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Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach

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Abstract

In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems.

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References

  1. Scholl A (1999) Balancing and sequencing of assembly lines, 2nd edn. Physica-Verlag, Heidelberg

    Book  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  3. Suresh G, Sahu S (1994) Stochastic assembly line balancing using simulated annealing. Int J Prod Res 32:1801–1810

    Article  MATH  Google Scholar 

  4. Peterson C (1993) A tabu search procedure for the simple assembly line balancing problem. In: Proceedings of the Decision Science Institute Conference. Washington, DC, 21–23 November 1993. 1502–1504

  5. Falkenauer E, Delchambre A (1992) A genetic algorithm for bin packing and line balancing. In: Proceedings of the 1992 I.E. International Conference on Robotics and Automation. Nice, France, 12–14 May 1992. Los Alamitos, CA: IEEE Computer Society Press, 1186–1192

  6. Ozmehmet S, 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 

  7. Ponnambalam S, Aravindan P, Mogileeswar NG (2000) Multi-objective genetic algorithm for solving assembly line balancing problem. Int J Adv Manuf Technol 16:341–352

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  10. Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley, New York

    Google Scholar 

  11. 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 

  12. 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 

  13. Gen M, Cheng R, Lin L (2008) Network models and optimization: multiobjective genetic algorithm approach. Springer, London

    Google Scholar 

  14. Holland J (1975) Adaptation in natural and artificial system. Univ of Michigan Press, Ann Arbor, MI; MIT Press, Cambridge, MA, 1992

  15. Scholl A, Klein R (2007) Assembly line balancing [online]. Available from: (http://alb.mansci.de/). [Accessed: 20 January 2011]

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Authors and Affiliations

  1. Department of Industrial Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Tarek Al-Hawari, Marwan Ali & Omar Al-Araidah

  2. Industrial Engineering Department, Yarmouk University, P.O. Box 566, Irbid, Jordan

    Ahmad Mumani

Authors
  1. Tarek Al-Hawari
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  2. Marwan Ali
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  3. Omar Al-Araidah
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  4. Ahmad Mumani
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Correspondence to Tarek Al-Hawari.

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Al-Hawari, T., Ali, M., Al-Araidah, O. et al. Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach. Int J Adv Manuf Technol 77, 1419–1432 (2015). https://doi.org/10.1007/s00170-014-6545-5

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  • DOI: https://doi.org/10.1007/s00170-014-6545-5

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