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A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints

Author

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  • Jietao Dong

    (Tsinghua University)

  • Linxuan Zhang

    (Tsinghua University)

  • Tianyuan Xiao

    (Tsinghua University)

Abstract

This paper addresses a stochastic assembly line balancing problem with flexible task times and zoning constraints. In this problem, task times are regarded as interval variables with given lower and upper bounds. Machines can compress processing times of tasks to improve the line efficiency, but it may increase the equipment cost, which is defined via a negative linear function of task times. Thus, it is necessary to make a compromise between the line efficiency and the equipment cost. To solve this problem, a bi-objective chance-constrained mixed 0–1 programming model is developed to simultaneously minimize the cycle time and the equipment cost. Then, a hybrid Particle swarm optimization algorithm is proposed to search a set of Pareto-optimal solutions, which employs the simulated annealing as a local search strategy. The Taguchi method is used to investigate the influence of parameters, and accordingly a suitable parameter setting is suggested. Finally, the comparative results show that the proposed algorithm outperforms the existing algorithms by obtaining better solutions within the same running time.

Suggested Citation

  • Jietao Dong & Linxuan Zhang & Tianyuan Xiao, 2018. "A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 737-751, April.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1126-5
    DOI: 10.1007/s10845-015-1126-5
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    References listed on IDEAS

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    1. Nearchou, Andreas C., 2011. "Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization," International Journal of Production Economics, Elsevier, vol. 129(2), pages 242-250, February.
    2. Becker, Christian & Scholl, Armin, 2006. "A survey on problems and methods in generalized assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 694-715, February.
    3. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2007. "A classification of assembly line balancing problems," European Journal of Operational Research, Elsevier, vol. 183(2), pages 674-693, December.
    4. Scholl, Armin & Becker, Christian, 2006. "State-of-the-art exact and heuristic solution procedures for simple assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 666-693, February.
    5. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    6. Scholl, Armin & Fliedner, Malte & Boysen, Nils, 2010. "Absalom: Balancing assembly lines with assignment restrictions," European Journal of Operational Research, Elsevier, vol. 200(3), pages 688-701, February.
    7. Hamta, Nima & Fatemi Ghomi, S.M.T. & Jolai, F. & Akbarpour Shirazi, M., 2013. "A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect," International Journal of Production Economics, Elsevier, vol. 141(1), pages 99-111.
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    Cited by:

    1. Diefenbach, Johannes & Stolletz, Raik, 2022. "Stochastic assembly line balancing: General bounds and reliability-based branch-and-bound algorithm," European Journal of Operational Research, Elsevier, vol. 302(2), pages 589-605.
    2. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    3. Arnaud Flori & Hamouche Oulhadj & Patrick Siarry, 2022. "QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 525-559, June.
    4. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    5. Fontes, Dalila B.M.M. & Homayouni, S. Mahdi & Gonçalves, José F., 2023. "A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1140-1157.
    6. Lopes, Thiago Cantos & Michels, Adalberto Sato & Sikora, Celso Gustavo Stall & Molina, Rafael Gobbi & Magatão, Leandro, 2018. "Balancing and cyclically sequencing synchronous, asynchronous, and hybrid unpaced assembly lines," International Journal of Production Economics, Elsevier, vol. 203(C), pages 216-224.

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