An Assembly Sequence Planning Approach with a Multi-state Particle Swarm Optimization

Assembly sequence planning (ASP) is the foundation of the assembly planning which plays a key role in the whole product life cycle. Although the ASP problem has been tackled via a variety of optimization techniques, the particle swarm optimization (PSO) algorithm is scarcely used. This paper presents a PSO algorithm to solve ASP problem. Unlike generic versions of particle swarm optimization, the algorithm redefines the particle's position and velocity, and operation of updating particle positions. In order to overcome the problem of premature convergence, a new study mechanism is adopted. The geometrical constraints, assembly stability and the changing times of assembly directions are used as the criteria for the fitness function. To validate the performance of the proposed algorithm, a 29-component product is tested by this algorithm. The experimental results indicate that the algorithm proposed in this paper is effective for the ASP.

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

Mathematical Problems in Engineering ◽

10.1155/2020/7848329 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yanfang Yang ◽

Miao Yang ◽

Liang Shu ◽

Shasha Li ◽

Zhiping Liu

Keyword(s):

Particle Swarm Optimization ◽

Large Scale ◽

Particle Swarm ◽

Assembly Sequence Planning ◽

Assembly Sequence ◽

Swarm Optimization ◽

Complex Products ◽

Sequence Planning ◽

Parallel Assembly ◽

Matrix Calculation

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.

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Research on Assembly Sequence Planning Based on Improved Slope One Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.3444 ◽

2011 ◽

Vol 121-126 ◽

pp. 3444-3449

Author(s):

Yu Jin ◽

Wen Jun Hou ◽

Fu Xing Yang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Assembly Sequence Planning ◽

Assembly Sequence ◽

Network Graph ◽

Swarm Optimization ◽

Sequence Planning

According to the characteristics and needs of complicated products, a method for handing assembly sequence based on improved Slope One algorithm was proposed. On the basis of that the network graph of assembly relationship was constructed, and a method for simplifying it was proposed by expressing elliptically same parts, identifying and hiding fasteners. In this paper, Slope One algorithm was initially introduced into the assembly sequence planning, and it was improved according to the problems to be resolved. In the meantime, particle swarm optimization algorithm was introduced into the feedback of the recommendation result. The method has been proved that it was not only used to obtain a good recommendation of assembly sequence but also sensitive to the individuation of designers.

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