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

Yeong Hwa Park, Ji Sun Shin, Ki Yun Woo, Fumihiro Shoji, HeeHyol Lee

研究成果: Article

1 引用 (Scopus)

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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.

元の言語English
ページ(範囲)138-143
ページ数6
ジャーナルArtificial Life and Robotics
14
発行部数2
DOI
出版物ステータスPublished - 2009 11

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Biochemistry, Genetics and Molecular Biology(all)

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