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The type-II assembly line rebalancing problem considering stochastic task learning

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  • Yuchen Li

Abstract

Assembly lines with non-constant task time attribute are widely studied in the literature. For the SALBP-II assembly line balancing problem, we take account of stochastic task time changes, which is more practical than the deterministic times often assumed in industrial application. An algorithm – ENCORE, which leverages the traditional algorithm SALOME2, is proposed to address the assembly line balancing problem with stochastic task time attribute. Computational and statistical experiments are conducted to show the efficiency of proposed algorithms over traditional methods with regards to the improvement of total production times.

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  • Yuchen Li, 2017. "The type-II assembly line rebalancing problem considering stochastic task learning," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7334-7355, December.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:24:p:7334-7355
    DOI: 10.1080/00207543.2017.1346316
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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.
    3. Ranasinghe, Thilini & Senanayake, Chanaka D. & Grosse, Eric H., 2024. "Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system," International Journal of Production Economics, Elsevier, vol. 267(C).

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