Dynamic Cross-Regional Scheduling of Healthcare Staff during Infectious Disease Outbreaks: A Multi-Objective Robust Optimization Model

Global public health emergencies, particularly, infectious disease outbreaks, are occurring with increasing frequency and, imposing multifaceted challenges on healthcare systems, including staff-scheduling imbalances, elevated infection risks, and substantial uncertainty in patient demand. To address these challenges, a multi-objective robust optimization model for cross-regional allocation of healthcare staff is developed in this study. The model explicitly accounts for demand uncertainty and infection-risk constraints while optimizing rescue efficiency, fairness, and penalties for unmet demand. Our results demonstrate that (1) introducing cross-regional coordination markedly improves resource-allocation efficiency; (2) incorporating infection-risk constraints prevents overestimation of workforce availability and provides practical benefits; (3) scheduling policies must adapt dynamically to evolving epidemic phases and policy objectives; and (4) under high demand uncertainty, the robust model substantially improves schedule stability and reliability. By incorporating infection risk as a key decision constraint, our framework provides quantitative and actionable guidance for the adaptive scheduling of healthcare staff during complex, rapidly evolving infectious disease outbreaks.