A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect

Dynamic Bayesian network (DBN) theory provides a valid tool to estimate the risk of disruptions, propagating along the supply chain (SC), i.e. the ripple effect. However, in cases of data scarcity, obtaining perfect information on probability distributions required by the DBN is impractical. To overcome this difficulty, a new robust DBN approach is, for the first time, proposed in this study to analyse the worst-case oriented disruption propagation in the SC. This work considers an SC with multiple suppliers and one manufacturer over several time periods, in which only probability intervals of the suppliers' states and those of the related disruption propagations are known. The objective is to acquire the robust performance of risk estimation, measured by the worst-case probability in the disrupted state for the manufacturer. We first establish a nonlinear programming formulation to mathematically materialise the proposed robust DBN, which can be used to solve small-size problems. To overcome the computational difficulty in solving large-size problems, an efficient simulated annealing algorithm is further designed. Numerical experiments are conducted to validate its efficiency.