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A General Robust Dynamic Bayesian Network Method for Supply Chain Disruption Risk Estimation under Ripple Effect

Abstract : Robust dynamic Bayesian network (DBN) is a valid tool for disruption propagation estimation in the supply chain under data scarcity. However, one of assumptions in robust DBN is that the Markov transition matrix is fixed and fully known, which is unpractical. To make up this deficiency, a novel and general robust DBN is, for the first time, proposed in this work to assess the worst-case oriented supply chain disruption risk under ripple effect. The study focuses on a supply chain with multiple suppliers and one manufacturer over a time horizon, in which only probability intervals of related probabilities are known. The objective is to obtain the worst-case supply chain disruption risk, measured by the probability of the manufacturer in the fully disrupted state in the final time period. For the problem, a new and general nonconvex programming model is established. Then, a case study is conducted to compare our approach with the classic DBN and robust DBN in the literature.
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Conference papers
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https://hal-univ-evry.archives-ouvertes.fr/hal-03723062
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Submitted on : Wednesday, July 13, 2022 - 10:48:20 PM
Last modification on : Friday, July 15, 2022 - 3:46:20 AM

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  • HAL Id : hal-03723062, version 1

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Ming Liu, Tao Lin, Feng Chu, Feifeng Zheng, Chengbin Chu. A General Robust Dynamic Bayesian Network Method for Supply Chain Disruption Risk Estimation under Ripple Effect. 10th IFAC Conference on Manufacturing Modelling, Management and Control (MIM 2022), Jun 2022, Nantes, France. ⟨hal-03723062⟩

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