Evaluating supply chain (SC) disruption risks in the context of data scarcity and ripple effects is essential because uncertain disruptions can propagate throughout the SC, resulting in negative impacts on SC performance. Considering that different decision-makers have varying risk tolerances, this can affect the expected disruption risk assessment results. To tackle the problem, this paper proposes a methodology that employs probability intervals to capture uncertain parameters, utilizes dynamic Bayesian networks (DBNs) to model risk propagation, and incorporates risk deviation variables to quantify decision-makers' risk tolerance. Subsequently, a bi-objective optimization model is developed to evaluate the optimal Pareto front with respect to both SC disruption risk and deviation budget. For solving the studied problem, the linearisation and ϵ-constraint methods are developed. To demonstrate the feasibility and effectiveness of the proposed model, the numerical experiment is carried out, and the results are analysed to draw managerial insights.