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An Optimization Model for Assembly Line Balancing Problem with Uncertain Cycle Time

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  • Yong Cao
  • Yuan Li
  • Qinghua Liu
  • Jie Zhang

Abstract

With the drastic change in the market, the assembly line is susceptible to some uncertainties. This study introduces the uncertain cycle time to the assembly line balancing problem (ALBP) and explores its impact. Firstly, we improve the traditional precedence graph to express the precedence, spatial, and incompatible constraints between assembly tasks, which makes ALBP more realistic. Secondly, we establish the assembly line balancing model under an uncertain cycle time, which is defined as an interval whose size can be adjusted according to the level of uncertainty. The objective of the model was to minimize the number of stations and the cycle time. Thirdly, we integrate the operator’s skill level into the model, and a multipopulation genetic algorithm is used to solve it. The method proposed in this study is verified by several test problems of different sizes. The results show that when the cycle time is uncertain, the proposed method can be used to obtain more reasonable results.

Suggested Citation

  • Yong Cao & Yuan Li & Qinghua Liu & Jie Zhang, 2020. "An Optimization Model for Assembly Line Balancing Problem with Uncertain Cycle Time," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, June.
    • Handle: RePEc:hin:jnlmpe:2785278
    DOI: 10.1155/2020/2785278
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    Cited by:

    1. 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).
    2. Eduardo Álvarez-Miranda & Jordi Pereira & Harold Torrez-Meruvia & Mariona Vilà, 2021. "A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations," Mathematics, MDPI, vol. 9(17), pages 1-19, September.

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