Abstract:

In recent years， while Unmanned Aerial Vehicles （UAVs） have played a significant role in various rescue operations， they are constrained by inherent limitations of short flight endurance. To address this， deploying a network of battery swapping stations （BSS） in post-disaster scenarios allows UAVs to effectively overcoming their range limitations. This context imparts considerable theoretical and practical significance to the study of designing UAV-based emergency rescue networks under a battery-swapping paradigm.Against this backdrop， a novel planning problem is introduced for UAV rescue networks. The problem investigates how to integrally optimize the location of BSS and the flight paths of UAVs， under conditions where BSS are subject to disruptions from secondary disasters. The objective is to maximize the number of rescue demand points a UAV can visit， with the highest possible probability， after departing from its depot. This problem is fundamentally an integration of the interdiction covering location problem， the maximal covering location problem， and the most reliable path problem， requiring simultaneous decisions on BSS locations and UAV routing.This problem is formulated as a nonlinear mixed-integer programming model. To solve the model， it is simplified by constructing a “maximal facility-connected network” and subsequently a two-stage hybrid algorithm is designed. In the first stage， an exhaustive search or a genetic algorithm is employed to search and update the location decision variables. In the second stage， the maximal facility-connected network is generated， and the optimal flight plan is then solved.Finally， the model and algorithm are validated using data from the “9/5 Luding Earthquake.” The results reveal several key findings： （1） Increasing the number of BSS， extending UAV flight endurance， and enhancing flight speed all contribute to improving the coverage of post-disaster rescue demand points. （2） Although increasing the number of BSS improves demand coverage and rescue effectiveness， it paradoxically reduces the reliability of UAV flight paths under disruption scenarios， leading to a trade-off among effectiveness， reliability， and timeliness. （3） Increasing the number of UAV depots （take-off points） can simultaneously enhance rescue effectiveness， reliability， and timeliness. This positive synergistic effect remains robust and is not adversely affected by changes in the number of BSS or UAV flight endurance.

Key words: unmanned aerial vehicle, emergency rescue, battery swapping mode, facility interruption, network design