A Discrete Particle Swarm Optimization Approach to Optimize the Assembly Sequence of Mechanical Product

2012 ◽  
Vol 490-495 ◽  
pp. 203-207
Author(s):  
Zhong Bo Zhang ◽  
Chuan Yong Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Assembly Sequence Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Discrete Pso ◽  
The Difference ◽  
Assembly Tool

The aim of assembly sequence planning (ASP) is to achieve the best assembly sequence which assembly cost and time used is less. The geometrical feasibility of an assembly sequence is validated by the interference matrix of the product. The number of assembly tool changes and the number of assembly operation type changes are considered in the fitness function. To establish the mapping relation between ASP and particle swarm optimization (PSO) approach, some definitions of position, velocity and operator of particles are proposed. The difference of the proposed discrete PSO (DPSO) algorithm with the other algorithm is the emphasis on the geometrical feasibility of the assembly sequence. The geometrical feasibility is verified at the first and the every iteration. The performance and feasibility of the proposed algorithm is verified via a simplified engine assembly case.

A Binary Particle Swarm Optimization Approach to Optimize Assembly Sequence Planning

2012 ◽  
Vol 13 (1) ◽  
pp. 732-738 ◽  
Author(s):  
Jameel A. A. Mukred ◽  
Zuwairie Ibrahim ◽  
Ismail Ibrahim ◽  
Asrul Adam ◽  
Khairunizam Wan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Assembly Sequence Planning ◽  
Binary Particle Swarm Optimization ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Sequence Planning

An Particle Swarm Optimization Approach for Assembly Sequence Planning

2009 ◽  
Vol 16-19 ◽  
pp. 1228-1232 ◽  
Author(s):  
Hong Yu ◽  
Jia Peng Yu ◽  
Wen Lei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Assembly Sequence Planning ◽  
Assembly Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Sequence Planning

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.

A Particle Swarm Optimization Approach for Reuse Guided Case Retrieval

2014 ◽  
Vol 6 (3) ◽  
pp. 16-30 ◽  
Author(s):  
Nabila Nouaouria ◽  
Mounir Boukadoum ◽  
Robert Proulx
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Direct Access ◽  
Memory Model ◽  
Case Based Reasoning ◽  
Optimization Approach ◽  
Case Retrieval ◽  
Swarm Optimization ◽  
Case Based

The success of Case Based Reasoning (CBR) problem solving is mainly based on the recall process. The ideal CBR memory is one that simultaneously speeds up the retrieval step while improving the reuse of retrieved cases. In this paper, the authors present a novel associative memory model to perform the retrieval stage in a case based reasoning system. The described approach makes no prior assumption of a specific organization of the case memory, thus leading to a generic recall process. This is made possible by using Particle Swarm Optimization (PSO) to compute the neighborhood of a new problem, followed by direct access to the cases it contains. The fitness function of the PSO stage has a reuse semantic that combines similarity and adaptability as criteria for optimal case retrieval. The model was experimented on two proprietary databases and compared to the flat memory model for performance. The obtained results are very promising.

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

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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