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A Hybrid Heuristic Algorithm for Multi-manned Assembly Line Balancing Problem with Location Constraints

  • Damian KrenczykEmail author
  • Karol Dziki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)

Abstract

Mass customization production is the next stage in the development of production systems that combines an individual approach to the client needs and benefits of mass production. This approach forces manufacturers to seek new, more effective methods of production flow planning, in particular methods for solving the assembly line balancing problem. The traditional approaches and methods proposed for solving balancing problems require adaptation to new constraints associated with the increasingly widespread introduction of multi-manned and spatially divided assembly workstations. This requires considering additional location restrictions and a more complex allocation of tasks in contrast to restricted only by technological precedencies and time constraints for Simple Assembly Line Balancing Problem. The paper presents a proposal for solving the problem of line balancing with location constraints using new hybrid heuristic algorithm, which is a combination of a modified RPW algorithm and a local search of task sequence on assembly stations zones. Moreover, the concepts of smoothness and efficiency is referred to two separate areas: stations and employees. Experimental results for the literature case of a 30 tasks problem indicate the effectiveness of the proposed approach in practice.

Keywords

Heuristics Assembly line balancing Multi-manned assembly line Space and location constraints 

References

  1. 1.
    Pape, T.: Heuristics and lower bounds for the simple assembly line balancing problem type 1: Overview, computational tests and improvements. Eur. J. Oper. Res. 240(1), 32–42 (2015).  https://doi.org/10.1016/j.ejor.2014.06.023MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Gansterer, M., Hartl, R.F.: One- and two-sided assembly line balancing problems with real-world constraints. Int. J. Prod. Res. 56(8), 3025–3042 (2018).  https://doi.org/10.1080/00207543.2017.1394599CrossRefGoogle Scholar
  3. 3.
    Make, M.R.A., Rashid, M.F.F.A., Razali, M.M.: A review of two-sided assembly line balancing problem. Int. J. Adv. Manuf. Technol. 89, 1743 (2017).  https://doi.org/10.1007/s00170-016-9158-3CrossRefGoogle Scholar
  4. 4.
    Zemczak, M., Skolud, B., Krenczyk, D.: Two-stage orders sequencing system for mixed-model assembly. In: IOP Conference Series: Materials Science and Engineering, vol. 95, p. 012130 (2015).  https://doi.org/10.1088/1757-899x/95/1/012130CrossRefGoogle Scholar
  5. 5.
    Rashid, M.F.F., Hutabarat, W., Tiwari, A.: A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int. J. Adv. Manuf. Technol. 59, 335 (2012).  https://doi.org/10.1007/s00170-011-3499-8CrossRefGoogle Scholar
  6. 6.
    Fathi, M., Ghobakhloo, M.: A technical comment on “a review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches”. Int. J. Adv. Manuf. Technol. 71, 2033–2042 (2014).  https://doi.org/10.1007/s00170-014-5613-1CrossRefGoogle Scholar
  7. 7.
    Tuncel, G., Topaloglu, S.: Assembly line balancing with positional constraints, task assignment restrictions and station paralleling: a case in an electronics company. Comput. Ind. Eng. 64(2), 602–609 (2012).  https://doi.org/10.1016/j.cie.2012.11.006CrossRefGoogle Scholar
  8. 8.
    Cheng, Y., Sun, F., Zhang, Y., Tao, F.: Task allocation in manufacturing: a review. J. Ind. Inf. Integr. (2018, in press).  https://doi.org/10.1016/j.jii.2018.08.001
  9. 9.
    Krenczyk, D., Skolud, B., Herok, A.: A heuristic and simulation hybrid approach for mixed and multi model assembly line balancing. Adv. Intell. Syst. Comput. 637, 99–108 (2018).  https://doi.org/10.1007/978-3-319-64465-3_10CrossRefGoogle Scholar
  10. 10.
    Hamid, Y., Mustafa, Y.: Multi-manned assembly line balancing problem with balanced load density. Assem. Autom. 35(1), 137–142 (2015).  https://doi.org/10.1108/AA-05-2014-041CrossRefGoogle Scholar
  11. 11.
    Dimitriadis, S.G.: Assembly line balancing and group working: a heuristic procedure for workers’ groups operating on the same product and workstation. Comput. Oper. Res. 33(9), 2757–2774 (2006).  https://doi.org/10.1016/j.cor.2005.02.027CrossRefzbMATHGoogle Scholar
  12. 12.
    Roshani, A., Roshani, A., Roshani, A., Salehi, M., Esfandyari, A.: A simulated annealing algorithm for multi-manned assembly line balancing problem. J. Manuf. Syst. 32(1), 238–247 (2013).  https://doi.org/10.1016/j.jmsy.2012.11.003CrossRefGoogle Scholar
  13. 13.
    Prasad, M.M., Ganesan, K., Suresh, R.K.: An optimal balancing of multiple assembly line for a batch production unit. Int. J. Lean Think. 4(2), 22–32 (2013)Google Scholar
  14. 14.
    Krenczyk, D., Dziki, K.: A multi-manned assembly line balancing in spatial restrictions. In: Knosala, R. (ed.) Management Engineering. Digitalization of Production. Research news, PWE Warszawa (2019, in press). (in polish)Google Scholar
  15. 15.
    Grzechca, W.: Estimation of time and cost oriented assembly line balancing problem. In: 19th International Conference on Systems Engineering, Las Vegas, NV, pp. 248–253 (2008).  https://doi.org/10.1109/icseng.2008.48
  16. 16.
    Assembly Line Balancing Data sets & Research topics (2019). https://assembly-line-balancing.de/. Accessed 01 Feb 2019
  17. 17.
    Scholl, A.: Data of assembly line balancing problem. Darmstadt Technical University (1993)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringSilesian University of TechnologyGliwicePoland

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