Component scheduling for chip shooter machines: a hybrid genetic algorithm approach

A chip shooter machine for electronic component assembly has a movable feeder carrier, a movable X–Y table carrying a printed circuit board (PCB), and a rotary turret with multiple assembly heads. This paper presents a hybrid genetic algorithm (HGA) to optimize the sequence of component placements and the arrangement of component types to feeders simultaneously for a chip shooter machine, that is, the component scheduling problem. The objective of the problem is to minimize the total assembly time. The GA developed in the paper hybridizes different search heuristics including the nearest-neighbor heuristic, the 2-opt heuristic, and an iterated swap procedure, which is a new improved heuristic. Compared with the results obtained by other researchers, the performance of the HGA is superior in terms of the assembly time. Scope and purpose When assembling the surface mount components on a PCB, it is necessary to obtain the optimal sequence of component placements and the best arrangement of component types to feeders simultaneously in order to minimize the total assembly time. Since it is very difficult to obtain the optimality, a GA hybridized with several search heuristics is developed. The type of machines being studied is the chip shooter machine. This paper compares the algorithm with a simple GA. It shows that the performance of the algorithm is superior to that of the simple GA in terms of the total assembly time.