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A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints

Author

Listed:
  • Dashuang Li

    (Huazhong University of Science and Technology (HUST))

  • Chaoyong Zhang

    (Huazhong University of Science and Technology (HUST))

  • Xinyu Shao

    (Huazhong University of Science and Technology (HUST))

  • Wenwen Lin

    (Huazhong University of Science and Technology (HUST))

Abstract

Two-sided assembly line is often designed to produce large-sized high-volume products such as cars, trucks and engineering machinery. However, in real-life production process, besides the elementary constraints in the one-sided assembly line, additional constraints, such as zoning constraints, positional constraints and synchronous constraints, may occur in the two-sided assembly line. In this paper, mathematical formulation of balancing multi-objective two-sided assembly line with multiple constraints is established, and some practical objectives, including maximization of the line efficiency, minimization of the smoothness index and minimization of the total relevant costs per product unit (Tcost), have been considered. A novel multi-objective optimization algorithm based on improved teaching–learning-based optimization (ITLBO) algorithm is proposed to obtain the Pareto-optimal set. In the ITLBO algorithm, teacher and learner phases are modified for the discrete problem, and late acceptance hill-climbing is integrated into a novel self-learning phase. A novel merging method is proposed to construct a new population according to the ordering relation between the original and evolutionary population. The proposed algorithm is tested on the benchmark instances and a practical case. Experimental results, compared with the ones computed by other algorithm and in current literature, validate the effectiveness of the proposed algorithm.

Suggested Citation

  • Dashuang Li & Chaoyong Zhang & Xinyu Shao & Wenwen Lin, 2016. "A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 725-739, August.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:4:d:10.1007_s10845-014-0919-2
    DOI: 10.1007/s10845-014-0919-2
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    References listed on IDEAS

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    1. Scholl, Armin & Becker, Christian, 2005. "A note on "An exact method for cost-oriented assembly line balancing"," International Journal of Production Economics, Elsevier, vol. 97(3), pages 343-352, September.
    2. Amen, Matthias, 2000. "An exact method for cost-oriented assembly line balancing," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 187-195, March.
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    7. Özcan, Ugur, 2010. "Balancing stochastic two-sided assembly lines: A chance-constrained, piecewise-linear, mixed integer program and a simulated annealing algorithm," European Journal of Operational Research, Elsevier, vol. 205(1), pages 81-97, August.
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    Cited by:

    1. Elango Natarajan & Varadaraju Kaviarasan & Wei Hong Lim & Sew Sun Tiang & S. Parasuraman & Sangeetha Elango, 2020. "Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 911-935, April.
    2. Héctor Migallón & Akram Belazi & José-Luis Sánchez-Romero & Héctor Rico & Antonio Jimeno-Morenilla, 2020. "Settings-Free Hybrid Metaheuristic General Optimization Methods," Mathematics, MDPI, vol. 8(7), pages 1-25, July.
    3. Soleimani, Borhan & Keihan Asl, Dariush & Estakhr, Javad & Seifi, Ali Reza, 2022. "Integrated optimization of multi-carrier energy systems: Water-energy nexus case," Energy, Elsevier, vol. 257(C).
    4. Wenzhu Liao & Tong Wang, 2018. "Promoting Green and Sustainability: A Multi-Objective Optimization Method for the Job-Shop Scheduling Problem," Sustainability, MDPI, vol. 10(11), pages 1-19, November.

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