Memetic Optimization of Collaborative Human-UAV-Truck Search-and-Rescue Task Scheduling in Earthquakes

Unmanned aerial vehicles (UAVs) have become increasingly used in search and rescue tasks in disasters. However, there are many tasks that cannot be completed solely by UAVs; instead, they should be completed by cooperation of UAVs and human rescuers. Moreover, as battery capacity is a major limiting factor in UAV operations, using ground vehicles (trucks) to provide mobile battery swapping services is an efficient approach. To this end, this paper studies a problem of collaborative human-UAV-truck search-and-rescue task scheduling in earthquakes, where UAVs are used to quickly search for survivors who are expected to be ultimately rescued by human rescuers, while a truck serves as a mobile battery depot for UAVs, such that the survivors can be rescued as many as possible and as early as possible. To solve the problem, we propose a memetic algorithm that combines the ecogeography-based optimization metaheuristic for global exploration, adaptive local search for improving solution accuracy, and a Gaussian binary simulated annealing method for truck path planning. Numerical experiments on a set of real-world problem instances demonstrate the effectiveness and efficiency of the proposed method.