Purpose This study aims to optimize pharmaceutical emergency logistics under dynamic demand and disrupted routes during public health crises. By integrating multi-scenario analysis and multimodal transportation, it seeks to minimize response time, unmet demand penalties, and costs while balancing efficiency and equity. The model addresses limitations of traditional single-mode logistics, leveraging COVID-19 case data to enhance adaptability in resource allocation. Design/methodology/approach A robust optimization model is developed, integrating dynamic demand forecasting, scenario probabilities, and capacity constraints across four epidemic stages. The NSGA-III algorithm is employed to solve multi-objective trade-offs, with performance compared against NSGA-II using metrics like spacing and Pareto ratio. Robust standard vectors and scenario probabilities are analyzed to evaluate stability, supported by computational experiments from Chinese cities like Wuhan. Findings NSGA-III outperformed NSGA-II, generating 60% more Pareto solutions in T4 with 3% faster computation. Robust vectors significantly influenced outcomes: γ3 increased penalty costs linearly in high-demand phases, while γ1 escalated procurement expenses over time. Scenario probabilities p3 reduced penalties by 15–20% through coordinated logistics. Practical implications The framework enables emergency managers to prioritize air transport for urgent deliveries and establish centralized hubs, reducing average response times by 18%. Public-private partnerships and dynamic inventory adjustments improve equity and efficiency, particularly in high-risk regions. Originality/value This study contributes to the field by unifying dynamic demand modelling, multimodal transport optimization, and robust scenario-based decision-making into a single analytical framework. The application of NSGA-III effectively resolves many-objective optimization challenges, outperforming traditional methods in both diversity and convergence. A scenario-driven parameter analysis is introduced to quantitatively assess the impacts of uncertainty, thereby advancing theory in crisis logistics management.
Yang et al. (Sat,) studied this question.