A robust two-stage transit-based evacuation model for large-scale disaster response

Abstract

After a natural or man-made large-scale disaster occurs, it is a great danger to the residents who are living in the affected area. Evacuees in the (potential) impacted area need to be assembled at pick-up points and evacuated within the specified time by using vehicles that transport them to the safe shelters, potentially multiple times. It is necessary to consider this transit-based evacuation problem right after the occurrence of a large-scale disaster with different time windows caused by different radius to the disaster center point. As the pick-up points of assembling evacuees can greatly influence the evacuation process, it is crucial to identify the critical pick-up point locations to assemble evacuees. We decompose the problem into two stages: determination of pick-up point locations, vehicle routing and scheduling. In the first stage, the goal is to determine a set of pick-up points to assemble evacuees while minimizing the total walking time of evacuees from their locations to pick-up points. In the second stage, the aim is to allocate vehicles to safe shelters to evacuate evacuees from pick-up points to safer shelters to minimize the total transit-based evacuation time. The first-stage problem is formulated as an integer nonlinear programming model and the second-stage problem is modeled as a mixed-integer programming model. To better recognize the locations of pick-up points, a hybrid genetic algorithm (HGA) is developed. An interval/roundtrip-based routing and scheduling heuristic (IRRSH) algorithm is proposed to route and schedule the vehicles under time-window constraint. Finally, computational results are provided to demonstrate the validity and robustness of the proposed model.