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Assembly Line Worker Assignment and Balancing Problem with Positional Constraints

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Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems (APMS 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 634))

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Abstract

One of the most important operational issues in the assembly line is to assign tasks and workers to stations while balancing the workload of the workers. We consider the assembly line worker assignment and balancing problem (ALWABP) where the process time of a task depends on worker skill levels. We also consider new positional constraints in the ALWABP to secure the working area of the workers. Two mathematical programming models are proposed to assign workers and tasks when new products are introduced and when a worker is absent or leaves a position temporary, respectively. A heuristic algorithm for the first model is proposed due to its extremely high complexity. The experimental results show the efficiency of the proposed methods. Finally, we explain how the proposed models can be used with a real-time dashboard of a digital twin.

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Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea

    Hyungjoon Yang, Je-Hun Lee & Hyun-Jung Kim

Authors
  1. Hyungjoon Yang
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  2. Je-Hun Lee
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  3. Hyun-Jung Kim
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Corresponding author

Correspondence to Hyun-Jung Kim .

Editor information

Editors and Affiliations

  1. IMT Atlantique, Nantes, France

    Alexandre Dolgui

  2. Centrale Nantes, Nantes, France

    Alain Bernard

  3. IMT Atlantique, Nantes, France

    David Lemoine

  4. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  5. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

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Yang, H., Lee, JH., Kim, HJ. (2021). Assembly Line Worker Assignment and Balancing Problem with Positional Constraints. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-030-85914-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-85914-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85913-8

  • Online ISBN: 978-3-030-85914-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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