Optimization of printed circuit board component placement using an efficient hybrid genetic algorithm

The hardware restrictions of surface mount placement machines, such as height, pick and place restrictions, and simultaneous pickup are often in printed circuit board (PCB)-related studies. This study proposes an efficient hybrid genetic algorithm (HGA) for solving the nozzle assignment problem and the component pick and place sequence problem. First, the proposed method obtains the sequence of the automatic nozzle changer (ANC) with the maximum number of simultaneous pickups and the minimum number of picks as the solution of the nozzle setup problem. Then, the proposed method uses the nearest neighbor search (NNS), 2-optimization, and a genetic algorithm (GA) with the known ANC sequences to obtain the PCB assembly time with the optimal component pick and place sequence. Experiments are conducted on the PCB of the EVEST EM-780 surface mount placement machine. Results show that the proposed HGA gives the lowest total number of picks, the shortest total head movement distance, and the minimum total PCB assembly time compared to those of other methods.