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.

世界范围内自然灾害和人为灾害的数量和严重程度不断增加，导致需要更精确和有效的人道主义响应，而使用人道主义救援网络评估来减少灾害不确定性可以在提供精确的人道主义行动中发挥至关重要的作用。在这项研究中，协作式卡车和无人机系统被开发为人道主义救援网络使用的灾后评估工具。拟议的系统包括配备有摄像头的无人机，该无人机可以从卡车上发射，从灾后交通网络的节点和链路收集信息。无人机操作后，卡车用于取回无人机的电池并为其充电。为了优化这个协作的卡车和无人机系统，我们专注于路由问题，目标是在预定义的时间限制内最大化从节点和链接收集的信息的价值，由于需要以综合方式确定卡车和无人机的路线，这个问题变得具有挑战性。据我们所知，这项研究是第一个考虑以利润最大化为目标的协同卡车和无人机路线优化问题。在将所提出的问题制定为混合整数线性规划 (MILP) 模型后，我们将问题结构分解为一个基于路径的主问题和两个子问题，以允许使用列生成 (CG) 框架来解决该问题. 进行了数值实验以检查通过修改现有基准生成的各种实例大小的建议模型和算法，结果表明，所提出的算法可以在预定义的时间限制内为所有终止实例获得最优性差距小于 10% 的高质量解决方案。然后使用一个真实世界的实例——伊斯坦布尔的卡尔塔尔区——来证明所提议模型的实用性。最后，数值分析的结果被用于开发管理洞察力，以供人道主义救援机构应用。