Stochastic model of production and inventory control using dynamic bayesian network

Ji Sun Shin, Tae Hong Lee, Jin Il Kim, Hee Hyol Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.

Original languageEnglish
Pages (from-to)148-154
Number of pages7
JournalArtificial Life and Robotics
Volume13
Issue number1
DOIs
Publication statusPublished - 2008 Dec 1

Keywords

  • Dynamic Bayesian Network
  • Graphical Modeling
  • Probability distribution
  • Production inventory control

ASJC Scopus subject areas

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

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