面向智慧城市管理巡查的可靠性无人机机巢选址优化方法

To enable automatic inspection by unmanned aerial vehicles for urban management events based on fixed nests and to mitigate the impact of nest and unmanned aerial vehicle failures on efficiency and stability, the reliability-oriented fixed nest location-allocation problem with multi-level backup mechanisms was investigated. The differentiated inspection frequencies of task points and service radius constraints of fixed nests were considered, and a mixed integer programming model was formulated with the objective of minimizing the total cost of nest construction and operation. A hybrid algorithm based on Lagrangian relaxation was proposed. The original problem was decomposed into two subproblems, i.e., nest location and multi-level task allocation, by relaxing the location-allocation coupling constraints, and they were solved exactly to obtain a tight lower bound. A coverage gain-driven location repair algorithm was designed to generate feasible upper bounds. An upper bound improvement algorithm based on neighborhood search was proposed to accelerate convergence. Research results show that, for small-scale and medium-scale instances, the proposed algorithm reduces computation time by 57.56%-88.86% compared with Gurobi, while producing high-quality solutions for large-scale cases within short runtimes. Multi-level redundancy significantly reduces system costs. In the case of Zhongshan District of Dalian, the three-level redundancy configuration reduces the total cost from 723 600 CNY to 437 200 CNY, a reduction of approximately 39.59%. The marginal benefits of configuring nests with more than three levels of redundancy diminish significantly. As the nest service radius increases, total and construction costs decline and then stabilize, with inspection costs remaining nearly unchanged. Unit cost of nest procurement is positively correlated with total, construction, inspection, and manual inspection costs and negatively correlated with the number of nests. Unmanned aerial vehicle unit flight cost shows a near-linear positive correlation with total, construction, and inspection costs but has no significant impact on manual inspection cost.