Model 1 solution results.

With their superior capabilities, unmanned aerial vehicles (UAVs) play a crucial role in search, rescue, and surveillance operations in disaster management. It is of great importance in the long run to optimally designate the base locations and deployment plans of the UAVs needing a base for their operations. In this study, we develop an integrated multi-criteria decision-making model to select bases and plan missions of UAVs using a combination of multi-attribute and multi-objective optimization techniques, with the decision maker having an interactive role. We formulate a goal programming model in which the number of bases, flight distance, unairworthy days, and cost are jointly minimized. The Analytic Hierarchy Process (AHP) is used to designate the associated goal weights. We develop Algorithm 1 to identify the target level for each goal and Algorithm 2 to refine the model for better solutions. We apply the process in a problem setting where designated disaster activity zones (DAZs) need to be covered by some candidate bases, among which an optimal selection is made. The model’s validation and refinement were evaluated across multiple scenarios. The illustrative example yields improvements of 8.57% in cost in the first scenario and 7.54% in distance in the second. The third scenario achieves 7.66% and 6.58% improvements in distance and cost, respectively. A real-world earthquake scenario from Türkiye further demonstrates the model’s practical applicability, with 5% improvement in distance and 14.8% in cost. The results of the proposed decision-making process guarantee satisfactory solutions for long-term base and operational planning of UAVs.

凭借卓越的作业性能，无人机（Unmanned Aerial Vehicles, UAVs）在灾害管理领域的搜救、侦察等作业中发挥着至关重要的作用。从长远视角出发，为需依托基站开展作业的无人机优化基站选址与部署方案，具备重要的战略意义。本研究构建了一套集成化多准则决策模型，结合多属性与多目标优化技术实现无人机基站选型与任务规划，并赋予决策者交互式参与权限。本研究建立目标规划模型，以联合最小化基站数量、飞行航程、不适航天数与运营成本为优化目标；采用层次分析法（Analytic Hierarchy Process, AHP）确定各目标的权重系数。本研究设计算法1以确定各目标的理想达成水平，同时开发算法2对模型进行迭代优化以获取更优解。本研究将所提决策流程应用于如下场景：需通过候选基站覆盖指定灾害活动区（Disaster Activity Zones, DAZs），并从中筛选最优基站集合。针对多类情景开展了模型验证与优化效果评估。算例结果显示，第一类情景下运营成本降低8.57%，第二类情景下飞行航程缩短7.54%；第三类情景下飞行航程与运营成本分别优化7.66%与6.58%。来自土耳其的真实地震场景进一步验证了模型的实际应用价值，该场景下飞行航程优化5%，运营成本降低14.8%。本研究所提决策流程可为无人机的长期基站选址与作业规划提供令人满意的解决方案。