A multi-decision genetic approach for workload balancing of mixed-model U-shaped assembly line systems

Reakook Hwang*, Hiroshi Katayama

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    66 Citations (Scopus)


    A mixed-model assembly line is a type of production line where a variety of product models similar in product characteristics are produced. As a consequence of introducing the just-in-time (JIT) production principle, it has been recognised that a U-shaped assembly line system offers several benefits over the traditional straight line system. This paper proposes a new evolutionary approach to deal with workload balancing problems in mixed-model U-shaped lines. The proposed method is based on the multi-decision of an amelioration structure to improve a variation of the workload. This paper considers both the traditional straight line system and the U-shaped assembly line, and is thus an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload, simultaneously. The results of experiments enhanced the decision process during multi-model assembly line system production; thus, it is therefore suitable for the augmentation of line efficiency in workstation integration and simultaneously enhancement of the variation of the workload. A case study is examined as a validity check in collaboration with a manufacturing company.

    Original languageEnglish
    Pages (from-to)3797-3822
    Number of pages26
    JournalInternational Journal of Production Research
    Issue number14
    Publication statusPublished - 2009 Jan


    • Artificial intelligence
    • Assembly line balancing
    • Assembly lines
    • Evolutionary algorithms
    • Genetic algorithms
    • Global manufacturing
    • Innovation management
    • JIT performance measurement
    • Kaizen
    • Lean manufacturing

    ASJC Scopus subject areas

    • Industrial and Manufacturing Engineering
    • Management Science and Operations Research
    • Strategy and Management


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