Uncertainty-cognizant post-disaster routing with progressive hedging centered multi meta-heuristic approach

Post-disaster routing is crucial for timely relief delivery, medical aid, rescue, evacuation services, and utility restoration. Routing in post-disaster scenarios is complex due to uncertainty from road network damage and unpredictable resource demands. Factors such as physical obstacles, logistical complexities, operational coordination issues, temporal constraints, and ethical considerations require the development of adaptive routing strategies. These challenges require constant re-assessment and adaptation of routing plans. The study proposes a post-disaster routing solution using a stochastic graph representation of road networks, incorporating probabilistic passability and demand variability. It employs a Progressive-Hedging (PH) centered multimetaheuristic approach including Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, and Tabu Search to enhance computational efficiency and resource allocation. The proposed approach significantly reduces travel cost (distance), outperforming benchmark algorithms across test networks. The developed methodology also considers robustness in the solutions through an explicit function. In addition, we provided a post-analysis of the resilience score computed with hypothetical cost assumptions.