Open AccessA Novel Parallel Assembly Sequence Planning Method for Complex Products Based on PSOBC
Author(s) -
Yong Yang,
Miao Yang,
Liang Shu,
Shasha Li,
Zhiping Liu
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/7848329
Subject(s) - particle swarm optimization , computer science , convergence (economics) , process (computing) , matrix (chemical analysis) , mathematical optimization , premature convergence , sequence (biology) , swarm behaviour , algorithm , parallelism (grammar) , parallel computing , mathematics , materials science , chemistry , economics , composite material , economic growth , operating system , biochemistry
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.