Multiple drone-vehicle parallel scheduling optimization in humanitarian logistics

This paper studies the multiple drone-vehicle parallel scheduling optimization problem in humanitarian logistics, introducing constraints for multiple drones, multiple vehicles, and multiple trips. Both drones and vehicles have capacity and endurance limitations, and drones can visit multiple areas in a single trip. We construct a mixed-integer linear programming model based on these characteristics, considering the effects of undeliverable nodes, dominated paths, and node priorities. Due to the scale and complexity of the problem, a fast feasible strategy was designed to obtain a feasible solution in a very short time. At the same time, a heuristic algorithm based on best interpolation was proposed, combined with multiple perturbation operations to improve overall optimization efficiency. Through the comparison of results from multiple case studies, the advantages of our proposed method over the benchmark algorithm were verified. Further tests were conducted on the impact of the number of transport vehicles and the delivery range of drones, determining the marginal benefits of increasing the number of drones and vehicles, as well as the balancing relationship in relief missions. Finally, a set of real-life cases in Shanghai, China, was analyzed to examine the uncertainties in restricted areas, delivery priorities, and the realization of rescue for undeliverable nodes, and management insights were provided regarding capacity and endurance configuration.