Technological advancements in unmanned aerial vehicles (UAVs) have revolutionized various industries, enabling the widespread adoption of UAV-based solutions. In engineering management, UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments, surpassing traditional inspection techniques. Building on this foundation, this paper delves into the optimization of UAV inspection routing and scheduling, addressing the complexity introduced by factors such as no-fly zones, monitoring-interval time windows, and multiple monitoring rounds. To tackle this challenging problem, we propose a mixed-integer linear programming (MILP) model that optimizes inspection task assignments, monitoring sequence schedules, and charging decisions. The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem (VRP), leading to a mathematically intractable model for commercial solvers in the case of large-scale instances. To overcome this limitation, we design a tailored variable neighborhood search (VNS) metaheuristic, customizing the algorithm to efficiently solve our model. Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm, demonstrating its scalability for both large-scale and real-scale instances. Sensitivity experiments and a case study based on an actual engineering project are also conducted, providing valuable insights for engineering managers to enhance inspection work efficiency.

中文翻译：

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用于工程管理的无人机巡检路线和调度

无人机 (UAV) 的技术进步彻底改变了各个行业，使得基于无人机的解决方案得以广泛采用。在工程管理中，无人机巡检已成为识别高风险施工环境中隐患的高效方法，超越了传统巡检技术。在此基础上，本文深入研究了无人机巡检路线和调度的优化，解决了禁飞区、监控间隔时间窗和多轮监控等因素带来的复杂性。为了解决这个具有挑战性的问题，我们提出了一种混合整数线性规划（MILP）模型，该模型可以优化检查任务分配、监控序列计划和充电决策。对这些因素的综合考虑将我们的问题与传统的车辆路径问题（VRP）区分开来，导致在大规模实例的情况下，商业求解器在数学上难以处理模型。为了克服这一限制，我们设计了一种定制的变量邻域搜索（VNS）元启发式算法，定制算法来有效地解决我们的模型。进行了大量的数值实验来验证我们提出的算法的有效性，证明其对于大规模和真实规模实例的可扩展性。还进行了灵敏度实验和基于实际工程项目的案例研究，为工程管理人员提高检测工作效率提供了宝贵的见解。