Skip to main content
Log in

A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In this paper, a new modified particle swarm optimization algorithm with negative knowledge is proposed to solve the mixed-model two-sided assembly line balancing problem. The proposed approach includes new procedures such as generation procedure which is based on combined selection mechanism and decoding procedure. These new procedures enhance the solution capability of the algorithm while enabling it to search at different points of the solution space, efficiently. Performance of the proposed approach is tested on a set of test problem. The experimental results show that the proposed approach can be acquired distinguished results than the existing solution approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Ağpak, K., & Gökçen, H. (2005). Assembly line balancing: Two resource constrained cases. International Journal Production Economics, 96, 129–140.

    Article  Google Scholar 

  • AitZai, A., Benmedjdoub, B., & Boudhar, M. (2014). Branch-and-bound and PSO algorithms for no-wait job shop scheduling. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0906-7.

  • Akagi, F., Osaki, H., & Kikuchi, S. (1983). A method for assembly line balancing with more than one worker in each station. International Journal of Production Research, 21, 755–770.

    Article  Google Scholar 

  • Andrés, C., Miralles, C., & Pastor, R. (2008). Balancing and scheduling tasks in assembly lines with sequence-dependent setup times. European Journal of Operational Research, 187(3), 1212–1223.

    Article  Google Scholar 

  • Bartholdi, J. J. (1993). Balancing two-sided assembly lines: A case study. International Journal of Production Research, 31, 2447–2461.

    Article  Google Scholar 

  • Battaia, O., & Dolgui, A. (2013). A taxonomy of line balancing problems and their solution approaches. International Journal of Production Economics, 142, 259–277.

    Article  Google Scholar 

  • Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168(3), 694–715.

    Article  Google Scholar 

  • Becker, C., & Scholl, A. (2009). Balancing assembly lines with variable parallel workplaces: Problem definition and effective solution procedure. European Journal of Operational Research, 199, 359–374.

    Article  Google Scholar 

  • Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2013). Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. Journal of Intelligent Manufacturing, 24(4), 775–789.

    Article  Google Scholar 

  • Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European Journal of Operational Research, 183, 674–693.

    Article  Google Scholar 

  • Buxey, G. M. (1974). Assembly line balancing with multiple statitons. Management Science, 20, 1010–1021.

    Article  Google Scholar 

  • Chakravarty, A. K. (1988). Line balancing with task learning effects. IIE Transactions, 20, 186–193.

    Article  Google Scholar 

  • Chakaravarthy, G. V., Marimuthu, S., & Sait, A. N. (2013). Performance evaluation of proposed differential evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming. Journal of Intelligent Manufacturing, 24(1), 175–191.

    Article  Google Scholar 

  • Chutima, P., & Chimklai, P. (2012). Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge. Computers and Industrial Engineering, 62, 39–55.

    Article  Google Scholar 

  • Chutima, P., & Naruemitwong, W. (2014). A Pareto biogeography-based optimisation for multi-objective two-sided assembly line sequencing problems with a learning effect. Computers and Industrial Engineering, 69, 89–104.

  • Eberhart, R. C., & Shi, Y. (2001). Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 Congress on Evolutionary Computation, 2001 (Vol. 1, pp. 81–86). IEEE.

  • Gökçen, H., Ağpak, K., & Benzer, R. (2006). Balancing of parallel assembly lines. International Journal of Production Economics, 103, 600–609.

    Article  Google Scholar 

  • Gutjahr, A. L., & Nemhauser, G. L. (1964). An algorithm for the line balancing problem. Management Science, 11(2), 308–315.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE international joint conference on neural networks (pp. 1942–1948). IEEE Press.

  • Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm optimization. In Proceedings of the conference on systems, man, and cybernetics SMC97 (pp. 4104–4108).

  • Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Kim, Y. K., Kim, Y., & Kim, Y. J. (2000). Two-sided assembly line balancing: A genetic algorithm approach. Production Planning and Control, 11(1), 44–53.

    Article  Google Scholar 

  • Lee, T. O., Kim, Y., & Kim, Y. K. (2001). Two-sided assembly line balancing to maximize work relatedness and slackness. Computers and Industrial Engineering, 40, 273–292.

    Article  Google Scholar 

  • Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261–1271.

    Article  Google Scholar 

  • Ma, W., Wang, M., & Zhu, X. (2013). Hybrid particle swarm optimization and differential evolution algorithm for bi-level programming problem and its application to pricing and lot-sizing decisions. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0803-5.

  • Macaskill, J. L. C. (1972). Production-line balances for mixed model lines. Management Science, 19, 423–434.

    Article  Google Scholar 

  • Miltenburg, J. (1998). Balancing U-lines in a multiple U-line facility. European Journal of Operational Research, 109, 1–23.

    Article  Google Scholar 

  • Miltenburg, J., & Wijngaard, J. (1994). The U-line line balancing problem. Management Science, 40, 1378–1388.

    Article  Google Scholar 

  • Mohemmed, A. W., Sahoo, N. C., & Geok, T. K. (2008). Solving shortest path problem using particle swarm optimization. Applied Soft Computing, 8(4), 1643–1653.

    Article  Google Scholar 

  • Moodie, C. L., & Young, H. H. (1965). A heuristic method of assembly line balancing for assumptions of constant or variable work element times. Journal of Industrial Engineering, 16, 23–29.

  • Özcan, U., & Toklu, B. (2009). Balancing of mixed-model two-sided assembly lines. Computers and Industrial Engineering, 57(1), 217–227.

    Article  Google Scholar 

  • Pinto, P. A., Dannenbring, D. G., & Khumawala, B. M. (1975). A branch and bound algorithm for assembly line balancing with paralleling. International Journal of Production Research, 13, 183–196.

    Article  Google Scholar 

  • Qiu, X., & Lau, H. Y. K. (2014). An AIS-based hybrid algorithm for static job shop scheduling problem. Journal of Intelligent Manufacturing, 25(3), 489–503.

    Article  Google Scholar 

  • Ratnaweera, A., Halgamuge, S., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 8(3), 240–255.

  • Salman, A., Ahmad, I., & Al-Madani, S. (2003). Particle swarm optimization for task assignment problem. Microprocessors and Microsystems, 26, 363–371.

    Article  Google Scholar 

  • Scholl, A., Boysen, N., & Fliedner, M. (2008). The sequence-dependent assembly line balancing problem. OR Spectrum, 30(3), 579–609.

    Article  Google Scholar 

  • Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. In Proceedings of the IEEE Congress on Evolutionary Computation, USA (pp. 69–73).

  • Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1945–1950). IEEE Press.

  • Shtub, A. (1984). The effect of incompletion cost on line balancing with multiple manning of work stations. International Journal of Production Research, 22, 235–245.

    Article  Google Scholar 

  • Simaria, A. S., & Vilarinho, P. M. (2009). 2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines. Computers and Industrial Engineering, 56(2), 489–506.

    Article  Google Scholar 

  • Sun, J., Xu, W., & Feng, B. (2004). A global search strategy of quantum-behaved particle swarm optimization. In Proceedings of the 2004 IEEE conference on cybernetics and intelligent systems (Vol. 1, pp. 111–116).

  • Thomopoulos, N. T. (1970). Mixed model line balancing with smoothed station assignments. Management Science, 16, 593–603.

    Article  Google Scholar 

  • Tyagi, S. K., Yang, K., Tyagi, A., & Dwivedi, S. N. (2011). Development of a fuzzy goal programming model for optimization of lead time and cost in an overlapped product development project using a Gaussian adaptive particle swarm optimization-based approach. Engineering Applications of Artificial Intelligence, 24(5), 866–879.

    Article  Google Scholar 

  • Zhan, Z. H., Zhang, J., Li, Y., & Chung, H. H. (2009). Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(6), 1362–1381.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Uğur Özcan.

Appendices

Appendix 1: Notations used in model formulations

\(i,j,h,p\) :

Index for task

Maxiter :

Number of iterations

iter :

Index for iteration

ns :

Number of swarms in the population

\(x\) :

Index for swarm

np :

Number of particles in the swarms

\(y\) :

Index for particle

\(S_{x,y} \) :

Solution and side string number \(y\) in the swarm \(x\)

\(M\) :

Number of product models

\(m\) :

Product model index

List :

List of candidate tasks

\({\textit{RPW}[i]}\) :

Ranked positional weight for each task \(i = 1, 2,\ldots , n\)

\({\textit{Sn}[i]}\) :

Number of successors for each task \(i = 1, 2,\ldots , n\)

\({\textit{Pl}[i]}\) :

Priority list for each task \(i = 1, 2,\ldots , n\)

\({\textit{Tt}[m]}\) :

Cumulative task times for each product model \(m = 1, 2,\ldots , M\)

\(c_{1},c_{2}\) :

Two positive constants indicating cognition and social learning factors

\(r_{1},r_{2}\) :

Two random real numbers drawn from uniform distribution \(U\)[0–1]

\(w\) :

Inertia weight, controls the impact of previous velocity value on the new one

\(L_{best}\) :

Particle with the best solution value in the current swarm

\(L_{worst}\) :

Particle with the worst solution value in the current swarm

\(G_{best}\) :

Particle with the best solution value in the current population

\(G_{worst}\) :

Particle with the worst solution value in the current population

\(\textit{FWPM}_{[i]}\) :

The first walk probability matrix, which consists of the values which show the selection probability of a task as the first task in the solution string, \(\hbox {where }i\in \left\{ {1,2,\ldots ,n} \right\} \)

\(\textit{JPPM}_{[i,j]}\) :

The joint probability position matrix, which consists of the values which show the selection probability of task \(j\) immediately after task \(i\) in the solution string, for all \(i\ne j \hbox { where }i\hbox { and }j\in \left\{ {1,2,\ldots ,n} \right\} \)

\(\textit{JPVM}_{[i,j]}\) :

The joint probability velocity matrix, which consists of the values which show the rate of the change at \(\textit{JPPM}[i,j]\), for all \(i\ne j\hbox { where }i\hbox { and }j \in \left\{ {1,2,\ldots ,n} \right\} \)

Elit_List :

List for the best solution of algorithm

NM :

Index for mated station

NL, NR :

Indexes for left and right stations, respectively

\(\textit{mWL}_{NM}^1\) :

The station load including unavoidable idle times of the left-side station of the current mated-station for all \(m\in \{1,2,\ldots ,M\}\)

\(\textit{mWL}_{NM}^2\) :

The station load including unavoidable idle times of the right-side station of the current mated-station for all \(m\in \{1,2,\ldots ,M\}\)

SL, SR :

Starting times of the task for the left and the right-side stations, respectively

\(t_{im}\) :

Completion time of task \(i\) for model \(m\)

\(C\) :

Cycle time

\(t_{im}^f\) :

Finish time of task \(i\) for model \(m\)

\(P(i)\) :

Set of immediate predecessors of task \(i\)

\(\textit{TL}_{NM}^1\) :

Set of tasks which are assigned to the left-side station of the current mated-station

\(\textit{TL}_{NM}^2\) :

Set of tasks which are assigned to the right-side station of the current mated-station

\(\textit{WL}_{max}\) :

Maximum of the cumulative station load including unavoidable idle times of the last stations for all \(m\in \{1,2,\ldots ,M\}\)

WLL :

The station load including unavoidable idle times of the left-side station of the current mated-station for all \(m\in \{1,2,\ldots ,M\}\)

WLR :

The station load including unavoidable idle times of the right-side station of the current mated-station for all \(m\in \{1,2,\ldots ,M\}\)

Appendix 2: Task times for each product model of P205 problem

See Table 7.

Table 7 Problem P205

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Delice, Y., Kızılkaya Aydoğan, E., Özcan, U. et al. A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. J Intell Manuf 28, 23–36 (2017). https://doi.org/10.1007/s10845-014-0959-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0959-7

Keywords

Navigation