• Three-stage SAR open routing framework via post-earthquake environment simulation • Priority-aware hybrid heuristic for generating high-quality initial rescue plans • M-D-ALNS with novel operators and dynamic logic to minimize time and priority cost • Event-driven re-planning mechanism enabling agile response to uncertain information • Simulation shows robust dynamics; realistic strategy can beat God View baseline Search and Rescue (SAR) path planning is critical in post-disaster scenarios with road damage and noisy demand information. This paper proposes a dynamic post-disaster SAR path planning model that comprehensively considers road damage severity, casualty uncertainty, and life-saving priority. A three-stage decision-making framework is developed: first, an improved K-means clustering algorithm based on silhouette analysis determines the optimal number of rescue clusters and establishes an unstructured post-disaster environment model. Second, SAR path pre-planning is implemented; a hybrid heuristic integrating priority greedy hierarchical allocation, priority-aware nearest neighbor heuristic and constraint-preserving 2-opt algorithm generates high-quality initial solutions, and an improved dynamic adaptive large neighborhood search (M-D-ALNS) algorithm constructs pre-planned SAR routes with minimal priority violations and shortest completion time. Third, a bidirectional event-driven response mechanism (BEDRM-LG) is proposed to mitigate discrepancies between pre-planned results and actual rescue conditions. Experimental validation on modified Solomon benchmark instances shows the proposed initialization strategy generates optimal initial solutions fastest; M-D-ALNS reduces violation rate to near zero with over 90% optimization efficiency; BEDRM-LG outperforms common benchmarks and even reaches the theoretical upper bound, demonstrating superior performance over static planning and continuous full-information re-optimization in post-disaster SAR tasks.
Feng et al. (Fri,) studied this question.