Two-Stage Heuristic Optimization With Hybrid Evolutionary Multitasking for Automatic Optical Inspection Route Scheduling

Route scheduling for automatic optical inspection (AOI) of printed circuit boards (PCBs) impacts the productivity of surface mount production lines. Current state-of-the-art mathematical models in the area are not rigorous enough and neglect significant practical constraints, such as component geometric constraints. This article proposes a hierarchical mixed integer programming model to describe the route scheduling problem for AOI of PCBs. The model allows theoretical optimal solutions to be obtained for small-scale problems. In addition, a two-stage heuristic framework, consisting of clustering and path planning stages, is proposed to improve efficiency in solving large-scale problems, achieving near-optimal solutions. Taking into account that component distribution affects clustering results, the clustering stage is developed with a hierarchical heuristic algorithm based on block density with an aggregation strategy. The Lin–Kernighan algorithm is first used to quickly generate the scheduling sequence in the path planning stage. Image acquisition centers are initially adjusted with a customized heuristic. After that, a hybrid evolutionary multitask algorithm is proposed to further reduce path distance by dividing the image acquisition center adjustment task into several subtasks using heuristic rules. The algorithm obtains better quality results and is faster than traditional evolutionary algorithms. Experiments on an actual industrial AOI platform demonstrate that the proposed two-stage heuristic route scheduling algorithm outperforms state-of-the-art research in the area.