### 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 language | English |
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Title of host publication | Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 |

Pages | 405-410 |

Number of pages | 6 |

Publication status | Published - 2008 Dec 1 |

Event | 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, Japan Duration: 2008 Jan 31 → 2008 Feb 2 |

### Publication series

Name | Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 |
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### Conference

Conference | 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 |
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Country | Japan |

City | Oita |

Period | 08/1/31 → 08/2/2 |

### Keywords

- Dynamic Bayesian network
- Graphical modeling
- Probability distribution
- Production inventory control

### ASJC Scopus subject areas

- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction

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## Cite this

*Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08*(pp. 405-410). (Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08).