Humanitarian relief network assessment using collaborative truck-and-drone system

The increasing number and severity of natural and man-made disasters worldwide has led to calls for more precise and effective humanitarian responses, and the use of humanitarian relief network assessment to reduce disaster uncertainty can play a vital role in the delivery of precise humanitarian operations. In this study, a collaborative truck-and-drone system was developed as a post-disaster assessment tool for use by humanitarian relief networks. The proposed system comprises a drone equipped with a camera that can launch from a truck to collect information from both nodes and links of a post-disaster transportation network. Following drone operation, the truck is used to retrieve and recharge the drone’s battery. To optimize this collaborative truck-and-drone system, we focused on the routing problem with the objective of maximizing the value of information collected from nodes and links within a predefined time limit, a problem made challenging by the need to determine the routes of the truck and drone in an integrated manner. To the best of our knowledge, this study was the first to consider the problem of collaborative truck-and-drone routing optimization with the goal of profit maximization. After formulating the proposed problem as a mixed-integer linear programming (MILP) model, we decomposed the problem structure into a path-based master problem and two sub-problems to allow the use of a column generation (CG) framework to tackle the problem. Numerical experiments were conducted to examine the proposed model and algorithm at various instance sizes that were generated by modifying an existing benchmark, with the results indicating that the proposed algorithm can obtain high-quality solutions with optimality gaps of less than 10% for all terminated instances within predefined time limit. A real-world instance—the Kartal district of Istanbul—was then used to demonstrate the practicality of the proposed model. Finally, the results of the numerical analysis were used to develop managerial insights for application by humanitarian relief agencies.