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Discovering task assignment rules for assembly line balancing via genetic programming

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Abstract

Assembly line is one of the most commonly used manufacturing processes to produce final products in a flow line. Design of efficient assembly lines has considerable importance for the production of high-quantity standardized products. Several solution approaches such as exact, heuristic, and metaheuristics have been developed since the problem is first formulated. In this study, a new approach based on genetic programming so as to generate composite task assignment rules is proposed for balancing simple assembly lines. The proposed approach can also be applied to other types of line balancing problems. The present method makes use of genetic programming to discover task assignment rules which can be used within a single-pass constructive heuristic in order to balance a given assembly line quickly and effectively. Suitable parameters affecting the balance of the assembly line are evaluated and employed to discover highly efficient composite task assignment rules. Extensive computational results and comparisons proved the efficiency of the proposed approach in producing generic composite task assignment rules for balancing assembly lines.

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References

  1. Boysen N, Fliedner M, Scholl A (2007) A classification of assembly line balancing problems. Eur J Oper Res 183(2):674–693

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  3. Scholl A, Becker C (2006) State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. Eur J Oper Res 168(3):666–693

    Article  MATH  MathSciNet  Google Scholar 

  4. Battaia O, Dolgui A (2013) A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ 142:259–277

    Article  Google Scholar 

  5. Sewell EC, Jacobson SH (2012) A branch, bound and remember algorithm for the simple assembly line balancing problem. INFORMS J Comput 24(3):433–442

    Article  MathSciNet  Google Scholar 

  6. 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 

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

    Google Scholar 

  8. Talbot FB, Patterson JH, Gehrlein WV (1986) A comparative evaluation of heuristic line balancing techniques. Manag Sci 32:430–454

    Article  Google Scholar 

  9. Scholl A, Voß S (1996) Simple assembly line balancing—heuristic approaches. J Heuristics 2:217–244

    Article  Google Scholar 

  10. Boctor FF (1995) A multiple-rule heuristic for assembly line balancing. J Oper Res Soc 46:62–69

    Article  MATH  Google Scholar 

  11. Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  12. Jakobovic D, Marasovic K (2012) Evolving priority scheduling heuristics with genetic programming. Appl Soft Comput 12:2781–2789

    Article  Google Scholar 

  13. Nguyen S, Mengjie Z, Kay MJ, Tan C (2013) Learning iterative dispatching rules for job shop scheduling with genetic programming. Int J Adv Manuf Technol 67:85–100

    Article  Google Scholar 

  14. Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evol Comput 5(1):17–26

    Article  Google Scholar 

  15. Baykasoğlu A, Özbakır L (2007) MEPAR-miner: multi-expression programming for classification rule mining. Eur J Oper Res 183(2):767–784

    Article  MATH  Google Scholar 

  16. Dimopoulos C, Zalzala AMS (2001) Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv Eng Softw 32:489–498

    Article  MATH  Google Scholar 

  17. Tay JC, Ho NB (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput Ind Eng 54:453–473

    Article  Google Scholar 

  18. Nie L, Shao X, Gao L, Li W (2010) Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int J Adv Manuf Technol 50:729–747

    Article  Google Scholar 

  19. Monsieurs P, Flerackers E (2001) Reducing bloat in genetic programming. In: Reusch B (ed) Fuzzy days 2001. Springer, Verlag, pp 471–478

    Google Scholar 

  20. Whingham PA, Dick G (2010) Implicitly controlling bloat in genetic programming. IEEE Trans Evol Comput 14(2):173–190

    Article  Google Scholar 

  21. Hwang RK, Katayama H, Gen M (2008) U-shaped assembly line balancing problem with genetic algorithm. Int J Prod Res 46(16):4637–4649

    Article  MATH  Google Scholar 

  22. Baykasoğlu A (2006) Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. J Intell Manuf 17:217–232

    Article  Google Scholar 

  23. Özcan U, Toklu B (2009) A new hybrid improvement heuristic approach to simple straight and u-type assembly line balancing problems. J Intell Manuf 20(1):123–136

    Article  Google Scholar 

  24. Hwang RK, Katayama H (2010) Uniform workload assignments for assembly line by GA-based amelioration approach. Int J Prod Res 48(7):1857–1871

    Article  MATH  Google Scholar 

  25. 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 

  26. Driscoll J, Thilakawardana D (2001) The definition of assembly line balancing difficulty and evaluation of balance solution quality. Robot Comput Integr Manuf 17:81–86

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Faculty of Engineering, Department of Industrial Engineering, Dokuz Eylül University, Tinaztepe Campus, 35160, Buca, Izmir, Turkey

    Adil Baykasoğlu

  2. Department of Industrial Engineering, Erciyes University, Kayseri, 38039, Turkey

    Lale Özbakır

Authors
  1. Adil Baykasoğlu
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  2. Lale Özbakır
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Correspondence to Adil Baykasoğlu.

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Baykasoğlu, A., Özbakır, L. Discovering task assignment rules for assembly line balancing via genetic programming. Int J Adv Manuf Technol 76, 417–434 (2015). https://doi.org/10.1007/s00170-014-6295-4

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  • DOI: https://doi.org/10.1007/s00170-014-6295-4

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