1687814016667907.pdf (865.29 kB)
Download file

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Download (865.29 kB)
journal contribution
posted on 19.08.2019, 09:21 by Zixiang Li, Mukund Nilakantan Janardhanan, Qiuhua Tang, Peter Nielsen
Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial - hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

Funding

This research work is partially funded by the National Natural Science Foundation of China (Grant No. 51275366).

History

Citation

Advances in Mechanical Engineering, 2016, 8 (9), pp. 1-14

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

VoR (Version of Record)

Published in

Advances in Mechanical Engineering

Publisher

SAGE Publications (UK and US)

issn

1687-8132

eissn

1687-8140

Acceptance date

13/08/2016

Copyright date

2016

Available date

19/08/2019

Publisher version

https://journals.sagepub.com/doi/10.1177/1687814016667907

Notes

Appendix 1: The procedure of improved C-PSO

Language

en

Usage metrics

Categories

Keywords

Exports