Disasters cause severe economic and human losses, and delays in rescue efforts can lead to more deaths due to damaged infrastructure, as seen in events like the Wenchuan earthquake and Hurricane Sandy. To enhance post-disaster response efficiency, this study proposes a collaborative truck-and-drone system that leverages the high capacity of trucks and the speed and flexibility of drones to overcome road accessibility challenges. While truck-drone collaborations have been studied in commercial logistics, their application in disaster relief remains understudied, particularly for simultaneous delivery and surveillance tasks, which are critical for saving lives and assessing damage. Unlike cost- or time-focused commercial logistics, humanitarian operations require prioritizing urgency, where tasks with higher mortality risks should be prioritized. This study addresses the truck-and-drone routing problems with flexible collaboration strategies for delivery and surveillance tasks to minimize the priority cost in the disaster response. First, we model the problem with deterministic travel time and rescue demand as a Mixed Integer Linear Programming. An exact method (Branch-and-Bound incorporated with Benders Decomposition) and a heuristic algorithm (simulated annealing) are developed to solve the problem. Next, we develop a robust route optimization method to address the travel time uncertainties after disasters. Furthermore, considering dynamic rescue demands in the rescue process, we model a dynamic truck-and-drone routing problem as a Markov Decision Process and solve it using a Multi-Agent Reinforcement Learning method. Numerical experiments demonstrate that the proposed algorithms can efficiently solve these complex problems and that the flexible truck-and-drone collaboration can significantly enhance rescue efficiency in disaster response.

Dr. Fangni Zhang is an Assistant Professor in the Department of Data and Systems Engineering at The University of Hong Kong (HKU). She received her B.S. in Industrial Engineering from Beihang University and Ph.D. in Transportation Engineering from The Hong Kong University of Science and Technology. Prior to joining HKU, Dr. Zhang has been a Lecturer at the University of Leeds and University of New South Wales, Sydney. Dr. Zhang’s research focuses on the economics, analytics, and optimization of multimodal transportation systems, in particular the planning, operations, and management issues of shared, automated, and electrified transportation and logistics systems. Dr. Zhang’s research mainly appears in leading international journals, including Transportation Research Part A/B/C/D/E, Transportation Science, and IEEE Transactions on Intelligent Transportation Systems. She is currently serving as the Associate Editor of Journal of Transport Economics and Policy and a member of Editorial Advisory Board of Transportation Research Part E. As the Principal Investigator, Dr. Zhang has won several competitive grants from the Research Grants Council (RGC) of Hong Kong and the National Natural Science Foundation of China (NSFC).