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A Benders’ decomposition algorithm with combinatorial cuts for the multi-manned assembly line balancing problem

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  • Michels, Adalberto Sato
  • Lopes, Thiago Cantos
  • Sikora, Celso Gustavo Stall
  • Magatão, Leandro

Abstract

Multi-manned assembly lines are commonly found in industries that manufacture large-size products (e.g. automotive industry), in which multiple workers are assigned to the same station in order to perform different operations simultaneously on the same product. Although the balancing problem of multi-manned assembly lines had been modelled before, the previously presented exact mathematical formulations are only able to solve few small-size instances, while larger cases are solved by heuristics or metaheuristics that do not guarantee optimality. This work presents a new Mixed-Integer Linear Programming model with strong symmetry break constraints and decomposes the original problem into a new Benders’ Decomposition Algorithm to solve large instances optimally. The proposed model minimises the total number of workers along the line and the number of opened stations as weighted primary and secondary objectives, respectively. Besides, feasibility cuts and symmetry break constraints based on combinatorial Benders’ cuts and model’s parameters are applied as lazy constraints to reduce search-space by eliminating infeasible sets of allocations. Tests on a literature dataset have shown that the proposed mathematical model outperforms previously developed formulations in both solution quality and computational processing time for small-size instances. Moreover, the proposed Benders’ Decomposition Algorithm yielded 117 optimal results out of a 131-instances dataset. Compared to previously presented methods, this translates to 19 and 25 new best solutions reached for medium and large-size instances, respectively, of which 19 and 23 are optimal solutions.

Suggested Citation

  • Michels, Adalberto Sato & Lopes, Thiago Cantos & Sikora, Celso Gustavo Stall & Magatão, Leandro, 2019. "A Benders’ decomposition algorithm with combinatorial cuts for the multi-manned assembly line balancing problem," European Journal of Operational Research, Elsevier, vol. 278(3), pages 796-808.
  • Handle: RePEc:eee:ejores:v:278:y:2019:i:3:p:796-808
    DOI: 10.1016/j.ejor.2019.05.001
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. 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).
    3. Boysen, Nils & Schulze, Philipp & Scholl, Armin, 2022. "Assembly line balancing: What happened in the last fifteen years?," European Journal of Operational Research, Elsevier, vol. 301(3), pages 797-814.
    4. Andreu-Casas, Enric & García-Villoria, Alberto & Pastor, Rafael, 2022. "Multi-manned assembly line balancing problem with dependent task times: a heuristic based on solving a partition problem with constraints," European Journal of Operational Research, Elsevier, vol. 302(1), pages 96-116.
    5. Murat Şahin & Talip Kellegöz, 2023. "Benders’ decomposition based exact solution method for multi-manned assembly line balancing problem with walking workers," Annals of Operations Research, Springer, vol. 321(1), pages 507-540, February.
    6. 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).
    7. Han, Jialin & Zhang, Jiaxiang & Zeng, Bing & Mao, Mingsong, 2021. "Optimizing dynamic facility location-allocation for agricultural machinery maintenance using Benders decomposition," Omega, Elsevier, vol. 105(C).
    8. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    9. Li, Zixiang & Kucukkoc, Ibrahim & Zhang, Zikai, 2020. "Branch, bound and remember algorithm for two-sided assembly line balancing problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 896-905.
    10. Michels, Adalberto Sato & Lopes, Thiago Cantos & Magatão, Leandro, 2020. "An exact method with decomposition techniques and combinatorial Benders’ cuts for the type-2 multi-manned assembly line balancing problem," Operations Research Perspectives, Elsevier, vol. 7(C).
    11. Hassan Zohali & Bahman Naderi & Vahid Roshanaei, 2022. "Solving the Type-2 Assembly Line Balancing with Setups Using Logic-Based Benders Decomposition," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 315-332, January.

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