Solving Feeder Assignment and Component Sequencing Problems for Printed Circuit Board Assembly Using Particle Swarm Optimization

• Published in: IEEE transactions on automation science and engineering Vol. 14; no. 2; pp. 881 - 893
• Main Author: Hsu, Hsien-Pin
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.2017
• Subjects: Component sequencing problem (CSP); Electronic components; feeder assignment problem (FAP); Genetic algorithms; Particle swarm optimization; particle swarm optimization (PSO); Planning; printed circuit board assembly (PCBA); Printed circuits; Sequential analysis; Workstations
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2016.2622253

Printed circuit board assembly (PCBA) is a process of connecting various electronic components through printed circuit boards (PCBs). Due to the need to assemble a lot of components and PCBs at the same time, the PCBA process tends to become the bottleneck in an assembly line. Many assembly firms have thus introduced automated PCBA machines to expedite this process. However, to best operate these machines, effective PCBA planning is still required. Some nature-inspired metaheuristics such as simulated annealing and genetic algorithm (GA) have been increasingly used for the PCBA planning. Also, we find that particle swarm optimization (PSO) has never been employed to deal with the feeder assignment problem (FAP) and component sequencing problem (CSP) at the same time, though it has been regarded as a good competitor to GAs. In this paper, we developed two PSO-based approaches to deal with the two problems simultaneously for a chip shooter machine. In addition, we have conducted experiments to compare the two PSO-based approaches with two GA-based approaches. The experimental results showed that PSO2, the PSO-based approach with sigmoid functions, outperformed others in terms of assembly cycle time. The comparison with an exact approach further shows that PSO2 has a high rate to find the optimal/near-optimal solution.