Vol. 40, No. 4 (2017) (9)

A Modified Particle Swarm Algorithm for Body-in-white Welding Assembly Line Balancing Problem

Author(s): 
Han-ye Zhang

Affiliation(s): 

†School of Mechanical & Materials Engineering, Jiujiang University, Jiujiang 332005, China
Cite this paper
Han-ye Zhang, “A Modified Particle Swarm Algorithm for Body-in-white Welding Assembly Line Balancing Problem”, Journal of Mechanical Engineering Research and Developments, vol. 40, no. 4, pp. 611-618, 2017. DOI: 10.7508/jmerd.2017.04.009

ABSTRACT: In this paper, a modified particle swarm optimization algorithm is used to solve the Body-in-white (BIW) welding assembly line balancing problem. Firstly, the objective function is to minimize the number of workstations and workstation load for a given cycle time. Secondly, a modified particle swarm optimization algorithm (MPSO) is used. In order to calculate the fitness of particles, a function named φ(∙) is defined. Finally, case studies are used to test the MPSO. The results show that the MPSO is an efficient approach.

Keywords : Assembly line balancing; Body-in-white; Objective function; Particle swarm optimization algorithm; Precedence matrix; Smooth index

References
[1] S. G. Ponnambalam, P. Aravindan, and G. M. Naidu, “A multi-objective genetic algorithm for solving assembly line balancing problem”, Int J Adv Manuf Technol, vol. 16, no. 5, pp. 341-352, 2000.
[2] I. Sabuncuoglu, E. Erel, and M. Tanyer, “Assembly line balancing using genetic algorithms”, Journal of intelligent manufacturing, vol. 11, no. 3, pp. 295-310, June 2000.
[3] J. F. Goncalves and J. R. de Almeida, “A hybrid genetic algorithm for assembly line balancing”, Journal of heuristics, vol. 8, no. 6, pp. 629-642, November 2002.
[4] J. F. Yu and Y. H.Yin, “Assembly line balancing based on an adaptive genetic algorithm”, Int J Adv Manuf Technol, vol. 48, no. 1-4, pp. 370-377, April 2010.
[5] A. Yolmeh and F. Kianfar, “An efficient hybrid genetic algorithm to solve assembly line balancing problem with sequence-dependent setup times”, Computers and Industrial Engineering, vol. 62, no. 4, pp. 936-945, May 2012.
[6] C.Blum, “Beam-ACO for Simple Assembly Line Balancing”, Informs journal on computing,Vol.20, no.4, pp.618-627, 2008.
[7] M. N. I. Sulaiman, Y. H. Choo, and K. E. Chong, “Ant Colony Optimization with Look Forward Ant in Solving Assembly Line Balancing Problem”, in 2011 3rd Conference on Data Mining and Optimization, 2011, pp. 115-121.
[8] J. P. Dou, J. Li, and C. Su, “A novel feasible task sequence-oriented discrete particle swarm algorithm for simple assembly line balancing problem of type 1”, Int J Adv Manuf Technol, vol. 69, no. 9-12, pp. 2445-2457, December 2013.
[9] S. D. Lapierre, A. Ruiz, and P. Soriano, “Balancing assembly lines with tabu search”, European journal of operational research, vol. 168, no. 3, pp. 826-837, Feburary 2006.
[10] H. Guden and S. Meral, “An adaptive simulated annealing method for type-one simple assembly line balancing: a real life case study”, Journal of the faculty of engineering and architecture of gazi university, vol. 28, no.4, pp. 897-908, December 2013.
[11] L. P. Khoo and D. Alisantoso, “Line balancing of PCB assembly line using immune algorithms”, Engin– eering with Computers, vol.19, no. 2-3, pp. 92-100, 2003.
[12] S. O.Tasan and S.Tunali, “A review of the current applications of genetic algorithms in assembly line balancing”, Journal of intelligent manufacturing, vol. 19, no. 1, pp. 49-69, Feburary 2008.
[13] M. Z. Matondang, “Soft Computing in Optimizing Assembly Lines Balancing”, Journal of Computer Sciences, vol. 6, no. 2, pp. 141-162, 2010.
[14] O. Battaia and A. Dolgui, “A taxonomy of line balancing problems and their solution approaches”, Int.J.Production Economics, vol. 142, pp. 259-277, 2013.
[15] P. Sivasankaran and P. Shahabudeen, “Literature review of assembly line balancing problems”, Int J Adv Manuf Technol, vol. 73, no. 9-12, pp.1665-1694, August 2014.
[16] Y. Q. Hu and Q. Q. Yang, “A New Scheduling Mechanism of BitTorrent Streaming System Based on Improved PSO Algorithm”, Journal of computers, vol. 10, no. 1, pp. 34-44, January 2015.
[17] H. Y. Zhang, H. J. Liu, and L. Y. Li, “Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm”, Int J Adv Manuf Technol, vol. 71, no. 5-8, pp. 795-808, March 2014.
[18] Y. B. Meng, J. H. Zou, X. S. Gan, and L. Zhao, “Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm”, Neurocomputing, vol. 83, pp. 212-221, April 2012.
[19] J. P. Dou, C. Su, and J. Li, “A Discrete particle swarm optimization algorithm for assembly line balancing problems of type 1”, Computer Integrated Manufacturing Systems, vol. 18, no. 5, pp. 1021-1030, May 2012.