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Production adjusting method based on the predicted distribution of production and inventory using a dynamic Bayesian network

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Abstract

In general, production quantities and goods delivered are changed randomly, and then the total stock is also changed randomly. This article deals with the production and inventory control of an automobile production parts line using a dynamic Bayesian network. A Bayesian network indicates the quantitative relations between individual variables by conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule for the production quantities to maintain the probability of the lower and upper bound values of the total stock at certain values is shown.

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Author information

Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan

    Yeong-Hwa Park, Ji-Sun Shin, Ki-Yun Woo & Hee-Hyol Lee

  2. Fukuoka Institute of Technology, Fukuoka, Japan

    Fumihiro Shoji

Authors
  1. Yeong-Hwa Park
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  2. Ji-Sun Shin
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  3. Ki-Yun Woo
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  4. Fumihiro Shoji
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  5. Hee-Hyol Lee
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Corresponding author

Correspondence to Yeong-Hwa Park.

Additional information

This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009

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Park, YH., Shin, JS., Woo, KY. et al. Production adjusting method based on the predicted distribution of production and inventory using a dynamic Bayesian network. Artif Life Robotics 14, 138–143 (2009). https://doi.org/10.1007/s10015-009-0727-5

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  • DOI: https://doi.org/10.1007/s10015-009-0727-5

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