A Spatiotemporal Graph-Based Robust Optimization Framework for Data-Driven Disaster Recovery Scheduling

Abstract

Scheduling heterogeneous resources for post-disaster recovery remains a critical challenge, as existing approaches predominantly rely on deterministic models or static heuristics that fail to capture the dynamic, uncertain, and spatially heterogeneous nature of real disaster environments.} This study introduces STGR-DRS, a Spatiotemporal Graph-based Robust Disaster Recovery Scheduling framework that utilizes large-scale, multi-source open data to coordinate heterogeneous resources, including repair crews, medical teams, transportation units, and shelter provisions. The framework comprises four stages: (1) a multi-source data fusion pipeline that constructs a dynamic spatiotemporal recovery graph using OpenStreetMap, Humanitarian Data Exchange, Shelter Cluster, and Copernicus datasets; (2) a gradient boosting-based demand urgency estimation module with SHAP interpretability; (3) a multi-objective robust mixed-integer linear programming formulation that jointly minimizes recovery time, transportation cost, and unmet demand while enforcing fairness constraints; and (4) a rolling-horizon re-optimization mechanism that updates schedules dynamically in response to new disruptions. Extensive experiments on the 2023 T\"{u}rkiye--Syria earthquake case study demonstrate that STGR-DRS reduces total recovery time by 13.4\% compared to the best-performing baseline under nominal conditions and achieves a robustness advantage of 15.2\% under severe disruption scenarios. Ablation studies illustrate the contribution of each framework component, and scalability analysis confirms computational tractability for networks with more than 5,000 nodes. \textcolor{blue}{Key limitations include the reliance on centralized coordination, which may be challenged by communication failures, and the use of synthetic disruption scenarios that may not fully replicate real-time operational complexity. Future work should explore decentralized multi-agent scheduling, integration of predictive aftershock models, and field validation with humanitarian organizations.} The framework offers actionable decision support for emergency management agencies and provides a reproducible open-data experimental pipeline for the disaster recovery scheduling research community.