Stochastic modeling of post-earthquake road clearance for maximizing accessibility and metaheuristic solution approaches

Disasters have had a great impact on human life throughout the 21st century in terms of loss of life, property, and economic growth. Post-disaster debris clearance is of utmost importance in disaster response and recovery. The goal in planning debris clearance operations in emergency response is to maximize road network accessibility and enable casualty transport to medical facilities, primary relief distribution to survivors, and evacuation of survivors from the affected region. An optimal debris clearance plan leads to fast access to warehouses, temporary survivor camps and medic tents/hospitals. Debris clearance is also important in the affected region's post-disaster recovery because it impacts the speed of business re-openings. Here, we develop a novel scenario-based mathematical model to represent the road network debris clearance scheduling problem with multiple cleaning crews. The goal is to maximize network accessibility throughout the clearance process of a given road network with blockages. The model's output schedule is consistent in the sense that it takes all clearing time scenarios into consideration. We ensure the reliability of any given solution by determining the required number of different clearance time estimate scenarios using the Sample Average Approximation Method (SAA). The proposed mathematical model can create schedules for small-scale networks due to the curse of dimensionality. Therefore, to deal with larger-scale networks, the designed metaheuristics include combinations or stand-alone versions of Biased Random Sampling (BRS), Tabu Search (TS), Simulated Annealing (SA), and Variable Neighborhood Search algorithms (VNS). To enable the usage of the model in practice, we also propose a rolling horizon approach to revise the initial schedule based on updated clearance time estimates received from the field.