Assessment of UAV-based low-altitude logistics in urban supply chains: slope-intercepted grey incidence analysis

Purpose The current study evaluates low-altitude drone logistics performance when operating in the environment of urban supply chains using a novel Grey Incidence Analysis model. Design/methodology/approach Key performance indications, involving Delivery Time, Traffic Congestion, Operational Cost per Delivery, Payload Capacity, and Route Optimization were evaluated using a novel Slope-Intercepted Grey Incidence Analysis (SIGIA) model. Comparative analyses with four other multiple criteria decision-making models have confirmed the reliability of the proposed approach. Findings The results indicate that delivery time and traffic congestion stand out as main performance factors where drone delivery demonstrates strengthened performance benefits. Drones shorten delivery duration through their unique feature of avoiding road traffic because they provide a crucial advantage for crowded cities such as in Zhejiang, China. The adoption of drone logistics still faces problems due to their restricted carrying weight capabilities alongside their higher operating expenses. The results show that drones generate better performance when conditions are condusive specifically for distributing small packages. Originality/value The study proposed a novel slope-intercept form of the resolution coefficient. The study demonstrates that drones deliver multiple advantages in speed-based shipment and traffic streamlining yet their deployment requirements evaluation must incorporate both operational expenses and weight capacity boundaries.