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A bi-level optimisation approach for assembly line design using a nested genetic algorithm

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  • Daria Leiber
  • Gunther Reinhart

Abstract

This article presents a novel approach for the automated design of assembly lines that combines the assembly line balancing problem with resource selection and the positioning of the chosen resources into one single optimisation problem. Existing approaches for the automated planning of assembly plants either focus on one planning step or work through different planning steps sequentially. So far, no method exists that sufficiently takes into account the interdependency between the selection and positioning of resources. This article addresses this problem by presenting a bi-level optimisation approach for the automated design of assembly lines. A nested genetic algorithm is used to solve an assembly line balancing problem that includes the selection of production resources while simultaneously considering the layouting options for the chosen resources. Three examples for the evaluation and validation of the algorithm are presented. The presented approach is economically promising as the design of assembly lines requires a lot of expert knowledge and is still mostly done manually.

Suggested Citation

  • Daria Leiber & Gunther Reinhart, 2021. "A bi-level optimisation approach for assembly line design using a nested genetic algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 59(24), pages 7560-7575, December.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:24:p:7560-7575
    DOI: 10.1080/00207543.2020.1845411
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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).

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