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 |
|---|---|
| Pages (from-to) | 148-154 |
| Number of pages | 7 |
| Journal | Artificial Life and Robotics |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2008 |
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Keywords
- Dynamic Bayesian Network
- Graphical Modeling
- Probability distribution
- Production inventory control
ASJC Scopus subject areas
- Artificial Intelligence
- Biochemistry, Genetics and Molecular Biology(all)
Cite this
Stochastic model of production and inventory control using dynamic bayesian network. / Shin, Ji Sun; Lee, Tae Hong; Kim, Jin Il; Lee, HeeHyol.
In: Artificial Life and Robotics, Vol. 13, No. 1, 2008, p. 148-154.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Stochastic model of production and inventory control using dynamic bayesian network
AU - Shin, Ji Sun
AU - Lee, Tae Hong
AU - Kim, Jin Il
AU - Lee, HeeHyol
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Dynamic Bayesian Network
KW - Graphical Modeling
KW - Probability distribution
KW - Production inventory control
UR - http://www.scopus.com/inward/record.url?scp=58049216458&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049216458&partnerID=8YFLogxK
U2 - 10.1007/s10015-008-0581-x
DO - 10.1007/s10015-008-0581-x
M3 - Article
AN - SCOPUS:58049216458
VL - 13
SP - 148
EP - 154
JO - Artificial Life and Robotics
JF - Artificial Life and Robotics
SN - 1433-5298
IS - 1
ER -