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Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times

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  • Tiacci, Lorenzo

Abstract

The buffer allocation problem (BAP) and the assembly line balancing problem (ALBP) are amongst the most studied problems in the literature on production systems. However they have been so far approached separately, although they are closely interrelated. This paper for the first time considers these two problems simultaneously. An innovative approach, consisting in coupling the most recent advances of simulation techniques with a genetic algorithm approach, is presented to solve a very complex problem: the Mixed Model Assembly Line Balancing Problem (MALBP) with stochastic task times, parallel workstations, and buffers between workstations. An opportune chromosomal representation allows the solutions space to be explored very efficiently, varying simultaneously task assignments and buffer capacities among workstations. A parametric simulator has been used to calculate the objective function of each individual, evaluating at the same time the effect of task assignment and buffer allocation decisions on the line throughput. The results of extensive experimentation demonstrate that using buffers can improve line efficiency. Even when considering a cost per unit buffer space, it is often possible to find solutions that provide higher throughput than for the case without buffers, and at the same time have a lower design cost.

Suggested Citation

  • Tiacci, Lorenzo, 2015. "Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times," International Journal of Production Economics, Elsevier, vol. 162(C), pages 201-215.
    • Handle: RePEc:eee:proeco:v:162:y:2015:i:c:p:201-215
    DOI: 10.1016/j.ijpe.2015.01.022
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    References listed on IDEAS

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    4. Tiacci, Lorenzo, 2015. "Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times," International Journal of Production Economics, Elsevier, vol. 159(C), pages 319-333.
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    Cited by:

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    2. Lopes, Thiago Cantos & Pastre, Giuliano Vidal & Michels, Adalberto Sato & Magatão, Leandro, 2020. "Flexible multi-manned assembly line balancing problem: Model, heuristic procedure, and lower bounds for line length minimization," Omega, Elsevier, vol. 95(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. Ibrahim Kucukkoc & Kadir Buyukozkan & Sule Itir Satoglu & David Z. Zhang, 2019. "A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2913-2925, December.
    5. Peng, Rui & Xiao, Hui & Liu, Hanlin, 2017. "Reliability of multi-state systems with a performance sharing group of limited size," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 164-170.
    6. Romero-Silva, Rodrigo & Shaaban, Sabry & Marsillac, Erika & Laarraf, Zouhair, 2021. "The impact of unequal processing time variability on reliable and unreliable merging line performance," International Journal of Production Economics, Elsevier, vol. 235(C).
    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).
    8. Minghai Yuan & Hongyan Yu & Jinting Huang & Aimin Ji, 2019. "Reconfigurable assembly line balancing for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2391-2405, August.
    9. Khodusov, Valery & Pihnastyi, Oleh, 2019. "The statement of the task of optimal control of the production line using the additional time of equipment operation," MPRA Paper 97076, University Library of Munich, Germany, revised 10 Sep 2019.
    10. Пигнастый, Олег & Koжевников, Георгий, 2019. "Распределенная Динамическая Pde-Модель Программного Управления Загрузкой Технологического Оборудования Производственной Линии [Distributed dynamic PDE-model of a program control by utilization of t," MPRA Paper 93278, University Library of Munich, Germany, revised 02 Feb 2019.
    11. Tiacci, Lorenzo & Mimmi, Mario, 2018. "Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines," Omega, Elsevier, vol. 78(C), pages 112-138.
    12. 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.
    13. Thiago Cantos Lopes & Celso Gustavo Stall Sikora & Adalberto Sato Michels & Leandro Magatão, 2020. "Mixed-model assembly lines balancing with given buffers and product sequence: model, formulation comparisons, and case study," Annals of Operations Research, Springer, vol. 286(1), pages 475-500, March.

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