Hierarchical Heuristic for Large-Scale Automatic Optical Inspection Route Scheduling Based on Neighborhood Search

Automatic Optical Inspection (AOI), as a core equipment in quality inspection process of printed circuit board (PCB) assembly lines, directly impacts overall production capacity through its inspection efficiency. However, existing research on AOI route scheduling problem exhibits limitations such as neglecting image acquisition center adjustment and lack of efficiency for large-scale PCBs. A hierarchical heuristic algorithm based on neighborhood search is proposed to address large-scale AOI route scheduling. The problem is decomposed into component clustering, path sequencing, and image acquisition center adjustment. The method features adaptive neighborhood construction through search area adjustment, enabling component clustering via neighborhood operations. Cluster centers are then sequenced using the Lin-Kernighan algorithm. A greedy heuristic algorithm for image acquisition center adjustment is further developed to optimize path length. Experimental results demonstrate that this algorithmic framework outperforms state-of-the-art methods, particularly showing significant improvements in solving large-scale problems.