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Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

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  • Nearchou, Andreas C.

Abstract

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:proeco:v:129:y:2011:i:2:p:242-250
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    References listed on IDEAS

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    Cited by:

    1. Boonmee, Atiwat & Sethanan, Kanchana, 2016. "A GLNPSO for multi-level capacitated lot-sizing and scheduling problem in the poultry industry," European Journal of Operational Research, Elsevier, vol. 250(2), pages 652-665.
    2. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    3. Jietao Dong & Linxuan Zhang & Tianyuan Xiao, 0. "A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-15.
    4. M. H. Alavidoost & M. H. Fazel Zarandi & Mosahar Tarimoradi & Yaser Nemati, 2017. "Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 313-336, 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. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    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.
    8. repec:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1126-5 is not listed on IDEAS

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