung-Ta Shih, 2016. "Using a diffusion wavelet neural network for short-term time series learning in the wafer level chip scale package process," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1261-1272, December.

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