Production adjusting method based on the predicted distribution of production and inventory using a dynamic Bayesian network

Yeong Hwa Park; Ji Sun Shin; Ki Yun Woo; Fumihiro Shoji; Hee Hyol Lee

doi:10.1007/s10015-009-0727-5

Production adjusting method based on the predicted distribution of production and inventory using a dynamic Bayesian network

Yeong Hwa Park^*, Ji Sun Shin, Ki Yun Woo, Fumihiro Shoji, Hee Hyol Lee

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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

Original language	English
Pages (from-to)	138-143
Number of pages	6
Journal	Artificial Life and Robotics
Volume	14
Issue number	2
DOIs	https://doi.org/10.1007/s10015-009-0727-5
Publication status	Published - 2009 Nov 1

Keywords

Delivery data
Dynamic Bayesian network
Predicted distribution
Production adjusting method

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Artificial Intelligence

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10.1007/s10015-009-0727-5

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abstract = "In general, production quantities and goods delivered are changed randomly, and then the total stock is also changed randomly. This article deals with the production and inventory control of an automobile production parts line using a dynamic Bayesian network. A Bayesian network indicates the quantitative relations between individual variables by conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule for the production quantities to maintain the probability of the lower and upper bound values of the total stock at certain values is shown.",

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AU - Park, Yeong Hwa

AU - Shin, Ji Sun

AU - Woo, Ki Yun

AU - Shoji, Fumihiro

AU - Lee, Hee Hyol

PY - 2009/11/1

Y1 - 2009/11/1

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

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

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KW - Predicted distribution

KW - Production adjusting method

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Production adjusting method based on the predicted distribution of production and inventory using a dynamic Bayesian network

Abstract

Keywords

ASJC Scopus subject areas

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