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