Abstract
Dynamic Bayesian network (DBN), combining with probability intervals, is a valid tool to estimate the risk of disruptions propagating along the supply chain (SC) under data scarcity. However, since the approach evaluate the risk from the worst-case perspective, the obtained result may be too conservative for some decision makers. To overcome this difficulty, a new robust DBN model, considering bounded deviation budget, is first time to be developed to analyse the disruption risk properly. We first formulate a new robust DBN optimization model with bounded deviation budget. Then a linearization technique is applied to linearize the nonlinear bounded deviation budget constraint. Finally, a case study is conducted to demonstrate the applicability of the proposed model and some managerial insights are drawn.
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Acknowledgments
The authors are grateful for the valuable comments from the reviewers. This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 72021002, 71972146, 71771048, 71432007, 71832001 and 72071144.
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Liu, M., Lin, T., Chu, F., Zheng, F., Chu, C. (2021). A New Robust Dynamic Bayesian Network Model with Bounded Deviation Budget for Disruption Risk Evaluation. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 632. Springer, Cham. https://doi.org/10.1007/978-3-030-85906-0_74
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DOI: https://doi.org/10.1007/978-3-030-85906-0_74
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