Stochastic model of production and inventory control using dynamic Bayesian network

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08
ページ405-410
ページ数6
出版ステータスPublished - 2008 12 1
イベント13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, Japan
継続期間: 2008 1 312008 2 2

出版物シリーズ

名前Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

Conference

Conference13th International Symposium on Artificial Life and Robotics, AROB 13th'08
国/地域Japan
CityOita
Period08/1/3108/2/2

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用

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