Data- and Model-Driven Crude Oil Supply Risk Assessment of China Considering Maritime Transportation Factors

Abstract

Effective supply-chain risk assessment is the basis for developing sustainable supply policies, and it has received growing attention in global oil supply system management. Dynamical modeling and data-driven modeling are two main risk assessment technologies that have been applied in crude oil supply networks. Dynamical risk modeling and data-driven risk modeling offer distinct advantages in capturing the complexities and dynamics of the system. Considering their complementary strengths, a hybrid modeling framework combining system dynamics and data-driven neural networks is proposed for risk assessment of crude oil transportation network. Specifically, the system dynamics module is to capture and interpret the underlying dynamics and mechanisms of the transportation network, while the deep neural networks module is to discover the nonlinear patterns and dependencies of risk factors from various inputs. Based on joint training, the hybrid model can ultimately develop the capability of risk prediction with a small amount of data. In addition, it can consider the dynamic nature of crude oil transportation networks to interpret the predicted results of the risk level for decision-makers to make specific risk-mitigating policies. Extensive experiments based on China’s scenario have been conducted to demonstrate the effectiveness of the proposed hybrid model, and the results show that our model achieves higher accuracy in risk prediction compared to the current state of the art. The results also present an explanation for China’s policy change of building a resilient crude oil transportation system.