A Novel Parallel Assembly Sequence Planning Method for Complex Products Based on PSOBC

Abstract

Parallel assembly sequence planning (PASP) greatly impacts on efficiency of assembly process. In traditional methods, large scale of matrix calculation still limits efficiency of PASP for complex products. A novel PASP method is proposed to address this issue. To avoid matrix calculation, the synchronized assembly Petri net (SAPN) is firstly established to describe the precedence relationships. Associated with the SAPN model, the PASP process can be implemented via particle swarm optimization based on bacterial chemotaxis (PSOBC). Characterized by an attraction-repulsion phase, PSOBC not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than standard particle swarm optimization (PSO) algorithm. Finally, feasibility and effectiveness of the proposed method are verified via a case study. With different assembly parallelism degrees, optimization results show that assembly efficiency of the solution calculated by PSOBC method is 9.0%, 4.2%, and 3.1% better than the standard PSO process.