Hybrid dispatching and genetic algorithm for the surface mount technology scheduling problem in semiconductor factories

Abstract

Surface mount technology (SMT) is widely used in semiconductor packaging factories to assemble electronic components onto printed circuit boards. Therefore, reducing bottlenecks in SMT implementation is crucial for achieving the optimal production efficiency and meeting customer demands in semiconductor factories. This study developed a hybrid dispatching and genetic algorithm (HDGA) which uses a genetic algorithm (GA) and dispatch rules, to reduce machine set-up times and increase delivery fulfillment rates. The proposed HDGA is embedded in a scheduling system to optimize production scheduling by considering all practical constraints associated with SMT implementation, such as machine and job statuses, lot consolidation constraints, processing time, works in progress and machine priority, multiple processing rounds, and issue-number-related constraints. To validate the effectiveness of this algorithm, the present study compared its performance with that of a traditional GA and a hybrid GA. The results indicated that the HDGA outperformed the other three algorithms. The proposed algorithm can improve productivity, product quality, product delivery rates, and overall scheduling efficiency in semiconductor factories.