Optimization of Component Sequencing and Feeder Assignment for a Chip Shooter Machine Using Shuffled Frog-Leaping Algorithm

Abstract

The printed circuit board assembly (PCBA) tends to become a bottleneck in an assembly line of electronic products. For improvement, many assembly firms have introduced chip shooter machines. This, however, in turn, raises the issue of how to best utilize these machines. To deal with the PCBA problems, swarm intelligence (SI)-based metaheuristics have been increasingly popular due to the use of guided search that can better drive solutions toward optimality. In this study, we have proposed a novel SI-based metaheuristics, termed improved shuffled frog-leaping algorithm (I-SFLA2), to deal with the feeder assignment problem (FAP), and component sequencing problem (CSP) simultaneously for a chip shooter machine. With novel features such as self-adaptive jump, push jump, direct-jump prevention, and self-adaptive variant, the I-SFLA2 can develop smart jumps for frogs to approach and search around elites quickly while avoiding being trapped in local optima. The I-SFLA2 includes the strategy of transitioning from exploration to exploitation by decreasing the number of memeplexes iteratively. Our small-sized experiments showed the I-SFLA2 had a high hit rate to the optimal solution whereas big-sized experiments showed the I-SFLA2 outperformed the basic SFLA (B-SFLA), B-SFLA(2), the improved SFLA (I-SFLA1), as well as the PSO2 proposed in previous studies. The computational times for the I-SFLA2 were found also reasonable for practical usage. Note to Practitioners —Chip shooter machines have been widely used in industry for printed circuit board assembly (PCBA). Approaches such as exact approach, simple heuristics, and metaheuristics have been proposed for PCBA planning. Although with the capability to find the optimal solution, the exact approaches are found to be computationally intractable when used to deal with a problem of a practical size. Although simple heuristics are easy to use, they usually have the difficulty to find the optimal/near-optimal solution. One recent trend is using metaheuristics to solve PCBA problems as they can avoid the computational intractability of exact approaches while improving over simple heuristics to find a better solution. In this study, we propose an improved shuffled frogs leaping algorithm (I-SFLA2), an advanced swarm intelligence (SI)-based metaheuristic to solve the CSP and FAP simultaneously for a chip shooter machine. With new features and smart jumps, the I-SFLA2 is able to find a better solution compared to some previously-proposed methods.