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

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)138-143
Number of pages6
JournalArtificial Life and Robotics
Volume14
Issue number2
DOIs
Publication statusPublished - 2009 Nov

Fingerprint

Bayesian networks
Equipment and Supplies
Epidemiologic Effect Modifiers
Automobiles
Inventory control
Production control

Keywords

  • Delivery data
  • Dynamic Bayesian network
  • Predicted distribution
  • Production adjusting method

ASJC Scopus subject areas

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

Cite this

Production adjusting method based on the predicted distribution of production and inventory using a dynamic Bayesian network. / Park, Yeong Hwa; Shin, Ji Sun; Woo, Ki Yun; Shoji, Fumihiro; Lee, HeeHyol.

In: Artificial Life and Robotics, Vol. 14, No. 2, 11.2009, p. 138-143.

Research output: Contribution to journalArticle

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