Modeling Supply Chain Diagnostics with Fuzzy Dynamic Bayesian Networks

Authors

  • Han-Ying Kao National Dong Hwa University
  • Chia-Hui Huang Kainan University

DOI:

https://doi.org/10.23055/ijietap.2008.15.3.140

Keywords:

Fuzzy dynamic Bayesian networks, supply chain diagnostics, simulation

Abstract

Bayesian networks have been widely used as knowledge bases under uncertainty. However, in previous works, the uncertainty measure in Bayesian networks are usually probability distributions for crisp variables, which restricts the practical usefulness when incomplete knowledge or linguistic vagueness is involved in reasoning systems. This study develops a fuzzy dynamic Bayesian network (FDBN) in which fuzzy variables as well as crisp variables are considered. The proposed fuzzy dynamic Bayesian network is applied to supply chain modeling and reasoning. The simulation algorithms are designed to answer various diagnostic queries from supply chains.

Author Biographies

Han-Ying Kao, National Dong Hwa University

H

Chia-Hui Huang, Kainan University

Chia-Hui Huang is an assistant professor of the Department of Information Management at Kainan University. He has degrees of Ph.D. in Information Management, National Chiao Tung University, Taiwan and M.S. in Industrial Engineering from University of Wisconsin – Madison. He is the author/coauthor of 9 journal papers, 17 conference papers and 23 books. His publications have covered topics such as Global Optimization, Graphical Decision Model, Supply Chain Management and Network.

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Published

2022-02-25

How to Cite

Kao, H.-Y., & Huang, C.-H. (2022). Modeling Supply Chain Diagnostics with Fuzzy Dynamic Bayesian Networks. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(3), 257–265. https://doi.org/10.23055/ijietap.2008.15.3.140

Issue

Vol. 15 No. 3 (2008)

Section

Supply Chain Management

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