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

2009 ◽  
Vol 48 (10) ◽  
pp. 2765-2791 ◽  
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

scholarly journals Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

2016 ◽  
Vol 232 (8) ◽  
pp. 1444-1459 ◽  
Author(s):  
Mohd Fadzil Faisae Ab Rashid ◽  
Windo Hutabarat ◽  
Ashutosh Tiwari
Keyword(s):  
Assembly Line ◽  
Particle Swarm ◽  
Assembly Line Balancing ◽  
Particle Swarm Optimisation ◽  
Assembly Sequence Planning ◽  
Line Balancing ◽  
Assembly Sequence ◽  
Discrete Particle ◽  
Sequence Planning ◽  
Multi Objective

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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