Home About APEM Events News Sponsorship
Advances in Production Engineering & Management

Archives > Volume 14 | Number 1 | March 2019 > pp 51–64

Advances in Production Engineering & Management
Volume 14 | Number 1 | March 2019 | pp 51–64

https://doi.org/10.14743/apem2019.1.311

Productivity improvement with parallel adjacent U-shaped assembly lines
Chutima, P.; Suchanun, T.
ABSTRACT AND REFERENCES (PDF)  |  FULL ARTICLE TEXT (PDF)

A B S T R A C T
A novel configuration of assembly lines was proposed in this research, namely parallel adjacent U-shaped assembly lines (PAUL). Typically, in a multiple U-line facility, each U-line is designed to work independently which may cause some workstations were not fully functioned. The PAUL aimed at increasing the utilisation of the whole facility by allowing cross-trained workers to work on the opposite legs of the adjacent U-lines (multi-line workstations). This configuration is easier to implement than parallel U-lines due to no restriction in terms of the lengths of U-lines to be paralleled and hidden expenditures that could be incurred in shop floor reconstruction. Since the line balancing of the PAUL is NP-hard and many conflicting objectives need to be optimised simultaneously, the evolutionary meta-heuristic which was the hybridisation of the multi-objective evolutionary algorithm based on decomposition (MOEA/D) and particle swarm optimisation (PSO), namely MOEA/D-PSO, was developed to effectively solve the problem. In addition, the decoding algorithm to convert the solutions obtained from MOEA/D-PSO into the PAUL's configuration was proposed. The performance of MOEA/D-PSO was evaluated against MOEA/D and multi-objective particle swarm optimisation (MOPSO). The experimental results reveal that MOEA/D-PSO outperformed its rival algorithms under the convergence-related performance.

A R T I C L E   I N F O
Keywords • Assembly line; U-shaped assembly line; Parallel adjacent assembly line; Assembly line balancing; Productivity improvement; Multi-objective optimisation; Evolutionary algorithm (MOEA/D); Particle swarm optimisation (PSO)
Corresponding authorChutima, P.
Article history • Received 29 October 2018, Revised 20 December 2019, Accepted 21 January 2019
Published on-line • 23 March 2019

E X P O R T   C I T A T I O N
» RIS format (EndNote, ProCite, RefWorks, and most other reference management software)
» BibTeX (JabRef, BibDesk, and other BibTeX-specific software)
» Plain text

< PREVIOUS PAPER   |   NEXT PAPER >