A two-stage approach optimising PCB assembly on the sequential pick-and-place machine by a score-based slot selection method and a swarm intelligence approach

Abstract

PCB assembly on the sequential pick-and-place machine is a typical NP-hard combinatorial optimisation problem which is a critical bottleneck in electronic product manufacturing industry. In this paper, a two-stage approach is proposed. In the first stage, the distance score with weights selection (DSWS) method is proposed to select a suited set of slots to load feeders. In the second stage, a novel swarm intelligence approach, called the elimination with decay-based swarm intelligence approach (EDSIA) is proposed to solve the problems of assignment of feeders to the selected slots and the placement sequence of the components. In EDSIA, a new population evolution mechanism, based on proposed elimination coefficient and decay factor is proposed to propel the population forwards the global optimum. The numerical results and comparisons illustrate the effectiveness and efficiency of the proposed two-stage approach.