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Sequencing mixed-model assembly lines operating with a heterogeneous workforce

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  • Pâmela M.C. Cortez
  • Alysson M. Costa

Abstract

We study the problem of sequencing mixed-model assembly lines operating with a heterogeneous workforce. The practical motivation for this study comes from the context of managing assembly lines in sheltered work centres for the disabled. We propose a general framework in which task execution times are both worker and model dependent. Within this framework, the problem is defined and mathematical mixed-integer models and heuristic procedures are proposed. These include a set of fast constructive heuristics, two local search procedures based on approximate measures using either a solution upper bound or the solution of a linear program and a GRASP metaheuristic. Computational tests with instances adapted from commonly used literature databases are used to validate the proposed approaches. These tests give insight on the quality of the different techniques, which prove to be very efficient both in terms of computational effort and solution quality when compared to other strategies such as a random sampling or the solution of the MIP models using a commercial solver.

Suggested Citation

  • Pâmela M.C. Cortez & Alysson M. Costa, 2015. "Sequencing mixed-model assembly lines operating with a heterogeneous workforce," International Journal of Production Research, Taylor & Francis Journals, vol. 53(11), pages 3419-3432, June.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:11:p:3419-3432
    DOI: 10.1080/00207543.2014.987881
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    Citations

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    Cited by:

    1. Mosadegh, H. & Fatemi Ghomi, S.M.T. & Süer, G.A., 2020. "Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 282(2), pages 530-544.
    2. Janis Brammer & Bernhard Lutz & Dirk Neumann, 2022. "Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 29-56, March.
    3. Hashemi-Petroodi, S. Ehsan & Thevenin, Simon & Kovalev, Sergey & Dolgui, Alexandre, 2022. "Model-dependent task assignment in multi-manned mixed-model assembly lines with walking workers," Omega, Elsevier, vol. 113(C).
    4. Hashemi-Petroodi, S. Ehsan & Thevenin, Simon & Kovalev, Sergey & Dolgui, Alexandre, 2023. "Markov decision process for multi-manned mixed-model assembly lines with walking workers," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. Otto, Alena & Li, Xiyu, 2020. "Product sequencing in multiple-piece-flow assembly lines," Omega, Elsevier, vol. 91(C).
    6. Masoud Rabbani & Mahdi Mokhtarzadeh & Neda Manavizadeh & Azadeh Farsi, 2021. "Solving a bi-objective mixed-model assembly-line sequencing using metaheuristic algorithms considering ergonomic factors, customer behavior, and periodic maintenance," OPSEARCH, Springer;Operational Research Society of India, vol. 58(3), pages 513-539, September.
    7. 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).

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