Supply chain diagnostics with dynamic Bayesian networks

Han Ying Kao*, Chia Hui Huang, Han-Lin Li

*此作品的通信作者

    研究成果: Article同行評審

    43 引文 斯高帕斯(Scopus)

    摘要

    This paper proposes a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology developed by Naim, Childerhouse, Disney, and Towill (2002). [A supply chain diagnostic methodlogy: Determing the vector of change. Computers and Industrial Engineering, 43, 135-157], a dynamic Bayesian network is employed as a more descriptive mechanism to model the causal relationships in the supply chain. Dynamic Bayesian networks can be utilized as a knowledge base of the reasoning systems where the diagnostic tasks are conducted. We finally solve this reasoning problem with stochastic simulation.

    原文English
    頁(從 - 到)339-347
    頁數9
    期刊Computers and Industrial Engineering
    49
    發行號2
    DOIs
    出版狀態Published - 1 九月 2005

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