Kriging-based resilience assessment of low-altitude logistics networks under multiple operational disturbances

Abstract

Low-altitude unmanned aerial vehicle (UAV) logistics networks are subject to multiple operational disturbances that can severely degrade delivery performance. This paper proposes a Kriging-based resilience assessment framework that efficiently evaluates the reliability and resilience of such networks under multi-dimensional disturbances. A discrete-event simulation environment is developed that incorporates Voronoi-based airspace topology, A* path planning, and dynamic re-planning. Three disturbance types—structural (no-fly zones), functional (adverse weather), and informational (communication delays)—are modelled and screened via Sobol sensitivity analysis. Two complementary Kriging surrogates, trained on Latin Hypercube samples, replace expensive Monte Carlo simulations with a 25-fold speed-up: one predicting endpoint cumulative orders for failure probability estimation, and the other predicting the integral resilience triangle index for resilience surface characterisation. Results show that structural disruption and weather degradation contribute nearly equally to performance variance (52.4% vs. 47.6%). The resilience surface reveals that only 10.9% of the disturbance parameter space sustains high resilience (R ≥ 0.8), while 26.4% falls into a low-resilience regime (R < 0.6) where severe throughput loss and incomplete recovery coexist. The proposed framework provides a computationally efficient tool for resilience-informed management of low-altitude logistics networks.

Keywords

Intelligent transportation systems / low-altitude logistics network / re-silience assessment / kriging surrogate model / failure probability mapping

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