Simulation optimization with PSO and OCBA for semiconductor back-end assembly

This study examines a dynamic parallel machine scheduling problem in a hybrid flow shop for semiconductor back-end assembly. The subject is a multi-line, multi-stage facility with multi-type parallel machine groups, and orders are scheduled with different start times. As a typical make-to-order and contract manufacturing business model, the main objective is to achieve the minimum manufacturing lead time, and the main decisions are to find an optimal assignment of production line and machine type by stage for each order. Nevertheless, production behavior and a number of conditions increase complexity. Such conditions include order split and merge of jobs for parallel processing with stochastic processing time and compliance with quality and traceability requirements. We proposed a simulation optimization approach, using particle swarm optimization algorithm combined with optimal computing budget allocation technique as solution. The proposed approach provides a novel application for a complex production system and directions for further study.