Ground vehicle and UAV collaborative routing and scheduling for humanitarian logistics using random walk based ant colony optimization

A well-planned humanitarian logistics aiming to rescue people and provide on-time lifesaving facilities in disaster-affected areas can significantly mitigate the repercussions of disasters. However, damaged bridges and blocked roads can hinder last-mile deliveries in disaster-affected areas to ground vehicles only. In this regard, the present study attempts to propose Ground Vehicle (GV) and Unmanned Air Vehicle (UAV) collaborative delivery system to be implemented in such areas. To this end, a fleet of homogenous ground vehicles, each equipped with a certain number of UAVs, was deployed for last-mile deliveries. UAVs make the flight from GVs, deliver to the end locations, and return to the GV for battery replacement and/or start another flight. The main objective of the proposed model is to minimize the total delivery time within UAV flight endurance and payload constraints. First, K-means algorithm was used to cluster the disaster-affected region into different sectors. Then, GV-touring and UAV-routing were scheduled using the nearest neighbor heuristics to serve the ground approachable locations and UAV served locations, respectively. Finally, the levy fiight-based Ant Colony Optimization-based (ACS RW) algorithm was developed to further optimize the overall travel time. Experimentation results show the potential superiority of the proposed algorithm over other available truck-drone collaborative transportation models. (C) 2022 Sharif University of Technology. All rights reserved.