Multi-objective optimization of maintenance policies for unmanned aerial vehicle swarms

The proliferation of unmanned aerial vehicle (UAV) swarms presents critical challenges to system-level reliability and safety. Traditional maintenance strategies, designed for single assets, are fundamentally inadequate for the systemic complexities and multifaceted risks of swarm operations. This study addresses this gap by developing a multi-objective optimization framework to derive optimal maintenance policies for heterogeneous UAV swarms. We formulate the problem to simultaneously minimize maintenance cost, maximize mission reliability, and minimize a composite operational risk encompassing both crash and hazardous material release. The framework distinguishes between nodal and non-nodal UAV roles and employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to explore the complex trade-off space. The framework is validated through numerical experiments against a heuristic benchmark, yielding significant results. Optimized policies reduce injury risk by more than tenfold compared to traditional methods, while simultaneously doubling mission reliability. Furthermore, the analysis reveals a distinct “efficiency frontier” for safety investment, providing a novel, data-driven tool for managerial decision-making. Ultimately, this research delivers a holistic, risk-informed framework that bridges the gap between theoretical optimization and the practical challenges of safe, reliable, and cost-effective swarm operation, offering actionable guidance for tailoring maintenance strategies to specific risk tolerance levels.