Skip to main content

Advertisement

SpringerLink
Log in

Hybrid genetic algorithm to solve resource constrained assembly line balancing problem in footwear manufacturing

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper aims to develop a hybrid genetic algorithm (HGA) to solve the resource constrained assembly line balancing problem (RCALBP) in the sewing line of a footwear manufacturing plant. Sewing, which is the most critical process in footwear manufacturing, has a series of processes, such as punching, trimming, attaching shoelaces. RCALBP in the sewing line considers not only the precedence constraints of product assembly but also the resource constraints, such as operators and equipment. A novel HGA that includes two stages is proposed to optimize the resources in the sewing line. The first stage uses the priority rule-based method (PRBM) to determine the feasible solutions of assigning tasks and machines to workstations. The solutions of PRBM are used to construct the initial population of genetic algorithm (GA) in the second stage. To ensure that the solution of GA is feasible, a two-point-order crossover with the new technique of searching feasible solution patterns is proposed. Moreover, the mutation procedure of GA is modified to avoid the building block from breaking, which may cause unfeasible solutions in RCALBP. A self-tuning method is also applied recursively to exclude unfeasible solutions. The proposed HGA is compared with the manual procedure adopted practically in factories, the existing heuristic model in the literature, and the traditional GA. Based on actual data from a footwear factory, computational results demonstrate that the proposed HGA can achieve better results than the other algorithms.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Ağpak K, Gökçen H (2005) Assembly line balancing: two resource constrained cases. Int J Prod Econ 96:129–140

    Article  Google Scholar 

  • Azadeh A, Sangari MS, Sangari E, Fatehi S (2015) A particle swarm algorithm for optimising inspection policies in serial multistage production processes with uncertain inspection costs. Int J Comput Integr Manuf 28:766–780

    Article  Google Scholar 

  • Bautista J, Pereira J (2009) A dynamic programming based heuristic for the assembly line balancing problem. Eur J Oper Res 194:787–794

    Article  MATH  Google Scholar 

  • Baybars İ (1986) A survey of exact algorithms for the simple assembly line balancing problem. Manag Sci 32:909–932

    Article  MathSciNet  MATH  Google Scholar 

  • Becker C, Scholl A (2006) A survey on problems and methods in generalized assembly line balancing. Eur J Oper Res 168:694–715

    Article  MathSciNet  MATH  Google Scholar 

  • Boysen N, Fliedner M, Scholl A (2007) A classification of assembly line balancing problems. Eur J Oper Res 183:674–693

    Article  MATH  Google Scholar 

  • Boysen N, Fliedner M, Scholl A (2008) Assembly line balancing: which model to use when? Int J Prod Econ 111:509–528

    Article  MATH  Google Scholar 

  • Cano-Belmán J, Ríos-Mercado R, Bautista J (2010) A scatter search based hyper-heuristic for sequencing a mixed-model assembly line. J Heuristics 16:749–770

    Article  MATH  Google Scholar 

  • Chen JC, Chen C-C, Su L-H, Wu H-B, Sun C-J (2012) Assembly line balancing in garment industry. Expert Syst Appl 39:10073–10081

    Article  Google Scholar 

  • Chen R-S, Lu K-Y, Yu S-C (2002) A hybrid genetic algorithm approach on multi-objective of assembly planning problem. Eng Appl Artif Intell 15:447–457

    Article  Google Scholar 

  • Chica M, Cordón Ó, Damas S, Bautista J (2015) Interactive preferences in multiobjective ant colony optimisation for assembly line balancing. Soft Comput 19:2891–2903

    Article  Google Scholar 

  • Delice Y, Kızılkaya Aydoğan E, Özcan U, İlkay M (2014) A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. J Intell Manuf. doi:10.1007/s10845-014-0959-7

    MATH  Google Scholar 

  • Ghosh S, Gagnon RJ (1989) A comprehensive review and analysis of the design, balancing and scheduling of assembly systems. Int J Prod Res 27:637–670

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company Inc, Boston

    MATH  Google Scholar 

  • Helgeson WB, Birnie DP (1961) Assembly line balancing using the ranked positional weight technique. J Ind Eng 12:394–398

    Google Scholar 

  • Johnson RV (1988) Optimally balancing large assembly lines with FABLE. Manag Sci 34:240–253

    Article  Google Scholar 

  • Kao H-H, Yeh D-H, Wang Y-H (2011) Resource constrained assembly line balancing problem solved with ranked positional weight rule. Rev Econ Finance 1:71–80

    Google Scholar 

  • Kim YK, Kim YJ, Kim Y (1996) Genetic algorithms for assembly line balancing with various objectives. Comput Ind Eng 30:397–409

    Article  Google Scholar 

  • Levitin G, Rubinovitz J, Shnits B (1995) Genetic algorithm for assembly line balancing. Int J Prod Econ 41:343–354

    Article  MATH  Google Scholar 

  • Marketline (2014) Footwear: global industry guide. Research and Markets, Dublin

    Google Scholar 

  • Otto A, Otto C (2014) How to design effective priority rules: example of simple assembly line balancing. Comput Ind Eng 69:43–52

    Article  Google Scholar 

  • Özcan U, Toklu B (2009) A tabu search algorithm for two-sided assembly line balancing. Int J Adv Manuf Technol 43:822–829

    Article  MATH  Google Scholar 

  • Rada-Vilela J, Chica M, Cordón Ó, Damas S (2013) A comparative study of multi-objective ant colony optimization algorithms for the time and space assembly line balancing problem. Appl Soft Comput 13:4370–4382

  • Rahimi-Vahed AR, Mirghorbani SM, Rabbani M (2007) A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem. Soft Comput 11:997–1012

    Article  MATH  Google Scholar 

  • Rekiek B, De Lit P, Pellichero F, L’Eglise T, Fouda P, Falkenauer E, Delchambre A (2001) A multiple objective grouping genetic algorithm for assembly line design. J Intell Manuf 12:467–485

    Article  Google Scholar 

  • Roshani A, Fattahi P, Roshani A, Salehi M, Roshani A (2012) Cost-oriented two-sided assembly line balancing problem: a simulated annealing approach. Int J Comput Integr Manuf 25:689–715

    Article  Google Scholar 

  • Roshani A, Roshani A, Roshani A, Salehi M, Esfandyari A (2013) A simulated annealing algorithm for multi-manned assembly line balancing problem. J Manuf Syst 32:238–247

    Article  Google Scholar 

  • Sabuncuoglu I, Erel E, Tanyer M (2000) Assembly line balancing using genetic algorithms. J Intell Manuf 11:295–310

    Article  Google Scholar 

  • Salveson ME (1955) The assembly line balancing problem. J Ind Eng 6:18–25

    MathSciNet  Google Scholar 

  • Scholl A (1999) Balancing and sequencing of assembly lines. Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt

    Book  Google Scholar 

  • Scholl A, Fliedner M, Boysen N (2010) Absalom: Balancing assembly lines with assignment restrictions. Eur J Oper Res 200:688–701

    Article  MATH  Google Scholar 

  • Scholl A, Klein R (1997) SALOME: a bidirectional branch and bound procedure for assembly line balancing. Informs J Comput 9:319–334

    Article  MATH  Google Scholar 

  • Sprecher A (1999) Competitive branch-and-bound algorithm for the simple assembly line balancing problem. Int J Prod Res 37:1787–1816

    Article  MATH  Google Scholar 

  • Tasan SO, Tunali S (2008) A review of the current applications of genetic algorithms in assembly line balancing. J Intell Manuf 19:49–69

    Article  Google Scholar 

  • Triki H, Mellouli A, Masmoudi F (2014) A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2). J Intell Manuf. doi:10.1007/s10845-014-0984-6

    Google Scholar 

  • Zha J, Yu J-J (2014) A hybrid ant colony algorithm for U-line balancing and rebalancing in just-in-time production environment. J Manuf Syst 33:93–102

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by the National Science Council of Taiwan, ROC (Contract No. NSC 102-2221-E-007-123-MY3), and Pou Chen International Group.

Author information

Authors and Affiliations

  1. Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Taipei, Taiwan, ROC

    Nguyen Thi Phuong Quyen & Chao-Lung Yang

  2. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Rd., Hsinchu, Taiwan, ROC

    James C. Chen

Authors
  1. Nguyen Thi Phuong Quyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James C. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao-Lung Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nguyen Thi Phuong Quyen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quyen, N.T.P., Chen, J.C. & Yang, CL. Hybrid genetic algorithm to solve resource constrained assembly line balancing problem in footwear manufacturing. Soft Comput 21, 6279–6295 (2017). https://doi.org/10.1007/s00500-016-2181-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2181-3

Keywords

Search

Navigation