Assembly sequence planning based on a hybrid particle swarm optimisation and genetic algorithm

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.

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A particle swarm optimisation algorithm for multi-plant assembly sequence planning with integrated assembly sequence planning and plant assignment

International Journal of Production Research ◽

10.1080/00207540902791835 ◽

2009 ◽

Vol 48 (10) ◽

pp. 2765-2791 ◽

Cited By ~ 19

Author(s):

Yuan-Jye Tseng ◽

Jian-Yu Chen ◽

Feng-Yi Huang

Keyword(s):

Particle Swarm ◽

Particle Swarm Optimisation ◽

Assembly Sequence Planning ◽

Assembly Sequence ◽

Sequence Planning ◽

Optimisation Algorithm ◽

Particle Swarm Optimisation Algorithm

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Stable Assembly Sequence Planning Using a Genetic Algorithm

Volume 4: 4th Design for Manufacturing Conference ◽

10.1115/detc99/dfm-8912 ◽

1999 ◽

Author(s):

Shiang-Fong Chen ◽

Xiao-Yun Liao

Keyword(s):

Genetic Algorithm ◽

Simple Structure ◽

Fitness Function ◽

Assembly Sequence Planning ◽

Connection Matrix ◽

Assembly Sequence ◽

Sequence Planning ◽

Industry Standard ◽

Planning Algorithm ◽

The Stability

Abstract Stability problems in assembly sequence planning have drawn great research interest in recent years. Most proposed methodologies are based on graph theory and involve complex geometric and physical analyses. As a result, even for a simple structure, it is difficult to take all the criteria into account and to implement real world solutions. This paper uses a genetic algorithm (GA) to synthesize different criteria fo generating a stable assembl plan. Three matrices (Connection Matrix, Supporting Matrix, and Interference-Free Matrix) are generated from an input B-rep file to represent the CAD information of a given product. The stability of a given assembly plan and reorientation numbers are incorporated into the fitness function of the genetic assembly planner. The proposed planning algorithm has been successfull implemented. This paper also presents implemented planne performance as measured for two industry-standard structures.

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