M-SIALNS for Air–Ground Collaborative Inspection: Spatio-Temporal Conflict Mitigation in Complex Bi-Layer Networks

Multi-vehicle–UAV collaborative inspection systems face critical challenges in complex environments, where spatiotemporal node conflicts, coupled scheduling, and congestion severely affect operational efficiency. To address these issues, we define the Multi-UAV–Multi-Vehicle Collaborative Inspection Vehicle Routing Problem in a Bi-Layer Road Network (MUMV-CIVRP-BLRN). The model captures realistic inspection scenarios through (i) spatiotemporal node-conflict constraints that prevent simultaneous vehicle access to occupied nodes and (ii) flexible UAV operations, enabling chained multi-task sorties and cross-vehicle recovery. To solve this NP-hard problem, we propose a Multi-Strategy Improved Adaptive Large Neighborhood Search (M-SIALNS) algorithm. Beyond standard ALNS frameworks, M-SIALNS incorporates a cluster-based initialization method, task-chain destroy–repair operators, and air–ground coordination strategies specifically tailored for the bi-layer structure. These strategies enhance global search, solution feasibility, and robustness. Comprehensive experiments on benchmark datasets and a power-grid case study demonstrate the advantages of M-SIALNS. Compared with state-of-the-art algorithms, it reduces inspection duration by 1.6%–11.0%, consistently delivering statistically significant improvements. Ablation and sensitivity analyses confirm the contribution of tailored operators and provide managerial insights into optimal fleet configurations and resource allocation thresholds. This work advances both the theoretical modeling of bi-layer vehicle–UAV routing and its practical deployment in large-scale inspection missions.