A New Robust Dynamic Bayesian Network Model with Bounded Deviation Budget for Disruption Risk Evaluation
IFIP Advances in Information and Communication Technology, ISSN: 1868-422X, Vol: 632 IFIP, Page: 681-688
2021
- 3Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85115346312&origin=inward; http://dx.doi.org/10.1007/978-3-030-85906-0_74; https://link.springer.com/10.1007/978-3-030-85906-0_74; https://dx.doi.org/10.1007/978-3-030-85906-0_74; https://link.springer.com/chapter/10.1007/978-3-030-85906-0_74
Springer Science and Business Media LLC
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