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.
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References
Pearl J (1988) Probabilistic reasoning in intelligent systems, Morgan Kaufmann
Motomura Y, Akaho S, Aso F (1999) Appllication of bayesian network to inteligent system. J SICE 38(7):468–473
Siver EA, et al (1998) Inventory managemaent and production planning and scheduling, John Wiley
Seferlis P, Giannelos NF (2004) A tow-lauered optimization-based control strategy for multi-echelon supply chain networks. Computers Chemical Eng 28:799–809
Suguro T, Kuroda M (2004) Safety stock and reorder pont for reordering pont system with variable lead times. J Jpn Inustrial Management Assoc 55:89–94
Neapolitan RE (2003) Learning bayesian networks, Prentice Hall
Biedermann A, Taron F (2006) Bayesian networks and probabilistic reasoning about scientifi c evidence when there is a lack of data. Forensic Science International 157:163–167
Lauritzen S, Spiegelhalter D (1988) Local computations with probabilities on graphica structure and their application to expert systems. J Royal Statistical Soc B 50:157–224
Kao H-Y, Huang S-H, Li H-L (2005) Supply chain diagnostics with dynamic Bayesian Networks. Computers Industrial Eng 49:339–347
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This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008
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Shin, JS., Lee, TH., Kim, JI. et al. Stochastic model of production and inventory control using dynamic bayesian network. Artif Life Robotics 13, 148–154 (2008). https://doi.org/10.1007/s10015-008-0581-x
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DOI: https://doi.org/10.1007/s10015-008-0581-x