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Workforce grouping and assignment with learning-by-doing and knowledge transfer

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  • Huan Jin
  • Mike Hewitt
  • Barrett W. Thomas

Abstract

We consider a workforce allocation problem in which workers learn both by performing a job and by observing the performance of and interacting with co-located colleagues. As a result, an organisation can benefit from both effectively assigning individuals to jobs and grouping workers into teams. A challenge often faced when solving workforce allocation models that recognise learning is that learning curves are non-linear. To overcome this challenge, we identify properties of an optimal solution to a non-linear programme for grouping workers into teams and assigning the resulting teams to sets of jobs. With these properties identified, we reformulate the non-linear programme to a mixed integer programme that can be solved in much less time. We analyse (near-)optimal solutions to this model to derive managerial insights.

Suggested Citation

  • Huan Jin & Mike Hewitt & Barrett W. Thomas, 2018. "Workforce grouping and assignment with learning-by-doing and knowledge transfer," International Journal of Production Research, Taylor & Francis Journals, vol. 56(14), pages 4968-4982, July.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:14:p:4968-4982
    DOI: 10.1080/00207543.2018.1424366
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    Cited by:

    1. Cavagnini, Rossana & Hewitt, Mike & Maggioni, Francesca, 2020. "Workforce production planning under uncertain learning rates," International Journal of Production Economics, Elsevier, vol. 225(C).
    2. Ausseil, Rosemonde & Ulmer, Marlin W. & Pazour, Jennifer A., 2024. "Online acceptance probability approximation in peer-to-peer transportation," Omega, Elsevier, vol. 123(C).
    3. Henao, César Augusto & Mercado, Yessica Andrea & González, Virginia I. & Lüer-Villagra, Armin, 2023. "Multiskilled personnel assignment with k-chaining considering the learning-forgetting phenomena," International Journal of Production Economics, Elsevier, vol. 265(C).

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