### Abstract

In general, the production quantities and the delivered goods are changed randomly, and then the total stock is also changed randomly. This paper deals with the production and inventory control of an automobile production part line using the Dynamic Bayesian Network. Bayesian Network indicates the quantitative relations between the individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound value and the upper bound value of the total stock to certain values is shown.

Original language | English |
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Title of host publication | Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09 |

Pages | 63-68 |

Number of pages | 6 |

Publication status | Published - 2009 |

Event | 14th International Symposium on Artificial Life and Robotics, AROB 14th'09 - Oita Duration: 2008 Feb 5 → 2009 Feb 7 |

### Other

Other | 14th International Symposium on Artificial Life and Robotics, AROB 14th'09 |
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City | Oita |

Period | 08/2/5 → 09/2/7 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

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

### Cite this

*Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09*(pp. 63-68)

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09.*pp. 63-68, 14th International Symposium on Artificial Life and Robotics, AROB 14th'09, Oita, 08/2/5.

}

TY - GEN

T1 - Production adjusting method based on predicted distribution of production and inventory using dynamic Bayesian network

AU - Park, Yeong Hwa

AU - Shin, Ji Sun

AU - Woo, Ki Yun

AU - Shoji, Fumihiro

AU - Lee, HeeHyol

PY - 2009

Y1 - 2009

N2 - In general, the production quantities and the delivered goods are changed randomly, and then the total stock is also changed randomly. This paper deals with the production and inventory control of an automobile production part line using the Dynamic Bayesian Network. Bayesian Network indicates the quantitative relations between the individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound value and the upper bound value of the total stock to certain values is shown.

AB - In general, the production quantities and the delivered goods are changed randomly, and then the total stock is also changed randomly. This paper deals with the production and inventory control of an automobile production part line using the Dynamic Bayesian Network. Bayesian Network indicates the quantitative relations between the individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound value and the upper bound value of the total stock to certain values is shown.

KW - Delivery data

KW - Dynamic Bayesian network

KW - Predicted distribution

KW - Production adjusting method

UR - http://www.scopus.com/inward/record.url?scp=78149336684&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78149336684&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9784990288037

SP - 63

EP - 68

BT - Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09

ER -