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Conference Papers Year : 2021

A New Robust Dynamic Bayesian Network Model with Bounded Deviation Budget for Disruption Risk Evaluation

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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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Dates and versions

hal-03360792 , version 1 (01-10-2021)

Identifiers

Cite

Ming Liu, Tao Lin, Feng Chu, Feifeng Zheng, Chengbin Chu. A New Robust Dynamic Bayesian Network Model with Bounded Deviation Budget for Disruption Risk Evaluation. International Conference on Advances in Production Management Systems (APMS 2021), Sep 2021, Nantes, France. pp.681--688, ⟨10.1007/978-3-030-85906-0_74⟩. ⟨hal-03360792⟩
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