ground vehicle and uav collaborative routing and scheduling for humanitarian logistics using random walk based ant colony optimization

A well-planned humanitarian logistics involving rescuing people and providing on-time lifesaving facilities to disaster-affected areas can significantly mitigate the aftermath of disasters. however, damaged bridges and blocked roads can hinder last-mile deliveries in disaster-affected areas by ground vehicles only. so, in this paper, we propose a ground vehicle (gv) and unmanned air vehicle (uav) collaborative delivery system in such areas. here, a fleet of homogenous ground vehicles each equipped with a certain number of uavs is deployed for last-mile deliveries. uavs make the flight from gvs, deliver to end locations and return to the gv for battery replacement and/or start another flight. the objective of the model is to minimize the total delivery time within uav flight endurance and payload constraints. firstly k-means clustering algorithm has been used to cluster the disaster-affected region into different sectors. then gv_touring and uav_routing have been scheduled using nearest neighbor heuristic to serve ground approachable locations and uav served locations respectively. finally, the random walk based ant colony optimization-based (acs_rw) has been developed to further optimize the overall travel time. experimentation results show the potential benefits of the proposed algorithm over other available truck-drone collaborative transportation models.